Methods, apparatus, and systems for wireless surveillance to ensure security

Abstract

The present invention provides methods, apparatus, and systems for wireless surveillance, motion tracking, human recognition, and acoustic sensing. [Solution] The disclosed system comprises a transmitter configured to transmit a first radio signal through a multipath channel of a place, a receiver configured to receive a second radio signal through the radio multipath channel, and a processor. The second radio signal is different from the first radio signal due to the radio multipath channel and the movement of an object in the place. The processor is configured to acquire time-series channel information (TSCI) of the radio multipath channel based on the second radio signal, calculate spatiotemporal information (STI) based on the TSCI, monitor the movement of the object based on the TSCI and STI, perform a task based on the monitoring, and generate a response based on the task.

Description

[Technical Field]

[0001] This disclosure generally relates to wireless surveillance, motion tracking, human recognition, and acoustic sensing. More specifically, this disclosure relates to motion wireless surveillance for enhanced security through wireless channel information (CI) processing, wireless surveillance based on antenna configurations with physical barriers to expand coverage area, wireless monitoring of micro-motion of objects by calculating micro-motion statistics based on wireless CI, tracking of the position and trajectory of moving objects in a place, human recognition based on one or more gait features detected wirelessly by processing wireless CI, and acoustic sensing based on wireless signals by processing wireless CI. [Background technology]

[0002] Today, object motion detection is becoming increasingly important. For example, for security and / or administrative purposes, a user might want to detect the movement of every object in their home; a supermarket manager might want to detect the movement of every object in their supermarket; and a hospital nurse might want to detect the movement of every patient in their hospital.

[0003] Existing systems and methods for detecting object movement often fail to provide sufficient accuracy and frequently result in false alarms. Existing approaches include passive infrared (PIR), active infrared (AIR), and ultrasonic approaches. PIR sensors are the most widely used motion sensors in home security systems, detecting human movement by sensing the difference between the heat emitted by a moving person and the background heat. However, PIR sensor-based solutions are prone to false alarms due to their sensitivity to environmental changes such as heat / cold air currents and sunlight. These solutions are easily neutralized by blocking body heat, such as by wearing an insulated suit. Furthermore, these solutions have a limited detection range, require a line of sight (LOS), and therefore require multiple devices. In AIR-based approaches, an infrared (IR) emitter sends out an IR beam, which is received by an IR receiver. When the light beam is blocked, movement is detected. However, this type of approach can easily be found as using a regular camera or any infrared detection mechanism, and also has a limited range and therefore requires an LOS. Ultrasonic sensors detect human movement by sending ultrasound waves into space and measuring their speed upon return; changes in frequency can detect motion. However, this type of approach can be rendered ineffective by wearing an anechoic suit. In addition, ultrasound cannot penetrate hard objects such as furniture or boxes, thus creating gaps in the detection field. Furthermore, slow movements by a thief may not trigger an alarm in an ultrasound-based detection system.

[0004] Motion measurement is an essential input for a wide range of applications, including robot navigation, indoor tracking, and mobile gaming, and has been widely used in robots, drones, automotive and autonomous vehicles, various home appliances, and virtually anything that moves. The mainstream technology has used inertial measurement units (IMUs) for motion tracking. With the increasing production of smart devices, the demand for accurate and robust motion tracking is growing, driving the IMU market. Improvements in motion measurement have a profound impact on many systems and applications.

[0005] Accurate and robust motion measurement is no easy task. Existing IMUs, such as accelerometers that measure linear acceleration, are well known to suffer from significant errors and drift. For example, accelerometers are often unable to accurately measure distance due to their noisy readings. Therefore, existing methods of calculating distance by integrating acceleration to obtain velocity and then integrating velocity to obtain distance are unreliable due to noise. This hinders many applications that require accurate motion processing, such as indoor tracking, virtual reality, and motion-sensing games.

[0006] Ubiquitous human recognition acts as an essential element for various applications in smart spaces, such as personalized environmental control, security management, automatic doors, and access control for Internet of Things (IoT) devices. Mainstream methods rely on fingerprint recognition, facial recognition, voice recognition, etc., and typically require users to actively cooperate within a certain vicinity. Wireless biometrics, based on the unique way the human body alters multipath channel propagation, has been proposed, but this is highly sensitive to environmental changes and therefore requires extensive training and calibration.

[0007] In recent years, human gait has been proposed as an effective biometric method for more passive person identification, i.e., identification during normal walking without any other active action. To enable ubiquitous and reliable applications, gait recognition systems must be robust to environmental changes, easy to use, and maintain high accuracy without requiring excessive user cooperation and recalibration, which is often unsatisfactory with conventional approaches. Various gait recognition modalities, such as visual, acoustic sensing, wearable sensors, and pressure-sensitive pads, have been considered in the literature. Each of these approaches has some shortcomings with respect to the above criteria. For example, visual-based systems suffer from environmental changes and impose privacy concerns. Methods using inertial sensors require user cooperation and are therefore impractical.

[0008] Acoustic sensing, as the most natural method of human communication, is also becoming a ubiquitous modality for human-machine interaction with the environment. Many applications are emerging in the Internet of Things (IoT), including voice interfaces, acoustic event monitoring in smart homes and buildings, and acoustic sensing for gestures and health. For example, smart speakers can now understand user voices, control IoT devices, monitor the environment, and sense sounds of interest such as glass breaking or smoke detection, all of which currently use microphones as their primary interface.

[0009] Microphones are the most commonly used sensors for detecting acoustic events, but they have certain limitations. A single microphone cannot separate and identify multiple sound sources because it can only detect sound at its destination (i.e., the microphone's location), while a microphone array can only separate sound sources in the azimuthal direction and requires a large aperture. By detecting any sound that arrives at its destination, microphones pose a potential privacy problem when deployed as a ubiquitous and continuous acoustic sensing interface in homes. In addition, because microphones only detect received sound and do not detect anything about the sound source, they are vulnerable to inaudible voice attacks and reply attacks.

[0010] To overcome some of these limitations, various modalities have been used to sense acoustic signals, such as accelerometers, vibration motors, and cameras. These systems still have the same drawbacks as microphones, as they sense the sound of the destination, or they require operation under line-of-sight (LOS) and lighting conditions. As a result, these modalities are not entirely satisfactory.

[0011] Wireless surveillance using wireless channel status information (CSI) has attracted considerable attention in the age of the Internet of Things. Environmental changes and human activities affect multipath propagation embedded in CSI fluctuations, and the environment can be monitored by analyzing wireless CSI. In existing systems, the transmitting and receiving antennas are different devices to provide a reasonable coverage area for wireless surveillance. For example, if the transmitting and receiving antennas are colocated in the same device to reduce device costs, a strong and dominant line of sight (LOS) exists between the transmitting and receiving antennas. When there are environmental changes or human activities, their impact on multipath propagation can be overwhelmed by the strong LOS. In that case, only activity very close to the transmitter (TX) or receiver (RX) may be detected with little chance. Therefore, it is desirable to design special configurations of transmitting and receiving antennas to ensure a reasonable coverage area for wireless surveillance.

[0012] Object motion detection is becoming increasingly important today. For example, for security and / or administrative purposes, a user might want to detect the movement of every object in their home; a supermarket manager might want to detect the movement of every object in their supermarket; and a hospital nurse might want to detect the movement of every patient in their hospital.

[0013] Existing systems and methods for detecting object movement often fail to provide sufficient accuracy and frequently result in false alarms. Conventional approaches include passive infrared (PIR), active infrared (AIR), and ultrasonic approaches. PIR sensors are the most widely used motion sensors in home security systems, detecting human movement by sensing the difference between the background heat emitted by a moving person and the heat emitted by the person. However, PIR sensor-based solutions are prone to false alarms due to their sensitivity to environmental changes such as heat / cold air currents and sunlight. These solutions are easily countered by blocking body heat, such as by wearing thermal full-body clothing. Furthermore, this solution has a limited detection range, requires a line of sight (LOS), and therefore requires multiple devices. In AIR-based approaches, an infrared (IR) emitter sends out an IR beam, which is received by an IR receiver. When the light beam is blocked, movement is detected. However, this type of approach can easily be seen as using a regular camera or any infrared detection mechanism, and also has a limited range and therefore requires an LOS. Ultrasonic sensors detect human movement by sending ultrasonic waves into space and measuring their return speed; changes in frequency can detect movement. However, this type of approach can be overcome by wearing an anechoic suit. In addition, ultrasonic waves cannot penetrate solid objects such as furniture or boxes, thus creating gaps in the detection field. Furthermore, slow movements by a thief may not trigger an alarm in ultrasonic detection systems.

[0014] Therefore, existing systems and methods for wireless surveillance, motion tracking, human recognition, and / or acoustic sensing are not entirely satisfactory. [Overview of the project]

[0015] This disclosure generally relates to wireless surveillance, motion tracking, human recognition, and acoustic sensing. More specifically, this disclosure relates to motion wireless surveillance for improving security through wireless channel information (CI) processing, wireless surveillance based on antenna placement with physical barriers to expand coverage area, wireless monitoring of micro-motion of objects by calculating micro-motion statistics based on wireless CI, tracking of the position and trajectory of moving objects in a place, human recognition based on one or more gait features detected wirelessly by processing wireless CI, and voice sensing based on wireless signals by processing wireless CI.

[0016] In one embodiment, a system for wireless motion monitoring is described. The system comprises a transmitter configured to transmit a first wireless signal over a wireless multipath channel of a location, a receiver configured to receive a second wireless signal over the wireless multipath channel, and a processor. The second wireless signal is different from the first wireless signal for the wireless multipath channel and motion of an object in the location. The processor is configured to acquire time-series channel information (TSCI) of the wireless multipath channel based on the second wireless signal, calculate spatiotemporal information (STI) based on the TSCI, monitor the motion of the object based on the TSCI and STI, perform tasks based on the monitoring, and generate responses based on the tasks.

[0017] In another embodiment, a wireless device of a wireless monitoring system is described. The wireless device includes a processor, memory communicatively coupled to the processor, and a receiver communicatively coupled to the processor. An additional wireless device of the wireless monitoring system is configured to transmit a first wireless signal over a wireless multipath channel of a location according to the current operating mode. The receiver is configured to receive a second wireless signal over the wireless multipath channel according to the current mode. The second wireless signal is different from the first wireless signal due to the wireless multipath channel and motion of an object in the location. The processor is configured to select the current operating mode from a plurality of supported operating modes associated with a task and the motion of the object, to acquire time-series channel information (TSCI) of the wireless multipath channel based on the second wireless signal, to calculate spatiotemporal information (STI) based on the TSCI, to monitor the motion of the object based on the TSCI and STI, to perform a task based on the monitoring, and to generate a response based on the task.

[0018] In yet another embodiment, a method for a wireless surveillance system is described. The method involves transmitting a plurality of radio signals asynchronously over a wireless multipath channel of a place, each radio signal being transmitted from each of a plurality of first devices in the wireless surveillance system; receiving a plurality of radio signals over the wireless multipath channel by a plurality of second devices in the wireless surveillance system, each received radio signal being different from each transmitted radio signal, depending on the wireless multipath channel and motion of objects in the place; obtaining each of a plurality of time-series channel information (TSCI) of the wireless multipath channel based on each received radio signal, each This includes: calculating multiple spatiotemporal information (STIs) based on each TSCI; monitoring the movement of the object in a first state based on a first subset of the multiple TSCIs and a first subset of the multiple STIs; monitoring a second state of the movement of the object based on a second subset of the multiple TSCIs and a second subset of the multiple STIs; performing a first task based on monitoring the first state of movement; performing a second task based on monitoring the second state of movement; generating a first response based on the first task; and generating a second response based on the second task.

[0019] In one embodiment, a system for motion tracking is described. The system comprises at least one sensor configured to generate at least one sensing information (SI), a memory, a processor communicatively coupled to the memory and at least one sensor, and a set of instructions stored in the memory. When the set of instructions is executed by the processor, it causes the processor to obtain at least one time-series SI (TSSI) from at least one sensor, analyze at least one TSSI, track the movement of an object in a place based on at least one TSSI, calculate an incremental distance related to the movement of the object in a first incremental period based on at least one TSSI, calculate a direction related to the movement of the object in a second incremental period based on at least one TSSI, and calculate the next position of the object in the next time based on at least one of the object's current position in the current time, the object's current orientation in the current time, the incremental distance, the direction, or a map of the place.

[0020] In another embodiment, a motion tracking system device is described. The device comprises a processor, a memory communicably coupled to the processor, and a sensor communicably coupled to the processor. The sensor is configured to generate at least one sensing information (SI). The processor is configured to obtain at least one time-series SI (TSSI) from the sensor, analyze at least one TSSI, track the movement of an object in a place based on at least one TSSI, calculate an incremental distance related to the object's movement in a first incremental period based on at least one TSSI, calculate a direction related to the object's movement in a second incremental period based on at least one TSSI, and calculate the object's next position in the next time based on at least one of the object's current position in the current time, the object's current orientation in the current time, the incremental distance, the direction, or a map of the place.

[0021] In yet another embodiment, a method for a motion tracking system is described. The method includes generating at least one sensing information (SI), obtaining at least one time-series SI (TSSI), analyzing at least one TSSI, tracking the movement of an object in a location based on at least one TSSI, calculating an incremental distance related to the movement of the object in a first incremental time period based on at least one TSSI, calculating a direction related to the movement of the object in a second incremental period based on the at least one TSSI, and calculating the next position of the object in the next time based on at least one of the current position of the object in the current time, the current orientation of the object in the current time, the incremental distance, the direction, or a map of the location.

[0022] In one embodiment, a system for human cognition is described. The system comprises a transmitter configured to transmit a first radio signal through a radio channel of a place, a receiver configured to receive a second radio signal through the radio channel, the second radio signal including a reflection of the first radio signal by at least one object in the place, and a processor. The processor is configured to acquire time-series channel information (CI) of the radio channel based on the second radio signal, determine the presence of a person moving within the place based on the time-series CI (TSCI), extract at least one gait feature of the person from the TSCI, and recognize the identity of the person based on the at least one gait feature.

[0023] In another embodiment, a wireless device for a human recognition system is described. The wireless device includes a processor, a memory communicatively coupled to the processor, and a receiver communicatively coupled to the processor. An additional wireless device for the human recognition system is configured to transmit a first wireless signal over a wireless channel of a place. The receiver is configured to receive a second wireless signal over the wireless channel. The second wireless signal includes a reflection of the first wireless signal by at least one object in the place. The processor is configured to obtain time-series channel information (CI) of the wireless channel based on the second wireless signal, determine the presence of a person moving within the place based on the time series of the CI (TSCI), extract at least one gait feature of the person from the TSCI, and recognize the person's identity based on the at least one gait feature.

[0024] In yet another embodiment, a method for a human recognition system is described. The method includes the steps of transmitting a first radio signal over a radio channel of a place, and receiving a second radio signal over the radio channel, wherein the second radio signal includes a reflection of the first radio signal by at least one object in the place; obtaining time-series channel information (CI) of the radio channel based on the second radio signal; determining the presence of a person moving within the place based on the time-series of the CI (TSCI); and extracting at least one gait feature of the person from the TSCI and recognizing the person's identity based on the at least one gait feature.

[0025] In one embodiment, a system for acoustic sensing is described. The system comprises a transmitter configured to transmit a first radio signal through a radio channel of a place, a receiver configured to receive a second radio signal through the radio channel, the second radio signal including a reflection of the first radio signal by at least one object in the place, and a processor. The processor is configured to acquire time-series channel information (CI) of the radio channel based on the second radio signal, determine the presence of a vibrating object in the place based on the time-series CI (TSCI), extract a sound signal from the TSCI, and reconstruct at least one sound based on the acoustic signal.

[0026] In another embodiment, a wireless device for an acoustic sensing system is described. The wireless device includes a processor, a memory communicatively coupled to the processor, and a receiver communicatively coupled to the processor. An additional wireless device for the acoustic sensing system is configured to transmit a first wireless signal over a wireless channel of a place. The receiver is configured to receive a second wireless signal over the wireless channel. The second wireless signal includes a reflection of the first wireless signal by at least one object in the place. The processor is configured to obtain time-series channel information (CI) of the wireless channel based on the second wireless signal, determine the presence of vibrating objects in the place based on the time-series CI (TSCI), extract a sound signal from the TSCI, and reconstruct at least one sound based on the acoustic signal.

[0027] In yet another embodiment, a method for an acoustic sensing system is described. The method includes transmitting a first radio signal over a radio channel of a place, and receiving a second radio signal over the radio channel, the second radio signal including a reflection of the first radio signal by at least one object in the place; obtaining time-series channel information (CI) of the radio channel based on the second radio signal; determining the presence of a vibrating object in the place based on the time series of the CI (TSCI); extracting an acoustic signal from the TSCI; and reconstructing at least one sound based on the acoustic signal.

[0028] In one embodiment, a system for wireless motion monitoring is described. The system comprises a transmitter configured to transmit a first wireless signal over a wireless multipath channel of a location, a receiver configured to receive a second wireless signal over the wireless multipath channel, and a processor. The second wireless signal is different from the first wireless signal for the wireless multipath channel and motion of an object in the location. The processor is configured to acquire time-series channel information (TSCI) of the wireless multipath channel based on the second wireless signal, where each channel information (CI) of the TSCI contains N1 CI components, where N1 is an integer greater than 1; to select N2 CI components from the N1 CI components based on a first analysis of the N1 CI components of the TSCI, where N2 is an integer less than or equal to N1; to calculate micro-motion (MM) statistics based on the N2 selected CI components of the TSCI and the first analysis; to monitor the motion of the object based on a second analysis of the MM statistics; to perform tasks based on the first and second analyses; and to generate responses based on the tasks, the first and second analyses.

[0029] In another embodiment, a wireless device of a wireless surveillance system is described. The wireless device includes a processor, memory communicatively coupled to the processor, and a receiver communicatively coupled to the processor. An additional wireless device of the wireless surveillance system is configured to transmit a first wireless signal over a wireless multipath channel of a location. The receiver is configured to receive a second wireless signal over the wireless multipath channel. The second wireless signal is different from the first wireless signal due to the wireless multipath channel and motion of objects in the location. The processor is configured to acquire time-series channel information (TSCI) of a radio multipath channel based on a second radio signal, wherein each channel information (CI) of the TSCI includes N1 CI components, where N1 is an integer greater than 1; to select N2 CI components from the N1 CI components of the TSCI based on a first analysis of the N1 CI components, where N2 is an integer less than or equal to N1; to calculate minute motion (MM) statistics based on the N2 selected CI components of the TSCI and the first analysis; to monitor the motion of the object based on a second analysis of the MM statistics; to perform a task based on the first and second analyses; and to generate a response based on the task, the first and second analyses.

[0030] In yet another embodiment, a method for a wireless surveillance system is described. The method involves transmitting a first radio signal from a first device in the wireless surveillance system over a wireless multipath channel of a place, and receiving a second radio signal from a second device in the wireless surveillance system over the wireless multipath channel, the second radio signal being different from the first radio signal due to the wireless multipath channel and motion of objects in the place, receiving, and obtaining time-series channel information (TSCI) of the wireless multipath channel based on the second radio signal, where each channel information (CI) of the TSCI contains N1 CI components, N1 being an integer greater than 1, and obtaining N2 from the N1 CI components based on a first analysis of the N1 CI components of the TSCI. The method includes selecting CI components such that N2 is an integer less than or equal to N1, calculating micro-motion (MM) statistics based on the N2 selected CI components of the TSCI and a first analysis, monitoring the movement of the object based on a second analysis of the MM statistics, performing a task based on the first and second analyses, and generating a response based on the task, the first and second analyses.

[0031] Other concepts relate to software for implementing this disclosure with respect to wireless monitoring, motion tracking, human recognition, and acoustic sensing. Additional novel features are described in part in the following description and may also be partially revealed to those skilled in the art by examining the following drawings and accompanying drawings, or by learning through the manufacture or operation of the embodiments. Novel features of this disclosure may be realized and achieved by implementing or using various aspects of the methods, means, and combinations described in the detailed embodiments described below. [Brief explanation of the drawing]

[0032] The methods, systems, and / or devices described herein will be further described in relation to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, and similar reference numerals represent similar structures throughout multiple figures of the drawings.

[0033] [Figure 1] This disclosure shows the overall architecture of a motion tracking system according to several embodiments.

[0034] [Figure 2] This figure shows the actions performed by a motion tracking system using an IMU, according to some embodiments of the present disclosure.

[0035] [Figure 3] This figure shows a workflow for a sensor engine in a motion tracking system according to some embodiments of the present disclosure.

[0036] [Figure 4] This figure shows exemplary atomic events extracted by a sensor engine in a motion tracking system, according to some embodiments of the present disclosure.

[0037] [Figure 5] The following are exemplary temporal constraints on extracted atomic events according to some embodiments of this disclosure.

[0038] [Figure 6] Examples of finite state machines (FSMs) for stair detection according to some embodiments of the present disclosure.

[0039] [Figure 7] Examples of steps of sensing information based on step detection according to some embodiments of this disclosure.

[0040] [Figure 8]A flowchart of exemplary methods for motion tracking according to several embodiments of this disclosure is shown.

[0041] [Figure 9] This disclosure provides an overview of fine-time measurement (FTM) protocols according to several embodiments.

[0042] [Figure 10] According to some embodiments of this disclosure, exemplary distance measurements are shown when an Initiating Station (ISTA) begins approaching a Responding Station (RSTA) from a different direction and initial distance.

[0043] [Figure 11] A flowchart illustrating exemplary methods for fine-time measurement (FTM)-based driver arrival sensing, according to several embodiments of this disclosure, is shown.

[0044] [Figure 12] The key steps of knee point detection for driver reach sensing are shown according to several embodiments of this disclosure.

[0045] [Figure 13] A table showing the accuracy of arrival time estimation according to several embodiments of this disclosure is provided.

[0046] [Figure 14] The diagrams illustrate exemplary human recognition systems based on gait cube data using millimeter-wave (mm-wave) radio, according to some embodiments of the present disclosure.

[0047] [Figure 15] The results of an exemplary human recognition system in several embodiments of this disclosure are shown.

[0048] [Figure 16]The flowcharts of exemplary methods for human recognition based on gait cube features using millimeter-wave signals, according to some embodiments of the present disclosure, are shown.

[0049] [Figure 17] This disclosure provides exemplary implementation environments for sound sensing systems using millimeter-wave radio, according to several embodiments of this disclosure.

[0050] [Figure 18A] ~ [Figure 18D] This disclosure illustrates various use cases for acoustic sensing systems according to several embodiments of this disclosure.

[0051] [Figure 19] The diagrams illustrate the operations performed by a millimeter-wave radio signal-based acoustic sensing system according to some embodiments of this disclosure.

[0052] [Figure 20] The diagrams show the structure of neural network models according to several embodiments of this disclosure.

[0053] [Figure 21] This disclosure illustrates process flows for data augmentation, training, and evaluation of neural network models according to several embodiments of this disclosure.

[0054] [Figure 22] A flowchart illustrating exemplary methods for acoustic sensing using millimeter-wave radio signals, according to some embodiments of the present disclosure, is shown.

[0055] [Figure 23] The following are exemplary network topologies of four devices according to several embodiments of this disclosure.

[0056] [Figure 24]The following are exemplary network topologies of four devices according to several embodiments of this disclosure.

[0057] [Figure 25] The following describes an exemplary network topology of three devices according to several embodiments of the present disclosure.

[0058] [Figure 26] The present disclosure provides illustrative flowcharts and components of a master-origin device according to several embodiments of this disclosure.

[0059] [Figure 27] This disclosure shows an exemplary network topology of six devices according to several embodiments of this disclosure.

[0060] [Figure 28] This disclosure illustrates an exemplary network topology of nine devices in a local area network according to several embodiments of this disclosure.

[0061] [Figure 29] The flowcharts illustrate exemplary methods for analyzing and improving network topology according to some embodiments of this disclosure.

[0062] [Figure 30] A flowchart illustrating exemplary methods for wireless motion monitoring according to several embodiments of this disclosure is shown.

[0063] [Figure 31] This figure shows a method for wireless surveillance based on a physical barrier placed between a transmitter and a receiver, according to some embodiments of the present disclosure.

[0064] [Figure 32] This disclosure describes several embodiments of a wireless monitoring method based on at least one directional antenna.

[0065] [Figure 33] The following are exemplary devices inserted into a wall socket for fall detection, according to some embodiments of the present disclosure.

[0066] [Figure 34] This figure shows a method for wireless surveillance based on a physical barrier placed between a transmitter and a receiver, according to some embodiments of the present disclosure.

[0067] [Figure 35] Examples of transmitter and receiver hardware configurations in a wireless surveillance system are shown according to some embodiments of this disclosure.

[0068] [Figure 36] Examples of various situations for transmitters and receivers in a wireless monitoring system are shown according to some embodiments of this disclosure.

[0069] [Figure 37] This disclosure presents exemplary scenarios in which object movement is monitored, according to several embodiments of this disclosure.

[0070] [Figure 38] This disclosure illustrates exemplary floor plans and wireless device installations for wireless monitoring according to several embodiments of this disclosure.

[0071] [Figure 39] An exemplary block diagram of a first wireless device of a wireless monitoring system according to one embodiment of the present disclosure is shown.

[0072] [Figure 40] An exemplary block diagram of a second wireless device of a wireless monitoring system according to one embodiment of the present disclosure is shown.

[0073] [Figure 41] A flowchart illustrating exemplary methods for wireless microtremor monitoring according to several embodiments of this disclosure is shown. [Modes for carrying out the invention]

[0074] In the following detailed description, numerous specific details are given as examples to provide a complete understanding of the relevant disclosures. However, it should be apparent to those skilled in the art that the disclosures can be implemented without such details. In other examples, well-known methods, procedures, components, and / or circuits are described at a relatively high level without detail, so as not to unnecessarily obscure aspects of the disclosures.

[0075] In one embodiment, this disclosure discloses a method, apparatus, device, system, and / or software (method / apparatus / device / system / software) for a wireless monitoring system. Time-series channel information (CI) of a wireless multipath channel can be obtained (e.g., dynamically) using a processor, memory communicably coupled to the processor, and a set of instructions stored in memory. The time-series CI (TSCI) can be extracted from radio signals (signals) transmitted over the channel between a type 1 heterogeneous radio device (e.g., a radio transmitter, TX) and a type 2 heterogeneous radio device (e.g., a radio receiver, RX) at a venue. The channel may be influenced by representations of objects at the venue (e.g., motion, movement, representation, and / or changes in position / pose / shape / representation). The properties of objects and / or the motion of objects and / or spatial-temporal information (STI, e.g., motion information) may be monitored based on the TSCI. Tasks may be performed based on the properties and / or STI. Presentations associated with tasks may be generated in a user interface (UI) on the user's device. The TSCI may be a radio signal stream. The TSCI or each CI may be preprocessed. The apparatus may be a station (STA). The symbol "A / B" means "A and / or B" in this disclosure.

[0076] An expression can include arrangement, arrangement of movable parts, place, position, orientation, identifiable place, area, spatial coordinates, presentation, state, representation, static representation, size, length, width, height, angle, scale, shape, curve, surface, area, volume, pose, posture, sign, bodily representation, dynamic representation, dynamic noun, movement, motion sequence, gesture, stretch, contraction, distortion, deformation, bodily representation (e.g., head, face, eyes, mouth, tongue, hair, voice, neck, limbs, arms, hands, legs, feet, muscles, movable parts), surface representation (e.g., shape, texture, material, color, electromagnetic (EM) properties, visual pattern, humidity, reflectivity, translucency, flexibility), material properties (e.g., living tissue, hair, textile, metal, wood, leather, plastic, metal, artificial material, solid, liquid, gas, temperature), movement, activity, behavior, change in expression, and / or any combination thereof.

[0077] Radio signals include transmit / receive signals, EM emissions, RF signals / transmit, licensed / unlicensed / ISM band signals, band-limited signals, baseband signals, radio / mobile / cellular communication signals, radio / mobile / cellular network signals, mesh signals, optical signals / communications, downlink / uplink signals, unicast / multicast / broadcast signals, standard (e.g., WLAN, WWAN, WPAN, WBAN, international, domestic, industry, de facto, IEEE, IEEE 802, 802.11 / 15 / 16, WiFi, 802.11n / ac / ax / be, 3G / 4G / LTE / 5G / 6G / 7G / 8G, 3GPP, Bluetooth, BLE, Zigbee, RFID, UWB, WiMax) compliant signals, protocol signals, standard frames, beacon / pilot / search / query / acknowledgment / handshake / synchronization signals, management / control / data signals, standardized radio / cellular communication protocols, reference signals, source signals, motion probe / detection / It may include sensing signals and / or a set of signals. The radio signals may include line-of-sight (LOS) and / or non-LOS components (or paths / links). Each CI may be extracted / generated / calculated / detected at a layer of a Type 2 device (e.g., the PHY / MAC layer of the OSI model) and obtained by an application (e.g., software, firmware, drivers, apps, radio monitoring software / systems).

[0078] A wireless multipath channel may include: communication channels, analog frequency channels (e.g., analog carrier frequencies around 700 / 800 / 900MHz, 1.8 / 1.8 / 2.4 / 3 / 5 / 6 / 27 / 60GHz), coding channels (e.g., CDMA), and / or wireless network / system channels (e.g., WLAN, WiFi, mesh, LTE, 4G / 5G, Bluetooth, Zigbee, UWB, RFID, microwave). It may include two or more channels. The channels may be contiguous (e.g., adjacent / overlapping bands) or non-contiguous (e.g., non-overlapping WiFi channels, one at 2.4GHz and one at 5GHz).

[0079] TSCI can be extracted from radio signals at the layers of a Type 2 device (e.g., the layers of the OSI reference model: physical layer, data link layer, logical link control layer, media access control (MAC) layer, network layer, transport layer, session layer, presentation layer, application layer, TCP / IP layer, internet layer, link layer). TSCI may also be extracted from derived signals (e.g., baseband signals, motion detection signals, motion sensing signals) derived from radio signals (e.g., RF signals). It may be a (radio) measurement detected by a communication protocol (e.g., a standardized protocol) using existing mechanisms (e.g., radio / cellular communication standards / networks, 3G / LTE / 4G / 5G / 6G / 7G / 8G, WiFi, IEEE 802.11 / 15 / 16). The derived signal may include a packet having at least one of a preamble, header, and payload (e.g., for data / control / management in a radio link / network). TSCI can be extracted from probe signals in the packet (e.g., training sequence, STF, LTF, L-STF, L-LTF, L-SIG, HE-STF, HE-LTF, HE-SIG-A, HE-SIG-B, CEF). Motion detection / sensing signals may be recognized / identified based on the probe signals. Packets may be standards-compliant protocol frames, management frames, control frames, data frames, sounding frames, excitement frames, illumination frames, null data frames, beacon frames, pilot frames, probe frames, request frames, response frames, association frames, reassociation frames, disconnection frames, authentication frames, action frames, report frames, pole frames, announcement frames, extension frames, query frames, acknowledgment frames, RTS frames, CTS frames, QoS frames, CF-Poll frames, CF-Ack frames, block acknowledgment frames, reference frames, training frames, and / or synchronization frames.

[0080] Packets may contain control data and / or motion detection probes. Data (e.g., ID / parameters / characteristics / settings / control signals / commands / instructions / notifications / broadcast-related information for Type 1 devices) can be retrieved from the payload. Radio signals may be transmitted by Type 1 devices and received by Type 2 devices. Databases (e.g., in local servers, hub devices, cloud servers, and storage networks) may be used to store TSCI, characteristics, STI, signatures, patterns, behavior, trends, parameters, analysis, output responses, identification information, user information, device information, channel information, location information (e.g., maps, environmental models, networks, proximity devices / networks), task information, class / category information, presentation information (e.g., UI) information, and / or other information.

[0081] Type 1 / Type 2 devices may include at least one of the following: electronics, circuits, transmitters (TX) / receivers (RX) / transceivers, RF interfaces, "origin satellites" / "tracker bots", unicast / multicast / broadcast devices, wireless power devices, power / destination devices, wireless nodes, hub devices, target devices, motion detection devices, sensor devices, remote / wireless sensor devices, wireless communication devices, wireless-enabled devices, standards-compliant devices, and / or receivers. If multiple examples of Type 1 (or Type 2) devices exist, they may be heterogeneous, having different circuits, enclosures, structures, purposes, auxiliary functions, chips / ICs, processors, memory, software, firmware, network connectivity, antennas, brands, models, appearance, form, shape, color, materials, and / or specifications. Type 1 / Type 2 devices may include access points, routers, mesh routers, Internet of Things (IoT) devices, wireless terminals, one or more wireless / RF subsystems / wireless interfaces (e.g., 2.4GHz wireless, 5GHz wireless, fronthaul wireless, backhaul wireless), modems, RF front ends, RF / wireless chips, or integrated circuits (ICs).

[0082] At least one of the following can be associated with an identification (ID), such as a UUID: a Type 1 device, a Type 2 device, the links between them, objects, characteristics, STI, motion monitoring, and tasks. A Type 1 / Type 2 / another device can acquire / store / retrieve / access / preprocess / condition / process / analyze / monitor / apply TSCI. Type 1 and Type 2 devices can communicate network traffic on other channels (e.g., Ethernet, HDMI®, USB, Bluetooth, BLE, WiFi, LTE, other networks, wireless multipath channels) in parallel with radio signals. A Type 2 device can passively observe / monitor / receive radio signals from a Type 1 device on a wireless multipath channel without establishing a connection (e.g., association / authentication) with a Type 1 device or requesting a service from a Type 1 device.

[0083] A transmitter (i.e., a Type 1 device) can function (perform its role) as a receiver (i.e., a Type 2 device) temporarily, sporadically, continuously, repeatedly, interchangeably, alternately, simultaneously, in parallel, and / or concurrently, and vice versa. A device can function as both a Type 1 device (transmitter) and / or a Type 2 device (receiver) temporarily, sporadically, continuously, repeatedly, simultaneously, in parallel, and / or concurrently. There may be multiple radio nodes, each being a Type 1 (TX) and / or Type 2 (RX) device. TSCI may be acquired for every two nodes when exchanging / communicating radio signals. Object properties and / or STI may be monitored individually based on TSCI or jointly based on two or more (e.g., all) TSCIs.

[0084] The movement of an object can be monitored actively (in that its Type 1 device, Type 2 device, or both, is wearable / associated with the object) and / or passively (in that neither the Type 1 device nor the Type 2 device is wearable / associated with the object). It can be passive because the object may not be associated with a Type 1 device and / or a Type 2 device. The object (e.g., a user, an automated guided vehicle, or an AGV) may not need to carry / install any wearables / fixtures (i.e., Type 1 and Type 2 devices are not wearable / mounted devices that the object needs to carry in order to perform a task). This can be active because the object may be associated with either a Type 1 device or a Type 2 device. The object may carry (or install) wearables / fixtures (e.g., a Type 1 device, a Type 2 device, or a device communicatively coupled to either a Type 1 or Type 2 device).

[0085] Presentations include visuals, audio, images, video, animation, graphic presentations, and text. Task calculations can be performed by a processor (or logic unit) of a Type 1 device, a processor (or logic unit) of an IC of a Type 1 device, a processor (or logic unit) of a Type 2 device, a processor of an IC of a Type 2 device, a local server, a cloud server, a data analysis subsystem, a signal analysis subsystem, and / or another processor. This task can be performed with or without radio finger prints or baselines (e.g., collection, processing, handling, transmission, and / or training phases / previous survey / latest survey / initial radio survey, passive markings), training, profiles, trained profiles, static profiles, static profiles, surveys, initial radio surveys, initial setup, installation, retraining, updates, and resets).

[0086] A Type 1 device (TX device) may comprise at least one heterogeneous radio transmitter. A Type 2 device (RX device) may comprise at least one heterogeneous radio receiver. Type 1 and Type 2 devices may be colocations. A Type 1 device and a Type 2 device may be the same device. Any device may have a data processing unit / device, a computing unit / system, a network unit / system, a processor (e.g., a logic unit), memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor. Some processors, memory, and sets of instructions may coordinate.

[0087] There may be multiple Type 1 devices interacting with the same Type 2 device (or multiple Type 2 devices) (e.g., communicating, exchanging signals / controls / notifications / other data), and / or multiple Type 2 devices interacting with the same Type 1 device. Multiple Type 1 / Type 2 devices may be synchronous and / or asynchronous, have the same / different window widths / sizes and / or time shifts, have the same / different sync start times and sync end times, etc. Radio signals transmitted by multiple Type 1 devices may be sporadic, transient, continuous, repetitive, synchronous, simultaneous, and / or simultaneous. Multiple Type 1 / Type 2 devices may operate independently and / or collaboratively. Type 1 and / or Type 2 devices may have / include heterogeneous hardware circuits (e.g., heterogeneous chips or ICs that can generate / receive radio signals, extract CI from received signals, or make CI available). They may be communicatively coupled to the same or different servers (e.g., cloud servers, edge servers, local servers, hub devices).

[0088] The operation of one device may be based on its operation, state, internal state, memory, processor, memory output, physical location, computing resources, and network of other devices. Different devices may communicate directly and / or via other devices / servers / hub devices / cloud servers. A device may be associated with one or more users and have associated settings. Settings may be selected once, pre-programmed, and / or changed over time (e.g., adjusted, altered, modified). A method may have further steps. The steps and / or additional steps of a method may be performed in the order shown or in a different order. Any step may be performed in parallel, repeatedly, or otherwise iteratively or otherwise. Users may be humans, adults, elderly adults, males, females, infants, children, babies, pets, animals, living organisms, machines, computer modules / software, etc.

[0089] For one or more Type 1 devices interacting with one or more Type 2 devices, any processing (e.g., time domain, frequency domain) may differ for different devices. Processing may be based on location, orientation, direction, role, user-related characteristics, settings, configuration, available resources, available bandwidth, network connectivity, hardware, software, processor, coprocessor, memory, battery life, available power, antenna, antenna type, antenna directivity / unidirectional characteristics, power settings, and / or other parameters / characteristics of the device.

[0090] A wireless receiver (e.g., a Type 2 device) may receive signals and / or other signals from a wireless transmitter (e.g., a Type 1 device). A wireless receiver may receive another signal from another wireless transmitter (e.g., a second Type 1 device). A wireless transmitter may transmit signals and / or other signals to another wireless receiver (e.g., a second Type 2 device). Wireless transmitters, wireless receivers, other wireless receivers and / or other wireless transmitters may move with objects and / or other objects. They may track other objects.

[0091] A Type 1 and / or Type 2 device may be capable of radio coupling with at least two Type 2 and / or Type 1 devices. A Type 1 device may be triggered / controlled to switch / establish a radio coupling (e.g., association, authentication) from a Type 2 device to a second Type 2 device at a different location in the same place. Similarly, a Type 2 device may be triggered / controlled to switch / establish a radio coupling from a Type 1 device to a second Type 1 device at yet another location in the same place. Switching can be controlled by a server (or hub device), a processor, a Type 1 device, a Type 2 device, and / or another device. Different radios may be used before and after switching. A second radio signal (second signal) may be transmitted through a channel between a Type 1 device and a second Type 2 device (or between a Type 2 device and a second Type 1 device). A second TSCI of the channel may be obtained from the second signal. The second signal may be the first signal. Object properties, STI and / or other quantities may be monitored based on the second TSCI. Type 1 and Type 2 devices may be the same. Characteristics, STI, and / or other quantities with different timestamps may form a waveform. The waveform can be displayed in the presentation.

[0092] Radio signals and / or other signals may have data embedded in them. A radio signal may be a series of probe signals (e.g., repeated transmission of probe signals, reuse of one or more probe signals). Probe signals may change / change over time. Probe signals may be standards-compliant signals, protocol signals, standardized radio protocol signals, control signals, data signals, radio communication network signals, cellular network signals, WiFi signals, LTE / 5G / 6G / 7G signals, reference signals, beacon signals, motion detection signals, and / or motion sensing signals. Probe signals may be formatted according to a radio network standard (e.g., WiFi), a cellular network standard (e.g., LTE / 5G / 6G), or another standard. Probe signals may include packets with a header and a payload. Probe signals may have data embedded in them. The payload may include data. Probe signals may be replaced by data signals. Probe signals may be embedded in data signals. A radio receiver, a radio transmitter, another radio receiver and / or another radio transmitter may be associated with at least one processor, memory communicably coupled to each processor, and / or each instruction set stored in memory, which, when executed, causes the processor to perform any and / or all steps necessary to determine the object's STI (e.g., motion information), initial STI, initial time, direction, instantaneous position, instantaneous angle, and / or velocity.

[0093] A processor, memory, and / or instruction set may be associated with a Type 1 device, at least one Type 2 device, an object, a device associated with an object, another device associated with a location, a cloud server, a hub device, and / or another server.

[0094] A Type 1 device can broadcast signals to at least one or more Type 2 devices through a location channel. The signals are transmitted without a Type 1 device establishing a radio connection (e.g., association, authentication) with any Type 2 device, and without a Type 2 device requesting service from the Type 1 device. A Type 1 device can transmit to a specific Media Access Control (MAC) address common to multiple Type 2 devices. Each Type 2 device can adjust its MAC address to a specific MAC address. A specific MAC address can be associated with a location. The association can be recorded in a mapping table of an association server (e.g., a hub device). A location can be identified by a Type 1 device, a Type 2 device, and / or another device based on a specific MAC address, a series of probe signals, and / or at least one TSCI extracted from the probe signals.

[0095] For example, a Type 2 device may be moved to a new location (e.g., from another location). A Type 1 device may be newly configured in a location so that Type 1 and Type 2 devices do not recognize each other. During setup, a Type 1 device may be instructed / guided / triggered / controlled to send a series of probe signals to a specific MAC address (e.g., using a dummy receiver, using hardware pin configuration / connection, using saved configuration, using local configuration, using remote configuration, using downloaded configuration, using a hub device, or using a server). Upon power-up, a Type 2 device may scan for probe signals according to a table of MAC addresses (e.g., stored in a specified source, server, hub device, or cloud server) that can be used to broadcast in different locations (e.g., different MAC addresses used in different locations such as houses, offices, enclosures, floors, multi-story buildings, shops, airports, malls, stadiums, halls, stations, subways, lots, regions, areas, districts, localities, cities, countries, continents, etc.). When a Type 2 device detects a probe signal sent to a specific MAC address, the Type 2 device can use the table to identify the location based on the MAC address.

[0096] The location of a Type 2 device can be calculated based on a specific MAC address, a series of probe signals, and / or at least one TSCI obtained by the Type 2 device from the probe signals. The calculation may be performed by the Type 2 device.

[0097] A specific MAC address may change over time (e.g., adjust, modify, revise). It may change according to a schedule, rules, policies, modes, conditions, circumstances, and / or changes. A specific MAC address may be selected based on MAC address availability, a pre-selected list, collision patterns, traffic patterns, data traffic between Type 1 devices and other devices, available bandwidth, random selection, and / or a MAC address switching plan. A specific MAC address may be the MAC address of a second radio device (e.g., a dummy receiver, or a receiver that functions as a dummy receiver).

[0098] A Type 1 device may transmit a probe signal on a channel selected from a set of channels. At least one CI on the selected channel may be acquired by each Type 2 device from the probe signal transmitted on the selected channel.

[0099] The selected channels may change over time (e.g., adjust, modify, revise). Changes may follow schedules, rules, policies, modes, conditions, circumstances, and / or changes. The selected channels may be chosen based on channel availability, random selection, a pre-selected list, same-channel interference, inter-channel interference, channel traffic patterns, data traffic between Type 1 devices and other devices, effective bandwidth associated with the channel, security criteria, channel switching plans, standards, quality criteria, signal quality conditions, and / or considerations.

[0100] Information about a specific MAC address and / or selected channel can be communicated between a Type 1 device and a server (e.g., a hub device) over a network. Information about a specific MAC address and / or selected channel can also be communicated between a Type 2 device and a server (e.g., a hub device) over another network. A Type 2 device can communicate information about a specific MAC address and / or selected channel to another Type 2 device (e.g., via a mesh network, Bluetooth, WiFi, NFC, ZigBee, etc.). The specific MAC address and / or selected channel may be selected by a server (e.g., a hub device). The specific MAC address and / or selected channel may be signaled within an announcement channel by a Type 1 device, a Type 2 device, and / or a server (e.g., a hub device). Any information may be pre-processed before being communicated.

[0101] A radio connection (e.g., association, authentication) between a Type 1 device and another radio device can be established (e.g., using a signal handshake). The Type 1 device may send a first handshake signal (e.g., a sounding frame, probe signal, request to transmit RTS) to the other device. The other device may respond by sending a second handshake signal (e.g., a command, or transmitable CTS) to the Type 1 device, triggering the Type 1 device to broadcast a signal (e.g., a series of probe signals) to multiple Type 2 devices without establishing a connection with any Type 2 device. The second handshake signal may be a response or acknowledgment (e.g., ACK) to the first handshake signal. The second handshake signal may include data having location and / or information about the Type 1 device. The other device may be a dummy device with the purpose (e.g., primary purpose, secondary purpose) of establishing a radio connection with the Type 1 device, receiving the first signal, and / or sending the second signal. The other device may be physically attached to the Type 1 device.

[0102] In another example, another device may send a third handshake signal to a Type 1 device that triggers the Type 1 device to broadcast a signal (e.g., a series of probe signals) to multiple Type 2 devices without establishing a connection (e.g., association, authentication) with any Type 2 device. The Type 1 device may respond to the third special signal by sending a fourth handshake signal to another device. Another device may be used to trigger multiple Type 1 devices to broadcast. The trigger may be continuous, partially continuous, partially parallel, or fully parallel. Another device may have multiple radio circuits to trigger multiple transmitters in parallel. Parallel triggering may also be achieved by using at least one yet another device to perform a trigger in parallel with another device (similar to what the other device does). After establishing a connection with a Type 1 device, another device may not be able to communicate with (or suspend communication with) the Type 1 device. Suspended communication may be resumed. After establishing a connection with a Type 1 device, another device may enter inactive mode, dormant mode, sleep mode, standby mode, low power mode, OFF mode, and / or power-down mode. Another device may have a specific MAC address so that the Type 1 device transmits signals to a specific MAC address. The Type 1 device and / or the other device may be controlled and / or coordinated by a first processor associated with the Type 1 device, a second processor associated with the other device, a third processor associated with a specified source, and / or a fourth processor associated with the other device. The first and second processors can coordinate with each other.

[0103] A first set of probe signals may be transmitted to at least one first type 2 device by a first antenna of a type 1 device through a first channel at a first location. A second set of probe signals may be transmitted to at least one second type 2 device by a second antenna of a type 1 device through a second channel at a second location. The first set of probe signals and the second set of probe signals may be different or different. The at least one first type 2 device may be different or different from the at least one second type 2 device. The first and / or second sets of probe signals may be broadcast without an established connection (e.g., association, authentication) between the type 1 device and any type 2 device. The first and second antennas may be the same or different.

[0104] The two locations may have different sizes, shapes, and multipath characteristics. The first and second locations may overlap. The immediate vicinity areas of the first and second antennas may overlap. The first and second channels may be the same or different. For example, the first may be WiFi and the second may be LTE. Or, both may be WiFi, but the first may be 2.4GHz WiFi and the second may be 5GHz WiFi. Or, both may be 2.4GHz WiFi, but have different channel numbers, SSID names, and / or WiFi settings.

[0105] Each Type 2 device can acquire at least one TSCI from its respective set of probe signals, where CI is the respective channel between the Type 2 device and the Type 1 device. Some first Type 2 devices (one or more) and some second Type 2 devices (one or more) may be the same. The first and second sets of probe signals may be synchronous / asynchronous. The probe signals may be transmitted with data or replaced with data signals. The first and second antennas may be the same.

[0106] A first series of probe signals may be transmitted at a first speed (e.g., 30 Hz). A second series of probe signals may be transmitted at a second speed (e.g., 200 Hz). The first and second speeds may be the same or different. The first and / or second speeds may be changed (e.g., adjusted, modified) over time. Changes may be in accordance with schedules, rules, policies, modes, conditions, circumstances, and / or changes. Any speed may be changed (e.g., adjusted, modified) over time.

[0107] A first and / or second set of probe signals may be transmitted to the first MAC address and / or the second MAC address, respectively. The two MAC addresses may be the same or different. The first set of probe signals may be transmitted within the first channel. The second set of probe signals may be transmitted on the second channel. The two channels may be the same or different. The first or second MAC address and the first or second channel may change over time. Any changes may be in accordance with schedules, rules, policies, modes, states, circumstances, and / or changes.

[0108] Type 1 devices and other devices may be controlled and / or coordinated, physically mounted to a common device, or be part of / within the common device. They may be controlled by / connected to a common data processor, or connected to a common bus interconnect / network / LAN / Bluetooth network / NFC network / BLE / wired network / wireless network / mesh network / mobile network / cloud. They may share common memory, or be associated with common users, user devices, profiles, accounts, identities (IDs), identifiers, homes, residences, physical addresses, locations, geographic coordinates, IP subnets, SSIDs, home devices, office devices, and / or manufacturing devices.

[0109] Each Type 1 device can be a signal source for its respective set of Type 2 devices (i.e., it sends each signal (e.g., each set of probe signals) to its respective set of Type 2 devices). Each Type 2 device selects a Type 1 device from all Type 1 devices as its signal source. Each Type 2 device can be selected asynchronously. At least one TSCI may be acquired by each Type 2 device from each set of probe signals from a Type 1 device, and the CI is a channel between Type 2 and Type 1 devices.

[0110] Each Type 2 device selects a Type 1 device as its signal source from among all Type 1 devices based on its identity (ID) or Type 1 / Type 2 device identifier, the task to be performed, past signal sources, history (e.g., past signal sources, Type 1 device, another Type 1 device, each Type 2 receiver, and / or another Type 2 receiver), thresholds for switching signal sources, and / or user information, account, access information, parameters, characteristics, and / or signal strength (e.g., associated with Type 1 devices and / or each Type 2 receiver).

[0111] Initially, a Type 1 device can be the signal source for each initial set of Type 2 devices (i.e., each Type 1 device sends its respective signal (a series of probe signals) to each initial set of Type 2 devices). Each initial Type 2 device selects a Type 1 device from among all the Type 1 devices as its signal source.

[0112] A signal source (Type 1 device) of a particular Type 2 device may be modified (e.g., adjusted, altered, or corrected) if: (1) the time interval between two adjacent probe signals received from the current signal source of the Type 2 device (e.g., between the current probe signal and the immediately preceding probe signal, or between the next probe signal and the current probe signal) exceeds a first threshold; (2) the signal intensity associated with the current signal source of the Type 2 device is less than a second threshold; (3) the processed signal intensity associated with the current signal source of the Type 2 device is less than a third threshold, and the signal intensity is processed with a low-pass filter, band-pass filter, median filter, moving average filter, weighted average filter, linear filter, and / or nonlinear filter; and / or (4) the signal intensity (or processed signal intensity) associated with the current signal source of the Type 2 device is below a fourth threshold for a significant percentage (e.g., 70%, 80%, 90%) of the recent time window, the percentage may exceed a fifth threshold. The first, second, third, fourth, and / or fifth thresholds may change over time.

[0113] Condition (1) can occur when the Type 1 and Type 2 devices gradually move further apart from each other, resulting in some probe signals from the Type 1 device becoming too weak to be received by the Type 2 device. Conditions (2) to (4) can occur when the two devices move so far apart that the signal strength becomes very weak.

[0114] The signal source of a Type 2 device may remain unchanged if other Type 1 devices have a weaker signal strength than the current signal source factor (e.g., 1, 1.1, 1.2, or 1.5).

[0115] If the signal source is changed (adjusted, modified, corrected, etc.), the new signal source may become active in the near future (e.g., the next time each change occurs). The new signal source may be a Type 1 device having the strongest signal strength and / or processed signal strength. The current signal source and the new signal source may be the same or different.

[0116] A list of available Type 1 devices may be initialized and maintained by each Type 2 device. The list may be updated by examining the signal strength and / or processed signal strength associated with each set of Type 1 devices. A Type 2 device may be selected from a first set of probe signals from a first Type 1 device and a second set of probe signals from a second Type 1 device based on the respective probe signal rates, MAC addresses, channels, features / characteristics / states, tasks to be performed by the Type 2 device, the signal strengths of the first and second sets of signals, and / or other considerations.

[0117] A series of probe signals may be transmitted at a constant rate (e.g., 100 Hz). A series of probe signals may be scheduled at regular intervals (e.g., 0.01 s for 100 Hz), although each probe signal may experience small time perturbations due to timing requirements, timing control, network control, handshake, message passing, collision avoidance, carrier sensing, congestion, resource availability, and / or other considerations.

[0118] The rate may be changed (e.g., adjusted, modified, and corrected). Changes may follow a schedule (e.g., changed hourly), rules, policies, modes, conditions, and / or changes (e.g., changed whenever a certain event occurs). For example, the rate is typically 100Hz but may be changed to 1000Hz in demanding situations and to 1Hz in low-power / standby situations. Probe signals may be transmitted in bursts.

[0119] The probe signal rate may vary based on the task performed by the Type 1 or Type 2 device (e.g., a task may typically require 100Hz, but temporarily require 1000Hz for 20 seconds). In one example, the transmitter (Type 1 device), receiver (Type 2 device), and associated task may be adaptively (and / or dynamically) associated with a class (e.g., low priority, high priority, urgent, critical, normal, privileged, unsubscribed, subscribed, paid, and / or uncharged). The rate (of the transmitter) may be adjusted for several classes (e.g., high priority classes). If the needs of that class change, the rate can be changed (e.g., adjusted, modified, corrected). If the receiver has critically low power, the rate may be reduced to reduce the receiver's power consumption in response to the probe signal. In one example, the probe signal may be used to wirelessly transfer power to the receiver (Type 2 device), and the rate may be adjusted to control the amount of power transferred to the receiver.

[0120] The rate can be changed by (or based on) the following: a server (e.g., a hub device), a Type 1 device, and / or a Type 2 device. Control signals may be communicated between them. The server can monitor, track, predict, and / or forecast the needs of the Type 2 device and / or the tasks performed by the Type 2 device, and control the Type 1 device to change the rate. The server can make scheduled changes to the rate according to a schedule. The server can detect emergencies and change the rate immediately. The server can detect developments and adjust the rate gradually.

[0121] Characteristics and / or STIs (e.g., motion information) may be monitored individually based on TSCIs associated with specific Type 1 devices and specific Type 2 devices, and / or jointly based on any TSCIs associated with specific Type 1 devices and any Type 2 devices, and / or jointly based on any TSCIs associated with specific Type 2 devices and any Type 1 devices, and / or globally based on any TSCIs associated with any Type 1 devices and any Type 2 devices. Any joint monitoring may relate to: users, user accounts, profiles, homes, location maps, location environment models, and / or user history, etc.

[0122] The first channel between a Type 1 device and a Type 2 device may differ from the second channel between another Type 1 device and another Type 2 device. The two channels may relate to different frequency bands, bandwidths, carrier frequencies, modulation, radio standards, coding, encryption, payload characteristics, networks, network IDs, SSIDs, network characteristics, network configurations, and / or network parameters.

[0123] The two channels can relate to two different types of wireless systems (for example, two of the following: WiFi, LTE, LTE-A, LTE-U, 2.5G, 3G, 3.5G, 4G, Beyond 4G, 5G, 6G, 7G, cellular network standards, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA®, TD-SCDMA, 802.11 systems, 802.15 systems, 802.16 systems, mesh networks, Zigbee, NFC, WiMAX, Bluetooth, BLE, RFID, UWB, microwave systems, radar-like systems). For example, one could be WiFi and the other LTE.

[0124] The two channels may be associated with similar types of wireless systems, but with different networks. For example, the first channel might be associated with a WiFi network named "Pizza and Pizza" in the 2.4GHz band with a 20MHz bandwidth, while the second channel might be associated with a WiFi network with the SSID "StarBud Hotspot" in the 5GHz band with a 40MHz bandwidth. The two channels may also be different channels within the same network (such as the "StarBud Hotspot" network).

[0125] In one embodiment, the wireless monitoring system may include training a classifier of multiple events at a location based on a training TSCI associated with multiple events. The CI or TSCI associated with an event may consider / include wireless samples / characteristics / fingerprints associated with that event (and / or location, environment, object, object movement, state / emotional state / mental state / state / stage / gesture / gait / action / movement / activity / daily activity / history / object event, etc.).

[0126] For each of several known events occurring at a location during each training time associated with a known event (e.g., survey, wirelee survey, initial wirelee survey), each training radio signal (e.g., each set of training probe signals) may be transmitted to at least one first type 2 heterogeneous radio device through the radio multipath channel of the location during each training time by the antenna of a first type 1 heterogeneous radio device using the processor, memory, and instruction set of a first type 1 device.

[0127] At least one time series of each training CI (training TSCI) can be acquired asynchronously from the training signal by each of at least one first type 2 device. The CI may be a channel between the first type 2 device and the first type 1 device during the training time associated with a known event. At least one training TSCI may be preprocessed. Training may be a radio survey (e.g., between the installation of the type 1 device and / or type 2 device).

[0128] For current events occurring within a location during the current period, current radio signals (e.g., a series of current probe signals) may be transmitted by the antenna of a second type 1 heterogeneous radio device using a processor, memory, and instruction set of a second type 1 device to at least one second type 2 heterogeneous radio device through a channel of the location during the current period related to the current event.

[0129] At least one time series of the current CI (current TSCI) can be acquired asynchronously from the current signal (e.g., a series of current probe signals) by each of at least one second type 2 device. The CI may be the CI of the channel between the second type 2 device and the second type 1 device during the current period related to the current event. At least one current TSCI can be preprocessed.

[0130] A classifier may be applied by at least one second type 2 device to classify at least one current TSCI obtained from a series of current probe signals, to classify at least one portion of a particular current TSCI, and / or to classify a combination of at least one portion of a particular current TSCI and another portion of another TSCI. The classifier can divide the TSCI (or features / STI or other analysis or output response) into clusters and associate the clusters with specific events / objects / subjects / locations / movements / activities. Labels / tags may be generated for the clusters. The clusters can be stored and retrieved. The classifier may be applied to relate the current TSCI (or possibly a characteristic / STI or other analysis / output response related to the current event) to: clusters, known / specific events, classes / categories / groups / groupings / lists / clusters, sets / targets / locations / movements / activity of known events, unknown events, classes / categories / groups / groupings / lists / clusters / sets / targets / locations / movements / activity of unknown events, and / or other events / targets / locations / movements / activity / classes / categories / groups / groupings / lists / clusters. Each TSCI may contain at least one CI, each associated with its respective timestamp. Two TSCIs related to two type 2 devices will differ by different start times, durations, stop times, amounts of CIs, sampling frequencies, and sampling periods. Their CIs may have different characteristics. The first and second type 1 devices may be at the same location in a place. They may be the same device. At least one second type 2 device (or their location) may replace at least one first type 2 device (or their location). A specific second type 2 device and a specific first type 2 device may be the same device.

[0131] A subset of the first type 2 device and a subset of the second type 2 device may be the same. At least one second type 2 device and / or at least one subset of the second type 2 device may be a subset of at least one first type 2 device. At least one first type 2 device and / or at least one subset of the first type 2 device may be a replacement for at least one subset of the second type 2 device. At least one second type 2 device and / or at least one subset of the second type 2 device may be a replacement for at least one subset of the first type 2 device. At least one second type 2 device and / or at least one subset of the second type 2 device may be in the same respective positions as at least one subset of the first type 2 device. At least one first type 2 device and / or at least one subset of the first type 2 device may be in the same respective positions as at least one subset of the second type 2 device.

[0132] The antennas of a Type 1 device and a second Type 1 device may be in the same location. The antennas of at least one second Type 2 device and / or at least one subset of second Type 2 devices may be in the same respective locations as the respective antennas of at least one subset of first Type 2 devices. The antennas of at least one first Type 2 device and / or at least one subset of first Type 2 devices may be in the same respective locations as the respective antennas of at least one subset of second Type 2 devices.

[0133] The first section of the first duration of the first TSCI and the second section of the second duration of the second section of the second TSCI may be aligned. A map between the items in the first section and the items in the second section can be calculated. The first section may include a first segment (e.g., a subset) of the first TSCI having a first start / end time, and / or another segment (e.g., a subset) of the processed first TSCI. The processed first TSCI may be the first TSCI processed by the first operation. The second section may include a second segment (e.g., a subset) of the second TSCI having a second start time and a second end time, and another segment (e.g., a subset) of the processed second TSCI. The processed second TSCI may be the second TSCI processed by the second operation. The first and / or second operations may include subsampling, resampling, interpolation, filtering, transformation, feature extraction, preprocessing, and / or other operations.

[0134] The first item in the first section may be mapped to the second item in the second section. The first item in the first section may also be mapped to another item in the second section. Another item in the first section may also be mapped to the second item in the second section. Mappings can be one-to-one, one-to-many, many-to-one, or many-to-many. At least one function of at least one of the following can satisfy at least one constraint: the first item in the first section of the first TSCI, another item in the first TSCI, the timestamp of the first item, the time difference of the first item, the time difference of the first item, the adjacent timestamp of the first item, the adjacent timestamp of the first item, another timestamp related to the first item, the second item in the second section of the second TSCI, another item in the second TSCI, the timestamp of the second item, the time difference of the second item, the time difference of the second item, the adjacent timestamp of the second item, and another timestamp related to the second item.

[0135] One constraint may be that the difference between the timestamp of the first item and the timestamp of the second item may be limited by an adaptive (and / or dynamically adjusted) upper threshold and limited by an adaptive lower threshold.

[0136] The first section may be the entire first TSCI. The second section may be the entire second TSCI. The first time duration may be equal to the second time duration. The sections of a TSCI's time duration can be determined adaptively (and / or dynamically). A provisional section of a TSCI can be calculated. The start and end times of a section (e.g., provisional section, section) can be determined. A section can be determined by removing the beginning and end parts of a provisional section. The beginning part of a provisional section can be determined as follows. Iteratively, items in a provisional section with increasing timestamps can be considered as the current item, which is one item at a time.

[0137] In each iteration, at least one activity metric / indicator may be calculated and / or considered. At least one activity metric may be associated with at least one of the following: the current item associated with the current timestamp, past items in the provisional section with a timestamp not greater than the current timestamp, and / or future items in the provisional section with a timestamp not less than the current timestamp. If at least one criterion (e.g., quality criterion, signal quality condition) associated with at least one activity metric is met, the current item may be added to the beginning of the provisional section.

[0138] At least one criterion related to the activity scale may include at least one of the following: (a) the activity scale is less than an adaptive (dynamically adjusted) upper threshold; (b) the activity scale is greater than an adaptive lower threshold; (c) the activity scale is consecutively less than an adaptive upper threshold for at least a given amount of consecutive timestamps; (d) the activity scale is consecutively greater than an adaptive lower threshold for at least another given amount of consecutive timestamps; (e) the activity scale is consecutively less than an adaptive upper threshold for at least a given percentage of a given amount of consecutive timestamps; (f) the activity scale is consecutively greater than an adaptive lower threshold for at least another given percentage of another given amount of consecutive timestamps; (g) another activity scale related to another timestamp related to the current timestamp is less than another adaptive upper threshold and greater than another adaptive lower threshold; (h) at least one activity scale related to each of the at least one timestamp related to the current timestamp is less than its respective upper threshold and greater than its respective lower threshold. (i) The percentage of timestamps associated with an activity metric that is less than each upper threshold and greater than each lower threshold in a set of timestamps associated with the current timestamp exceeds the threshold, and (j) another criterion (e.g., quality criterion, signal quality condition).

[0139] An activity measure / indicator related to an item at time T1 may include at least one of the following: (1) a first function of the item at time T1 and the item at time T1-D1, where D1 is a predetermined positive quantity (e.g., a constant time offset); (2) a second function of the item at time T1 and the item at time T1+D1; (3) a third function of the item at time T1 and the item at time T2, where T2 is a predetermined quantity (e.g., a fixed initial reference time, T2 may change over time (e.g., adjust, change, modify), T2 may be periodically updated, T2 may be the start of a period and T1 may be a sliding time in the period); and (4) a fourth function of the item at time T1 and other items.

[0140] At least one of the first, second, third, and / or fourth functions may be a function (e.g., F(X, Y, ...)) with at least two arguments X and Y, the two arguments may be scalars. The function (e.g., F) may be at least one of the following functions: X, Y, (XY), (YX), abs(XY), X^a, Y^b, abs(X^aY^b), (XY)^a, (X / Y), (X+a) / (Y+b), (X^a / Y^b), and ((X / Y)^ab), where a and b may be some predetermined values. For example, this function may simply be abs(XY), or (XY)^2, (XY)^4. This function may be a robust function. For example, the function is (XY)^2 when abs(XY) is less than the threshold T, and (XY)+a when abs(XY) is greater than T. Alternatively, when abs(XY) is greater than T, the function may be a constant. Also, when abs(XY) is greater than T, the function may be constrained by a slowly increasing function, and as a result, outliers cannot have a significant impact on the result. Another example of this function may be (abs(X / Y)-a) (where a=1). In this way, when X=Y (i.e., no change or activity), the function gives a value of 0. When X is greater than Y, (X / Y) is greater than 1 (assuming X and Y are positive), and the function is positive. When X is less than Y, (X / Y) is less than 1, and the function is negative. In another example, both arguments X and Y may be n-tuples such that X=(x_1, x_2, ..., x_n) and Y=(y_1, y_2, ..., y_n). This function may be at least one of the following functions: x_i, y_i, (x_i-y_i), (y_ix_i), abs(x_i-y_i), x_i^a, y_i^b, abs(x_i^a-y_i^b), (x_i-y_i)^a, (x_i / y_i), (x_i+a) / (y_i +b), (x_i^a / y_i^b), and ((x_i / y_i)^ab), where i is the component index of the n-tuples X and Y, and 1<=i<=n.For example, the component index of x_1 is i=1 and the component index of x_2 is i=2. The function can contain the sum of another function for at least one component from x_i, y_i, (x_i-y_i), (y_ix_i), abs(x_i-y_i), x_i^a, y_i^b, abs(x_i^a-y_i^b), (x_i-y_i)^a, (x_i / y_i), (x_i+a) / (y_i +b), (x_i^a / y_i^b), and ((x_i / y_i)^ab), where i is the component index of the n tuples X and Y. For example, this function could be of the form sum_{i=1}^n(abs(x_i / y_i)-1) / n, or sum_{i=1}^nw_i*(abs(x_i / y_i)-1), where w_i is the weight of component i.

[0141] The map can be computed using dynamic time warping (DTW). The DTW may include constraints on the map, items in the first TSCI, items in the second TSCI, the first duration, the second duration, the first section, and / or the second section. Suppose in the map that i^{th} domain items are mapped to j^{th} range items. The constraints may be on the acceptable combinations of i and j (constraints on the relationship between i and j). The mismatch cost between the first section of the first duration of the first TSCI and the second section of the second duration of the second TSCI can be computed.

[0142] The first and second sections can be aligned such that a map containing multiple links can be established between a first item in the first TSCI and a second item in the second TSCI. Each link can associate one of the first items with a first timestamp with one of the second items with a second timestamp. A mismatch cost can be calculated between the aligned first section and the aligned second section. The mismatch cost can be a function of the cost of the items between the first and second items associated by a particular link in the map and the (link-wise) cost of the links associated with a particular link in the map.

[0143] The aligned first section and the aligned second section may be represented as the first and second vectors, respectively, having the same vector length. The mismatch cost may include at least one of the following between the first and second vectors: the inner product, inner product quantifier, correlation-based quantity, correlation indicator, covariance-based quantity, discrimination score, distance, Euclidean distance, absolute distance, Lk distance (e.g., L1, L2, ...), weighted distance, distance quantifier, and / or other similarity values. The mismatch cost may be normalized by the respective vector lengths.

[0144] The parameters derived from the mismatch cost between the first section of the first duration of the first TSCI and the second section of the second duration of the second TSCI can be modeled by a statistical distribution. At least one of the scale parameter, location parameter, and / or other parameters of the statistical distribution can be estimated.

[0145] The first section of the first duration of the first TSCI may be a sliding section of the first TSCI. The second section of the second duration of the second TSCI may be a sliding section of the second TSCI.

[0146] A first sliding window may be applied to a first TSCI, and a corresponding second sliding window may be applied to a second TSCI. The first sliding window of the first TSCI and the corresponding second sliding window of the second TSCI may be aligned.

[0147] The mismatch cost can be calculated between the aligned first sliding window of the first TSCI and the corresponding aligned second sliding window of the second TSCI. Based on the mismatch cost, the current event may be associated with at least one of the known event, the unknown event, and / or another event.

[0148] The classifier may be applied to at least one of the first sections of the first duration of the first TSCI and / or the second sections of the second duration of the second TSCI in order to obtain at least one provisional classification result. Each provisional classification result may be associated with its respective first section and its respective second section.

[0149] The current event can be associated with at least one of the following based on mismatch cost: a known event, an unknown event, a set of classes / categories / groups / groupings / lists / unknown events, and / or another event. The current event can be associated with at least one of the known events, unknown events, and / or another event based on the most numerous provisional classification results in multiple sections of the first TSCI and the corresponding multiple sections of the second TSCI: For example, if the mismatch cost points to a particular known event in N consecutive (e.g., N=10) sequences, the current event can be associated with that particular known event. In another example, the current event can be associated with a particular known event if the percentage of mismatch costs in the N preceding consecutive sequences pointing to that particular known event exceeds a given threshold (e.g., >80%).

[0150] In another example, the current event may be associated with a known event that achieves the minimum mismatch cost for the most times in time. The current event may be associated with a known event that achieves the minimum overall mismatch cost, which is a weighted average of at least one mismatch cost associated with at least one first section. The current event may be associated with a specific known event that achieves a minimum of another overall cost. The current event may be associated with an “unknown event” if none of the known events achieve a mismatch cost lower than a first threshold T1 for a sufficient percentage of at least one first section. The current event may also be associated with an “unknown event” if none of the events achieve an overall mismatch cost lower than a second threshold T2. The current event may be associated with at least one of the known events, unknown events, and / or other events based on the mismatch costs and additional mismatch costs associated with at least one additional section of the first TSCI and at least one additional section of the second TSCI. Known events may include at least one of the following: door closing events, door opening events, window closing events, window opening events, multi-state events, on-state events, off-state events, intermediate-state events, continuous-state events, discrete-state events, human presence events, human absence events, life presence events, and / or life non-presence events.

[0151] The projection for each CI can be trained using a dimensionality reduction method based on the trained TSCI. The dimensionality reduction method may include at least one of the following: principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher linear decision formula, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or other methods. The projection can be applied to at least one of the trained TSCIs and / or the current TSCI associated with at least one event for the classifier.

[0152] A classifier for at least one event may be trained based on a projection associated with at least one event and its associated training TSCI. At least one current TSCI may be classified / categorized based on the projection and the current TSCI. The projection may be retrained using a dimensionality reduction method and at least one of other dimensionality reduction methods, based on at least one of the training TSCI, at least one current TSCI before retraining the projection, and / or additional training TSCIs. Other dimensionality reduction methods may include at least one of principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher linear decision, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or yet another method. A classifier for at least one event may be retrained based on the retrained projection, at least one training TSCI associated with at least one event, and / or at least one current TSCI. At least one current TSCI can be classified based on a retrained projection, a retrained classifier, and / or the current TSCI.

[0153] Each CI may contain a vector of complex numbers. Each complex number may be preprocessed to give a magnitude for the complex number. Each CI may be preprocessed to give a vector of non-negative real numbers containing the magnitudes for the corresponding complex numbers. Each training TSCI may be weighted in the training of the projection. The projection may contain multiple projection components. The projection may contain at least one most significant projection component. The projection may contain at least one projected component that may be useful to the classifier.

[0154] Channel / Channel information / Location (venue) / Spatial information (spatia-temporal) / Motion / Object

[0155] Channel information (CI) includes signal strength, signal amplitude, signal phase, spectral power scale, modem parameters (e.g., used in relation to modulation / demodulation in digital communication systems such as WiFi, 4G / LTE), dynamic beamforming information (e.g., including feedback or steering matrices generated by wireless communication devices according to standardization processes such as IEEE 802.11 or other standards), transfer function components, radio state (e.g., used in digital communication systems to decode digital data, baseband processing state, RF processing state, etc.), measurable variables, sensing data, coarse / fine-grained layer information (e.g., physical layer, data link layer, MAC layer, etc.), digital settings, gain settings, RF filter settings, RF front-end switch settings, DC offset settings, DC correction settings, IQ correction settings, influence of the environment during propagation (e.g., location) on the radio signal, and the output signal (for Type 2 devices) of the input signal (radio signal transmitted by a Type 1 device). Each CI may be associated with / include: conversion to the received radio signal, stable behavior of the environment, state profile, radio channel measurement, received signal strength index (RSSI), channel state information (CSI), beamforming dynamic information (including feedback or steering matrix generated by the wireless communication device according to a standardization process such as IEEE 802.11 or other standards), channel impulse response (CIR), channel frequency response (CFR), characteristics of frequency components (e.g., subcarriers) in the bandwidth, channel characteristics, channel response, timestamp, auxiliary information, data, metadata, user data, account data, access data, security data, session data, status data, supervisory data, home data, identification (ID), identifier, device data, network data, neighborhood data, environmental data, real-time data, sensor data, stored data, encrypted data, compressed data, protected data, and / or other channel information. Each CI may be associated with a timestamp and / or arrival time.CSI is used to equalize / undo / minimize / reduce multipath channel effects (transmitting channels) and demodulate a signal that is similar to the one transmitted by the transmitter through the multipath channel. CI can be associated with information related to the frequency bandwidth, frequency signature, frequency phase, frequency amplitude, frequency trend, frequency characteristics, frequency-like characteristics, time-domain elements, frequency-domain elements, time-frequency-domain elements, orthogonal-resolved characteristics, and / or non-orthogonal-resolved characteristics of the signal passing through the channel. TSCI can be a stream of radio signals (e.g., CI).

[0156] CI can be pre-processed, processed, post-processed, stored (e.g., in local memory, portable / mobile memory, removable memory, storage network, cloud memory, in a volatile or non-volatile manner), retrieved, transmitted, and / or received. One or more modem parameters and / or radio state parameters can be kept constant. Modem parameters can be applied to the radio subsystem. Modem parameters can represent radio states. Motion detection signals (e.g., baseband signals, and / or packets decoded / demodulated from baseband signals) can be obtained by processing (e.g., down-converting) a first radio signal (e.g., RF / WiFi / LTE / 5G signal) by the radio subsystem using the radio state represented by the stored modem parameters. Modem parameters / radio states can be updated (e.g., using previous modem parameters or previous radio states). Both previous and updated modem parameters / radio states can be applied to the radio subsystem of a digital communication system. Both previous and updated modem parameters / radio states can be compared / analyzed / processed / monitored in a task.

[0157] Channel information may also be modem parameters (e.g., stored or newly calculated) used to process the radio signal. The radio signal may include multiple probe signals. The same modem parameters can be used to process multiple probe signals. It is also possible to process multiple radio signals using the same modem parameters. Modem parameters may include parameters that indicate the settings or overall configuration for the operation of the radio subsystem or baseband subsystem (or both) of the radio sensor device. Modem parameters may include one or more of the following: gain settings, RF filter settings, RF front-end switch settings, DC offset settings, or IQ compensation settings for the radio subsystem, or digital DC correction settings, digital gain settings, and / or digital filtering settings (e.g., for the baseband subsystem). CI may also relate to information relating to the signal's time, time signature, timestamp, time amplitude, time phase, time trend, and / or time characteristics. CI may relate to information relating to the signal's time-frequency division, signature, amplitude, phase, trend, and / or characteristics. CI may relate to the decomposition of the signal. A CI may relate to information relating to direction, angle of arrival (AoA), angle of the directional antenna, and / or the phase of the signal passing through the channel. A CI may relate to the attenuation pattern of the signal passing through the channel. Each CI may be associated with a Type 1 device and a Type 2 device. Each CI may be associated with the antenna of a Type 1 device and the antenna of a Type 2 device.

[0158] CI can be obtained from communication hardware (e.g., a Type 2 device or a Type 1 device) that can provide CI. Communication hardware may include WiFi-enabled chips / ICs (integrated circuits), chips compliant with 802.11 or 802.16 or other radio / wireless standards, next-generation WiFi-enabled chips, LTE-enabled chips, 5G-enabled chips, 6G / 7G / 8G-enabled chips, Bluetooth-enabled chips, NFC-enabled chips, BLE-enabled chips, UWB chips, and other communication chips (e.g., Zigbee, WiMAX, mesh networks). The communication hardware calculates the CI and stores it in buffer memory so that it can be used for extraction. CI may include data and / or at least one matrix related to channel state information (CSI). At least one matrix may be used for channel equalization and / or beamforming, etc. Channels may be associated with locations. Attenuation can be due to signal propagation in place, signal propagation / reflection / refraction / diffraction through / in / near air (e.g., the air of the place), refracting media / reflective surfaces such as walls, doors, furniture, obstacles and / or barriers. Attenuation can also be due to reflections on surfaces and obstacles (e.g., reflective surfaces, obstacles) such as floors, ceilings, furniture, fixtures, objects, people, pets, etc. Each CI may be associated with a timestamp. Each CI may contain N1 components (e.g., N1 frequency-domain components in a CFR, N1 time-domain components in a CIR, or N1 decomposed components). Each component may be associated with a component index. Each component may be a real, imaginary, or complex quantity, magnitude, phase, flag, and / or set. Each CI may contain a vector or matrix of complex numbers, a set of mixed quantities, and / or a multidimensional set of at least one complex number.

[0159] The components of a TSCI associated with a specific component index can form their own component time series, each associated with its respective index. A TSCI can be divided into N1 component time series. Each individual component time series is associated with its respective component index. The motion characteristics / STI of an object can be monitored based on the component time series. In one example, one or more ranges of CI components (e.g., one range from component 11 to component 23, a second range from component 44 to component 50, and a third range with only one component) can be selected based on criteria / cost functions / signal quality metrics for further processing (e.g., based on signal-to-noise ratio and / or interference level).

[0160] The component-wise properties of the component-feature time series of the TSCI may be calculated. The component-wise properties may be scalars (e.g., energy) or functions with domains and ranges (e.g., autocorrelation function, transformation, inverse transformation). The motion properties / STI of an object may be monitored based on the properties of its components. The total properties of the TSCI (e.g., aggregated properties) may be calculated based on the properties of each component time series of the TSCI. The total properties may be a weighted average of the properties of the components. The motion properties / STI of an object may be monitored based on the total properties. The total may be a weighted average of the individual quantities.

[0161] Type 1 and Type 2 devices may support WiFi, WiMAX, 3G / 3G Beyond, 4G / 4G Beyond, LTE, LTE-A, 5G, 6G, 7G, Bluetooth, NFC, BLE, Zigbee, UWB, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, mesh networks, proprietary wireless systems, IEEE 802.11, 802.15, 802.16 standards, 3GPP standards, and / or other wireless systems.

[0162] A common radio system and / or common radio channel may be shared by a Type 1 transceiver and / or at least one Type 2 transceiver. At least one Type 2 transceiver may transmit its respective signals simultaneously (or: asynchronously, synchronously, sporadically, continuously, repeatedly, in parallel, simultaneously, and / or at one time) using the common radio system and / or common radio channel. A Type 1 transceiver may transmit its signal to at least one Type 2 transceiver using the common radio system and / or common radio channel.

[0163] Each Type 1 and Type 2 device may have at least one transmit / receive antenna. Each CI may be associated with one of the transmit antennas of the Type 1 device and one of the receive antennas of the Type 2 device. Each pair of transmit and receive antennas may be associated with a link, path, communication path, signal hardware path, etc. For example, if a Type 1 device has M (e.g., 3) transmit antennas and a Type 2 device has N (e.g., 2) receive antennas, there may be M × N (e.g., 3 × 2 = 6) links or paths. Each link or path may be associated with a TSCI.

[0164] At least one TSCI may correspond to various pairs of antennas between a Type 1 device and a Type 2 device. A Type 1 device may have at least one antenna. A Type 2 device may also have at least one antenna. Each TSCI may be associated with an antenna of a Type 1 device and an antenna of a Type 2 device. Averaging or weighted averaging may be performed across the antenna links. Averaging or weighted averaging may be performed across at least one TSCI. Averaging may optionally be performed on a subset of at least one TSCI corresponding to a subset of antenna pairs.

[0165] The timestamps of some CIs in a TSCI may be irregular and can be corrected so that the corrected timestamps of time-corrected CIs are spaced evenly over time. For multiple Type 1 devices and / or multiple Type 2 devices, the corrected timestamps may relate to the same or different clocks. The original timestamp associated with each CI can be determined. The original timestamps may not be spaced evenly over time. The original timestamps of all CIs in a particular part of a particular TSCI in the current sliding time window can be corrected so that the corrected timestamps of time-corrected CIs are spaced evenly over time.

[0166] Characteristics and / or STI (e.g., motion information) include position, position coordinates, change in position, position (e.g., initial position, new position), position on map, height, horizontal position, vertical position, distance, displacement, velocity, acceleration, rotational velocity, rotational acceleration, direction, angle of motion, orientation, direction of motion, rotation, path, deformation, transformation, contraction, extension, gait, gait cycle, head movement, repetitive motion, periodic motion, pseudo-periodic motion, impulse motion, sudden movement, fall motion, transient motion, behavior, transient behavior, motion cycle, frequency of motion, temporal trend, temporal profile, temporal characteristics, occurrence, change, temporal change, change in CI, change in frequency, change in timing, change in gait cycle, timing, start time, beginning time, end time, duration, motion history, type of motion, classification of motion, frequency, frequency spectrum, frequency characteristics, presence, absence, proximity, being close , backward movement, object identification / identifier, object components, head movement velocity, head movement direction, mouth-related rate, eye-related rate, respiratory velocity, heart rate, tidal volume, breathing depth, inhalation time, exhalation time, inhalation-to-exhalation time ratio, airflow, heart rate interval, heart rate variability, hand movement rate, hand movement direction, leg movement, body movement, walking speed, hand movement velocity, positional characteristics, object movement-related characteristics (e.g., change of position / location), tool movement, machine movement, compound movement, and / or combination of multiple movements, events, signal statistics, signal dynamics, anomalies, motion statistics, motion parameters, motion detection display, motion magnitude, motion phase, similarity score, distance score, Euclidean distance, weighted distance, L_1 norm, L_2 norm, L_k norm for k>2, statistical distance, correlation, correlation indicator,Autocorrelation, covariance, autocovariance, cross-covariance, inner product, Cartesian product, motion signal transformation, motion features, presence of motion, absence of motion, motion localization, motion identification, motion recognition, presence of object, absence of object, object entry, object exit, object change, motion cycle, number of motions, gait cycle, motion rhythm, motion deformation, gesture, handwriting, head movement, mouth movement, cardiac movement, visceral movement, motion trend, size, length, area, volume, capacity, shape, form, tag, start / start position, end position, start / start amount, end amount, event, fall event, se Security events, accident events, home events, office events, factory events, warehouse events, manufacturing events, assembly line events, maintenance events, car-related events, navigation events, tracking events, door events, door open events, door close events, window events, window open events, window close events, repeatable events, one-time events, consumption, unconsumption, state, physical state, health state, comfort state, emotional state, mental state, other events, analysis, output response, and / or other information may be included. Characteristics and / or STI may be calculated / monitored based on features calculated from CI or TSCI (e.g., feature calculation / extraction). Static segments or profiles (and / or dynamic segments / profiles) may be identified / calculated / analyzed / monitored / extracted / acquired / marked / presented / indicated / remembered / communicated based on feature analysis. Analysis may include motion detection / motion evaluation / presence detection. Computational workloads can be shared across Type 1 devices, Type 2 devices, and other processors.

[0167] Type 1 devices and / or Type 2 devices may be local devices. Local devices may include smartphones, smart devices, TVs, soundbars, set-top boxes, access points, routers, repeaters, wireless signal repeaters / extenders, remote controls, speakers, fans, refrigerators, microwave ovens, coffee machines, hot water pots, appliances, tables, chairs, lights, lamps, door locks, cameras, microphones, motion sensors, security devices, fire hydrants, garage door switches, power adapters, computers, dongles, computer peripherals, electronic pads, sofas, tiles, accessories, home devices, vehicle devices, office devices, building equipment, manufacturing equipment, watches, glass, clocks, televisions, ovens, air conditioners, accessories, utilities, electrical appliances, smart machines, smart vehicles, the Internet of Things (IoT), smart homes, smart offices, smart buildings, smart parking lots, smart systems, and other devices.

[0168] Each Type 1 device may be associated with its own identifier (e.g., ID). Each Type 2 device may also be associated with its own identification (ID). An ID can include numbers, text-and-number combinations, names, passwords, accounts, account IDs, web links, web addresses, indexes to any information, and / or another ID. IDs can be assigned. IDs can be assigned by hardware (e.g., hardwired, via dongles and / or other hardware), software and / or firmware. IDs can be stored (e.g., in a database, in memory, in a server (e.g., a hub device), in the cloud, stored locally, stored remotely, stored permanently, stored temporarily) and retrieved. An ID can be associated with at least one record, account, user, household, address, telephone number, social security number, customer number, another ID, another identifier, timestamp, and / or data collection. The IDs and / or parts of IDs of a Type 1 device may be made available to a Type 2 device. IDs may be used by Type 1 devices and / or Type 2 devices for registration, initialization, communication, identification, verification, detection, recognition, authentication, access control, cloud access, networking, social networking, logging, recording, cataloging, classification, tagging, association, pairing, transactions, electronic transactions, and / or intellectual property control.

[0169] Objects include people, users, subjects, passengers, children, the elderly, infants, sleeping infants, infants in vehicles, patients, workers, high-value workers, professionals, specialists, waiters, customers in malls, travelers at airports / train stations / bus terminals / shipping terminals, staff / workers / customer service personnel in factories / malls / supermarkets / offices / workplaces, service personnel in sewage / air ventilation systems / liftwells, lifts in liftwells, elevators, inmates, people being tracked / monitored, animals, plants, living things, pets, dogs, cats, smartphones, telephone accessories, computers, tablets, mobile computers, dongles, computer accessories, network equipment, WiFi devices, IoT devices, smartwatches, smart glasses, smart devices, speakers, keys, smart keys, wallets, purses, handbags, backpacks, goods, cargo, luggage, equipment, motors, machinery, air conditioners, fans, air conditioning equipment, lighting fixtures, movable lights, televisions, cameras, audio and / or video equipment. This may include stationery, surveillance equipment, parts, signs, tools, carts, tickets, parking tickets, toll tickets, airplane tickets, credit cards, plastic cards, access cards, food packaging, utensils, tables, chairs, cleaning equipment / tools, vehicles, automobiles, vehicles in parking facilities, goods in warehouses / stores / supermarkets / distribution centers, boats, bicycles, airplanes, drones, remote-controlled cars / airplanes / boats, robots, manufacturing equipment, assembly lines, materials / unfinished parts / robots / carts / transportation means on factory floors, trackable objects in airports / shopping marts / supermarkets, non-trackable objects, non-existence of objects, presence of objects, objects with shape, objects with altered shape, objects without shape, mass of fluids, mass of liquids, mass of gases / smoke, fire, flames, electromagnetic (EM) sources, EM media, and / or other objects.

[0170] The object itself can be coupled to communicate with several networks, including WiFi, MiFi, 3G / 4G / LTE / 5G / 6G / 7G, Bluetooth, NFC, BLE, WiMAX, Zigbee, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, mesh networks, ad-hoc networks, and / or other networks. The object itself may be bulky with AC power, but it can be moved for installation, cleaning, maintenance, and renovation. The object can also be installed on movable platforms such as lifts, pads, movable platforms, elevators, conveyor belts, robots, drones, forklifts, automobiles, boats, and vehicles. The object can have multiple parts, each part having a different movement (e.g., change of location / position). For example, the object may be a person walking ahead. While walking, their left and right hands may move in different directions with different instantaneous velocities, accelerations, and movements.

[0171] A wireless transmitter (e.g., a Type 1 device), a wireless receiver (e.g., a Type 2 device), another wireless transmitter, and / or another wireless receiver may move with an object and / or another object (e.g., in previous movement, current movement, and / or future movement). They may be communicatively coupled to one or more nearby devices. They may transmit TSCI and / or information related to TSCI to and / or to nearby devices. They may be together with nearby devices. The wireless transmitter and / or wireless receiver may be part of a small (e.g., coin-sized, cigarette pack-sized, or even smaller) lightweight portable device. The portable device may be wirelessly coupled to nearby devices.

[0172] A nearby device may be a smartphone, iPhone®, Android phone, smart device, smart appliance, smart vehicle, smart gadget, smart TV, smart refrigerator, smart speaker, smartwatch, smart glasses, smart pad, iPad®, computer, wearable computer, notebook computer, or gateway. A nearby device can connect to a cloud server, a local server (e.g., a hub device), and / or other servers via the Internet, a wired internet connection, and / or a wireless internet connection. A nearby device may be portable. Portable devices, nearby devices, local servers (e.g., hub devices), and / or cloud servers can share calculations and / or storage for tasks (e.g., TSCI, determining object properties / STI related to object movement (e.g., position / change of position), calculating time series of power (e.g., signal strength) information, determining / calculating specific functions, searching for local extrema, classification, identifying specific values ​​of offset time, denoising, processing, simplification, cleaning, wireless smart sensing tasks, extracting CI from signals, switching, segmentation, estimating trajectories / paths / tracks, processing maps, processing trajectories / paths / tracks based on environmental models / constraints / limits, correction, correction adjustment, adjustment, map-based (or model-based) correction, error detection, checking for boundary hits, thresholding) and information (e.g., TSCI). Nearby devices may or may not move with the object. Nearby devices may be portable / non-portable / mobile / non-mobile. Nearby devices may use battery power, solar power, AC power and / or other power sources. Nearby devices may have replaceable / non-replaceable batteries and / or rechargeable / non-rechargeable batteries. Nearby devices may be similar to the object. Nearby devices may have the same (and / or similar) hardware and / or software as the object.Nearby devices may include smart devices, network-enabled devices, devices with WiFi / 3G / 4G / 5G / 6G / Zigbee / Bluetooth / NFC / UMTS / 3GPP / GSM / EDGE / TDMA / FDMA / CDMA / WCDMA / TD-SCDMA / ad-hoc networks / other network connectivity, smart speakers, smartwatches, smart clocks, smart appliances, smart machines, smart devices, smart tools, smart vehicles, Internet of Things (IoT) devices, Internet-enabled devices, computers, portable computers, tablets, and other devices. Nearby devices and / or at least one processor associated with a radio receiver, radio transmitter, another radio receiver, another radio transmitter and / or a cloud server (in the cloud) may determine the initial STI of an object. Two or more of them may jointly determine the initial spatial-temporal information. Two or more of them may share intermediate information in determining the initial STI (e.g., initial position).

[0173] In one example, a wireless transmitter (e.g., a Type 1 device, or tracker bot) moves with the object. The wireless transmitter can send a signal to a wireless receiver (e.g., a Type 2 device, or an Origin Register) or determine the object's initial STI (e.g., initial position). The wireless transmitter can also send signals and / or other signals to another wireless receiver (e.g., another Type 2 device, or another Origin Register) to monitor the object's movement (spatial-temporal information). The wireless receiver may also receive signals and / or other signals from the wireless transmitter and / or another wireless transmitter to monitor the object's movement. The positions of the wireless receiver and / or another wireless receiver are known. In another example, a wireless receiver (e.g., a Type 2 device, or tracker bot) may move with the object. The wireless receiver may receive signals sent from the wireless transmitter (e.g., a Type 1 device, or an Origin Register) to determine the object's initial spatial-temporal information (e.g., initial position). The wireless receiver may also receive signals and / or other signals from another wireless transmitter (e.g., another Type 1 device, or another origin register) for monitoring the current movement of an object (e.g., spatial-temporal information). The wireless transmitter may also transmit signals and / or other signals to the wireless receiver and / or another wireless receiver (e.g., another Type 2 device, or another tracker bot) for monitoring the movement of an object. The locations of the wireless transmitter and / or other wireless transmitters may be known.

[0174] Locations include sensing areas, rooms, houses, offices, property, workspaces, corridors, lifts, liftwells, escalators, elevators, sewers, ventilation systems, stairs, collective areas, ducts, air ducts, pipes, enclosed spaces, enclosed structures, semi-enclosed structures, enclosed areas with at least one wall, plants, machinery, engines, structures, structures with wood, structures with glass, structures with metal, structures with walls, structures with doors, structures with gaps, structures with reflective surfaces, structures with liquids, buildings, rooftops, shops, factories, assembly lines, etc. Tel rooms, museums, classrooms, schools, universities, government buildings, warehouses, garages, malls, airports, train stations, bus terminals, hubs, transportation hubs, cargo terminals, government buildings, public facilities, entertainment venues, recreation venues, hospitals, pediatric / neonatal wards, nursing homes, elderly care facilities, senior living facilities, community centers, stadiums, playgrounds, fields, basketball courts, tennis courts, soccer stadiums, baseball fields, gymnasiums, garages, shopping malls, knolls, supermarkets, manufacturing facilities, parking facilities Construction sites, mining facilities, transportation facilities, main roads, roads, valleys, forests, trees, topography, landscapes, caves, patios, land, roads, amusement parks, urban areas, rural areas, suburban areas, metropolitan areas, gardens, squares, plazas, music halls, downtown facilities, open facilities, semi-open facilities, enclosed areas, train platforms, train stations, distribution centers, warehouses, shops, storage facilities, underground spaces, space (e.g., above ground, space) facilities, floating facilities, caves, tunnel facilities, indoor facilities, outdoor facilities, outdoor spaces with some walls / doors / reflective barriers These areas include facilities, open facilities, semi-open facilities, automobiles, trucks, buses, vans, containers, ships / boats, submarines, trains, electric trains, airplanes, vehicles, mobile platforms, caverns, tunnels, pipes, channels, metropolitan areas, commercial areas with relatively tall buildings, valleys, wells, ducts, routes, gas lines, oil pipes, water pipes, interconnecting routes / arrays / roads / tubes / cavities / caves / pipe-like structures / spaces / fluid spaces, the human body, animal bodies, body cavities, organs, bones, teeth, soft tissues, hard tissues, rigid tissues, non-hard tissues, blood / fluid channels, air channels, air ducts, burrows, etc. A location may be an indoor space or an outdoor space, and a location may include both the inside and outside of a space.For example, a location can include both the interior and exterior of a building. For example, a location could be a building with one or more floors, and part of the building could be underground. The shape of the building could be, for example, round, square, rectangular, triangular, or irregular. These are merely examples. This disclosure can be used to detect events in other types of locations or spaces.

[0175] A wireless transmitter (e.g., a Type 1 device) and / or a wireless receiver (e.g., a Type 2 device) may be embedded in a portable device (e.g., a module, or a device having a module) that can move with the object (e.g., in previous and / or current movements). The portable device may be coupled to the object in a communicative manner using wired connections (e.g., via USB, micro USB, FireWire, HDMI, serial port, parallel port, and other connectors) and / or wireless connections (e.g., Bluetooth, Bluetooth Low Energy (BLE), WiFi, LTE, NFC, ZigBee). The portable device may be a lightweight device. The portable may be powered by batteries, rechargeable batteries, and / or AC power. The portable device may be very small (e.g., on a sub-millimeter and / or sub-centimeter scale) and / or small (e.g., coin-sized, card-sized, pocket-sized, or larger). The portable device may be large, bulky, and / or cumbersome (e.g., installed heavy machinery). Portable devices include WiFi hotspots, access points, mobile WiFi (MiFi), dongles with USB / micro USB / FireWire / other connectors, smartphones, portable computers, computers, tablets, smart devices, Internet of Things (IoT) devices, WiFi-enabled devices, LTE-enabled devices, smartwatches, smart glass, smart mirrors, smart antennas, smart batteries, smart lights, smart pens, smart rings, smart doors, smart windows, smart clocks, smart wallets, smart belts, smart handbags, smart cloths / garments, smart ornaments, smart packaging, smart paper / books / magazines / posters / printed materials / signage / displays / illuminated systems / lighting systems, smart keys / tools, smart bracelets / chains / necklaces / clothing / accessories, smart pads / cushions, smart tiles / blocks / bricks / building materials / other materials.Smart trash cans / waste containers, smart food carriages / storage, smart balls / rackets, smart chairs / sofas / beds, smart shoes / footwear / carpets / mats / shoe racks, smart gloves / handwear / rings / handwear, smart hats / caps / cosmetics / stickers / tattoos, smart mirrors, smart toys, smart pills, smart cookware, smart bottles / food containers, smart tools, smart devices, IoT devices, WiFi-enabled devices, network-enabled devices, 3G / 4G / 5G / 6G-enabled devices, UMTS devices, 3GPP devices, GSM devices, EDGE devices, TDMA devices, FDMA devices, CDMA devices, WCDMA devices, TD-SCDMA devices, embedded devices, embeddable devices, air conditioners, refrigerators, heaters, furnaces, furniture, ovens, cooking devices This could include televisions / set-top boxes (STBs) / DVD players / audio players / video players / remote controls, hi-fi, audio devices, speakers, lamps / lights, walls, doors, windows, roofs, tiles / roofing boards / structures / attic structures / devices / features / installations / fixtures, lawnmowers / gardening tools / tools / machine tools / garage equipment, garbage cans / containers, 20-foot / 40-foot containers, storage containers, factory / production / manufacturing equipment, repair tools, fluid containers, machinery, installed machinery, vehicles, carts, wagons, warehouse vehicles, automobiles, bicycles, motorcycles, boats, ships, airplanes, baskets / boxes / bags / buckets / containers, smart plates / cups / bowls / pots / mats / appliances / kitchen utensils / kitchenware / kitchen accessories / cabinets / tables / chairs / tiles / lights / water pipes / faucets / gas ranges / ovens / dishwashers / , etc. Portable devices may have batteries that are replaceable, non-replaceable, rechargeable, and / or non-rechargeable. Portable devices may be charged wirelessly. The portable device may be a smart payment card. The portable device may be a payment card used in parking lots, highways, entertainment parks, or other places / facilities where payment is required. As described above, the portable device may have an identity (ID) / identifier.

[0176] Events can be monitored based on TSCI. Events can be object-related events such as objects (e.g., people and / or sick people) falling, spinning, hesitating, resting, impact (e.g., a person hitting a punching bag, door, window, bed, chair, table, desk, cabinet, box, another person, animal, bird, flight, table, chair, ball, bowling ball, tennis ball, football, soccer ball, baseball, basketball, volleyball), two-body actions (e.g., leaving a balloon, catching a fish, shaping clay, writing a paper, a person typing on a computer), moving a car in a garage, a person carrying a smartphone, a person walking around an airport / mall / government building / office / etc., and autonomously moving objects / machines moving around (e.g., vacuum cleaners, utility vehicles, cars, drones, self-driving cars).

[0177] Tasks or wireless smart sensing tasks include object detection, presence detection, proximity detection, object recognition, activity recognition, object verification, object counting, daily activity monitoring, health monitoring, vital signs monitoring, health status monitoring, baby monitoring, elderly monitoring, sleep monitoring, sleep stage monitoring, gait monitoring, motion monitoring, tool detection, tool recognition, tool verification, patient detection, patient monitoring, patient verification, machine detection, machine verification, human detection, human recognition, human verification, baby detection, baby recognition, baby verification, human respiration detection, human respiration recognition, human respiration estimation, human respiration verification, human heart rate detection, human heart rate recognition, human heart rate estimation, human heart rate verification, fall detection, fall recognition, fall estimation, fall verification, emotion detection, emotion recognition, emotion estimation, emotion verification, motion detection, motion degree estimation, Motion recognition, motion estimation, motion verification, periodic motion detection, periodic motion estimation, periodic motion verification, repetitive motion detection, periodic motion recognition, repetitive motion estimation, repetitive motion verification, static motion recognition, static motion detection, static motion estimation, static motion verification, cyclic stationary motion detection, cyclic stationary motion recognition, cyclic stationary motion estimation, cyclic stationary motion verification, transient motion detection, transient motion recognition, transient motion estimation, transient motion verification, trend detection, trend recognition, trend estimation, trend verification, respiration detection, respiration recognition, respiration estimation, human biometric detection, human biometric recognition, human biometric estimation, human biometric verification, environmental informatics detection, environmental informatics recognition, environmental informatics estimation, environmental informatics verification, gait detection, gait recognition, gait estimation, gait verification, gesture detection, gesture recognition, gesture estimation, gesture verification, machine learning, supervised learning, unsupervised learning, semi-supervised learning, clustering, feature extraction, feature training, principal component analysis, eigenvalue decomposition, frequency decomposition, time decomposition,Time-frequency decomposition, function decomposition, other decompositions, training, discrimination training, supervised training, unsupervised training, semi-supervised training, neural networks, sudden motion detection, fall detection, hazard detection, life-threat detection, regular motion detection, static motion detection, periodic steady motion detection, intrusion detection, suspicious movement detection, security, safety monitoring, navigation, guidance, map-based processing, map-based correction, model-based processing / correction, irregularity detection, localization, room sensing, tracking, multiple object tracking, indoor tracking, indoor positioning, indoor navigation, energy management, power transmission, wireless power transmission, object counting, car tracking in parking garages, device / system activation (e.g., security systems, access systems, alarms, sirens, speakers, televisions, entertainment systems, cameras, heater / air conditioning (HVAC) systems, ventilation systems, lighting systems, game systems, coffee machines, cooking appliances, cleaning equipment, housekeeping equipment), geometric estimation This may include, but may also include, augmented reality, wireless communication, data communication, signal broadcasting, networking, coordination, management, encryption, protection, cloud computing, other processing and / or other tasks. Tasks can be performed by Type 1 devices, Type 2 devices, other Type 1 devices, other Type 2 devices, nearby devices, local servers (such as hub devices), edge servers, cloud servers and / or other devices.This task can be based on TSCI between any pair of Type 1 and Type 2 devices. The Type 2 device may be a Type 1 device, and vice versa. The Type 2 device may perform the role (e.g., functionality) of the Type 1 device temporarily, continuously, sporadically, simultaneously, and / or simultaneously and / or vice versa. The first part of the task may include at least one of the following: preprocessing, processing, signal conditioning, signal processing, postprocessing, sporadic / continuous / simultaneous / concurrent / dynamic / adaptive / on-demand / as needed processing, calibration, denoising, feature extraction, coding, encryption, transformation, mapping, motion detection, motion estimation, motion change detection, motion pattern detection, motion pattern estimation, motion pattern recognition, vital sign detection, vital sign estimation, vital sign recognition, periodic motion detection, periodic motion estimation, repetitive motion detection / estimation, respiratory rate detection, respiratory rate estimation, respiratory pattern detection, respiratory pattern estimation, respiratory pattern recognition, heart rate detection, heart rate estimation, cardiac pattern detection, cardiac pattern estimation, cardiac pattern recognition, gesture detection, gesture estimation, gesture recognition, velocity detection, velocity estimation, object positioning, object tracking, navigation, acceleration estimation, acceleration detection, fall detection, change detection, intruder (and / or tort) detection, baby detection, baby monitoring, patient monitoring, object recognition, wireless power transmission, and / or wireless charging.

[0178] The second part of the task is to play at least one of the following sound clips: smart home tasks, smart office tasks, smart building tasks, smart factory tasks (e.g., manufacturing using machines or assembly lines), smart Internet of Things (IoT) tasks, smart system tasks, smart home operations, smart office operations, smart building operations, smart manufacturing operations (e.g., moving supplies / parts / raw materials to machines / assembly lines), IoT operations, smart system operations, turning lights on, turning lights off, controlling lighting in at least one of rooms, areas and / or places, playing sound clips, playing sound clips in at least one of rooms, areas and / or places, playing at least one sound clip of welcome, greeting, farewell, first message and / or second message related to the first part of the task, turning appliances on, turning appliances off, This may include at least one of the following: controlling electrical appliances in at least one of rooms, areas, and / or locations; turning on or off electrical systems; controlling electrical systems in at least one of rooms, areas, and / or locations; turning on or off security systems; controlling security systems in at least one of rooms, areas, and / or locations; turning on or off mechanical systems; controlling mechanical systems in at least one of rooms, areas, and / or locations; and / or controlling at least one of air conditioning systems, heating systems, ventilation systems, lighting systems, heating devices, stoves, entertainment systems, doors, fences, windows, garages, computer systems, networked devices, networked systems, home appliances, office equipment, lighting devices, robots (e.g., robotic arms), smart vehicles, smart machines, assembly lines, smart devices, Internet of Things (IoT) devices, smart home devices, and / or smart office devices.

[0179] This task involves detecting when a user returns home, when a user leaves, when a user moves from one room to another, when windows / doors / garage doors / blinds / curtains / panels / solar panels / sunshades are controlled / locked / unlocked / opened / closed / partially opened, when pets are detected, when a user does something (e.g., sleeping on the sofa, sleeping in the bedroom, running on the treadmill, cooking, sitting on the sofa, watching TV, eating in the kitchen, eating in the dining room, going up and down stairs, going out / returning, being in the bathroom), when user / pet location is monitored / detected, when detected an action is taken automatically (e.g., sending a message, notifying / reporting to someone), when a user is detected an action is taken towards the user, when lights are turned on / off / dimened, when music / radio / home entertainment systems are turned on / off, when TV / This may include turning on / off / adjusting / controlling hi-fi / set-top boxes (STBs) / home entertainment systems / smart speakers / smart devices, turning on / off / adjusting air conditioning systems, turning on / off / adjusting ventilation systems, turning on / off / adjusting heating systems, adjusting / controlling curtains / light shades, turning on / off / starting computers, turning on / off / preheating / controlling coffee machines / hot water kettles, turning on / off / preheating / controlling cookers / ovens / microwaves / other cooking appliances, checking / adjusting temperatures, checking weather forecasts, checking phone message boxes, checking emails, checking systems, controlling / adjusting systems, checking / controlling / preparing / disabling security systems / baby monitors, checking / controlling refrigerators, and reporting (e.g., via speakers such as Google Home and Amazon Echo, via web pages / email / messaging systems / notification systems).

[0180] For example, when a user arrives at home in their car, the task may automatically detect the user or vehicle approaching, open the garage door upon detection, turn on the driveway / garage lights as the user approaches the garage, and turn on the air conditioning / heater / fan. As the user enters the house, the task may automatically turn on the entrance lights, turn off the driveway / garage lights, emit a greeting message to wake the user, turn on music, turn on the radio and adjust it to the user's favorite radio news channel, open the curtains / blinds, monitor the user's mood, adjust the lighting and sound environment according to the user's mood or current / imminent events on the user's daily calendar (for example, if the user has dinner with their girlfriend in an hour, activate romantic lighting and music), warm up food the user prepared in the morning in the microwave, perform diagnostic checks on all systems in the house, check the weather forecast for tomorrow's work, check news of the user's interest, and view the user's calendar and to-do list. Check reminders, check phone answering systems, messaging systems, and emails, deliver verbal reports using dialogue systems / speech synthesis, and remind the user of their mother's birthday (e.g., using audible tools such as speakers, hi-fi, speech synthesis, sound, voice, music, song, sound field, background sound field, and dialogue systems; using visual tools such as TVs / entertainment systems / computers / notebooks / smartpads / displays / light / color / brightness / patterns; using haptic tools / virtual reality tools / gestures / tools; using smart devices / appliances / materials / furniture / fixtures; using web tools / servers / hub devices / cloud servers / fog servers / edge servers / home networks / mesh networks; using messaging tools / notification tools / communication tools / scheduling tools / email; using user interfaces / GUIs; using scents / odors / fragrances / tastes; using nervous tools / nerve system tools; and using combinations of these); create reports and deliver reports (e.g., using reminder tools as described above).Tasks may include pre-activating the air conditioning / heater / ventilation system or pre-adjusting the temperature setting of a smart thermostat. When a user moves from the entrance to the living room, the task may include turning on the living room lights, opening the living room curtains, opening the windows, turning off the entrance lights behind the user, turning on the TV and set-top box, turning on the set-top box, setting the TV to the user's preferred channel, and adjusting fixtures according to the user's preferences and conditions / state (e.g., adjusting the lighting, selecting / playing music to create a romantic atmosphere).

[0181] Another example could be: When the user wakes up in the morning, the task could be to detect the user moving around the bedroom, open the blinds / curtains, open the windows, turn off the alarm clock, adjust the indoor temperature profile from the night temperature profile to the day temperature profile, turn on the bedroom light, turn on the bathroom light as the user approaches the bathroom, check the radio or streaming channels, play the morning news, turn on the coffee machine, preheat the water, turn off the security system, etc. When the user walks from the bedroom to the kitchen, the task could be to turn on the kitchen and hallway lights, turn off the bedroom and bathroom lights, move music / messages / reminders from the bedroom to the kitchen, turn on the kitchen TV, change the TV to the morning news channel, lower the kitchen blinds, open the kitchen window to let in fresh air, unlock the back door for the user to check the backyard, adjust the kitchen temperature setting, etc. Another example could be: When a user leaves home for work, the task could include detecting the user's departure, sending a farewell and / or a message wishing them a good day, opening and closing the garage door, turning garage and driveway lights on / off, turning them off / dimening them to save energy (only if the user fails to do so), closing / locking all windows / doors (only if the user fails to do so), turning off appliances (especially stoves, ovens, and microwaves), turning on / activating the home security system for protection against intruders, adjusting the HVAC / heating / ventilation system to an "away from home" profile to save energy, and sending alerts / reports / updates to the user's smartphone.

[0182] Movement includes: no movement, resting movement, motionless action, movement, change of location / position, deterministic movement, transient movement, falling movement, repetitive movement, periodic movement, pseudo-periodic movement, periodic / repetitive movement related to respiration, periodic / repetitive movement related to heartbeat, periodic / repetitive movement related to living organisms, periodic / repetitive movement related to machines, periodic / repetitive movement related to artificial objects, periodic / repetitive movement related to nature, complex movement related to transient and periodic elements, repetitive movement, non-deterministic movement, probabilistic movement, chaotic movement, random movement, complex movement with non-deterministic and deterministic elements, stationary random movement, pseudo-stationary random movement, periodic stationary random movement, non-stationary random movement, non-stationary random movement with periodic ACF over time. Random motion, pseudo-steady random motion over a period, random motion where instantaneous ACF has pseudo-periodic / repetitive elements over a period, mechanical motion, vehicle motion, drone motion, air-related motion, wind-related motion, weather-related motion, water-related motion, fluid-related motion, ground-related motion, changes in magnetic properties, subsurface motion, seismic motion, plant motion, animal motion, animal movement, human body motion, normal motion, abnormal motion, dangerous motion, warning motion, suspicious motion, rain, fire, flood, tsunami, explosion, collision, imminent collision, human body motion, head motion, facial motion, eye motion, oral motion, tongue motion, neck motion, finger motion, hand motion, arm motion, shoulder motion, body motion, chest motion, abdominal motion, waist motion, leg motion, foot motion, body joint motion, knee motion, elbow motion, upper body motion, lower body motion, skin motion, subcutaneous motion, subcutaneous tissue motion, This may include at least one of the following: vascular movement, venous movement, organ movement, heart movement, lung movement, stomach movement, intestinal movement, bowel movement, feeding movement, respiratory movement, facial expression, eye expression, mouth expression, voice movement, singing movement, feeding movement, gestures, hand gestures, arm gestures, keystrokes, typing strokes, user interface gestures, man-machine interaction, gait, dance movements, coordinated movements, and / or coordinated bodily movements.

[0183] Heterogeneous ICs of a Type 1 device and / or any Type 2 receiver may include low-noise amplifiers (LNAs), power amplifiers, transmit-receive switches, media access controllers, baseband radios, 2.4 GHz radios, 3.65 GHz radios, 4.9 GHz radios, 5 GHz radios, 5.9 GHz radios, sub-6 GHz radios, 60 GHz radios, sub-60 GHz radios, and / or other radios. Heterogeneous ICs may include a processor, memory communicatively coupled to the processor, and a set of instructions stored in the memory that are executed by the processor. ICs and / or any processors may include at least one of the following: general-purpose processors, special-purpose processors, microprocessors, multiprocessors, multicore processors, parallel processors, CISC processors, RISC processors, microcontrollers, central processing units (CPUs), graphical processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), embedded processors (e.g., ARM), logic circuits, other programmable logic devices, discrete logic, and / or combinations thereof. Heterogeneous ICs include broadband networks, wireless networks, mobile networks, mesh networks, cellular networks, wireless local area networks (WLAN), wide area networks (WAN), metropolitan networks (MAN), WLAN standards, WiFi, LTE, LTE-A, LTE-U, 802.11 standards, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11af, 802.11ah, 802.11ax, 802.11ay, mesh network standard 802.16, 3G, 3.5G, 4G, Beyond 4G, 4.5G, 6G, 7G, 8G, 9G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, Bluetooth, and Bluetooth Low-Energy. It may support (BLE), NFC, Zigbee, WiMAX, and other wireless network protocols.

[0184] The processor may include general-purpose processors, special-purpose processors, microprocessors, microcontrollers, embedded processors, digital signal processors, central processing units, graphical processing units (GPUs), multiprocessors, multicore processors, and / or processors with graphics capabilities, and / or combinations thereof. Memory may be volatile, non-volatile, random-access memory (RAM), read-only memory (ROM), programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), hard disks, flash memory, CD-ROMs, DVD-ROMs, magnetic storage devices, optical storage devices, organic storage devices, storage systems, storage networks, network storage devices, cloud storage devices, edge storage devices, local storage devices, external storage devices, internal storage devices, or other forms of non-temporary storage media known in technology. The set of instructions corresponding to the steps of the method (machine executable code) can be implemented directly in hardware, software, firmware, or a combination thereof. The set of instructions can be embedded, pre-loaded, loaded at boot time, loaded on the fly, loaded on demand, pre-installed, installed, and / or downloaded.

[0185] Presentations may be visual (e.g., using a combination of visuals, graphics, text, symbols, colors, shades, video, animation, sound, voice, acoustics, etc.), graphical (e.g., using a GUI, animation, video), textual (e.g., web pages with text, messages, animated text), symbolic (e.g., emojis, signs, hand gestures), or mechanical (e.g., vibration, actuator movement, tactile feedback, etc.).

[0186] Basic calculation

[0187] The computational workload associated with this method is shared among processors, Type 1 heterogeneous radio devices, Type 2 heterogeneous radio devices, local servers (e.g., hub devices), cloud servers, and other processors.

[0188] Operations, preprocessing, processing, and / or postprocessing may be applied to data (e.g., TSCI, autocorrelation, TSCI features). Operations may include preprocessing, processing, and / or postprocessing. Preprocessing, processing, and / or postprocessing may also be operations. Operations include preprocessing, processing, postprocessing, scaling, calculation of confidence coefficients, calculation of line-of-sight (LOS) quantities, and non-LOS Calculation of (NLOS) quantities, calculation of quantities including LOS and NLOS, calculation of single-link quantities (e.g., path, communication path, link between transmitting and receiving antennas), calculation of quantities including multiple links, calculation of operand functions, filtering, linear filtering, nonlinear filtering, folding, grouping, energy calculation, low-pass filtering, band-pass filtering, high-pass filtering, median filtering, rank filtering, quartile filtering, percentile filtering, finite impulse response (FIR) filtering, infinite impulse response (IIR) filtering, moving average (MA) filtering, autoregressive (AR) filtering, autoregressive moving average (ARMA) filtering, selection filtering, adaptive filtering, interpolation, decimation, subsampling, upsampling, resampling, time correction, time-based correction, phase correction, magnitude correction, phase washing, amplitude washing, matched filtering, enhancement, restoration, noise reduction, smoothing, signal tuning, enhancement, restoration, linear transformation, nonlinear transformation, inverse transformation, frequency transformation, inverse frequency transformation, Fourier transform (FT), discrete-time FT (DTFT), Discrete FT (DFT), Fast FT (FFT), Wavelet Transform, Laplace Transform, Hilbert Transform, Hadamar Transform, Trigonometric Transform, Sine Transform, Cosine Transform, DCT, Power of 2 Transform, Sparse Transform, Graph-Based Transform, Graph Signal Processing, Fast Transform, Transform combined with zero padding, Cyclic Padding, Padding, Zero Padding, Feature Extraction, Decomposition, Projection, Orthogonal Projection, Non-Orthogonal Projection, Over-projection (oecomleeObjection, eigendecomposition, singular value decomposition (SVD), principle component analysis (ICA), independent component analysis (ICA), grouping, sorting, thresholding, soft thresholding, hard thresholding, clipping, soft clipping, first derivative, second derivative, higher derivative, convolution, multiplication, partitioning, addition, subtraction, integration, maximization, minimization, least squares error, recursive least squares, constrained least squares, batch least squares, least absolute deviation, least mean squares deviation, least absolute deviation, local maximization, local minimization, cost function optimization, neural network, recognition, labeling, training, class Tagging, machine learning, supervised learning, unsupervised learning, semi-supervised learning, comparison with other TSCIs, similarity score calculation, quantization, vector quantization, matching tracking, compression, encryption, coding, storage, transmission, normalization, time normalization, frequency domain normalization, classification, clustering, labeling, tagging, learning, detection, estimation, learning network, mapping, remapping, expansion, storage, retrieval, transmission, reception, representation, joining, merging, splitting, tracking, monitoring, matched filtering, Kalman filtering, particle filtering, interpolation, extrapolation, histogram estimation This may include importance sampling, Monte Carlo sampling, compressed sensing, representation, merge, join, split, scrambling, error protection, forward error correction, do nothing, time variation processing, adjusted averaging, weighted averaging, arithmetic mean, geometric mean, harmonic mean, averaging over selected frequencies, averaging over antenna links, logical operations, substitution, combination, sorting, AND, OR, XOR, join, cross, vector addition, vector subtraction, vector multiplication, vector division, inverse, norm, distance, and / or other operations. Operations may be pre-processing, processing, and / or post-processing. Operations may be applied jointly on multiple time series or functions.

[0189] Functions (e.g., operand functions) include scalar functions, vector functions, discrete functions, continuous functions, polynomial functions, characteristics, features, magnitude, phase, exponential functions, logarithmic functions, trigonometric functions, transcendental functions, logical functions, linear functions, algebraic functions, nonlinear functions, piecewise functions, real functions, complex functions, vector-valued functions, inverse functions, derivatives, integral functions, circular functions, functions of other functions, one-to-one functions, one-to-many functions, many-to-one functions, many-to-many functions, zero crossings, absolute functions, index functions, mean, mode, median, range, statistics, histogram, variance, standard deviation, measure of change, expansion, variance, deviation, divergence, range, interquartile range, total deviation, absolute deviation, arithmetic mean, geometric mean, harmonic mean, trimmed mean, percentile, square, cube, square root, power, sine, cosine, tangent, cotangent. Elliptic functions, parabolic functions, hyperbolic functions, game functions, zeta functions, absolute value, threshold, limit function, floor function, rounding function, sign function, quantization, piecewise constant functions, composite functions, functions of functions, time functions processed by operations (e.g., filtering), probabilistic functions, inferential functions, deterministic functions, periodic functions, iterative functions, transformations, frequency transformations, inverse frequency transformations, discrete-time transformations, Laplace transforms, Hilbert transforms, sine transforms, cosine transforms, trigonometric transforms, wavelet transforms, integer transformations, powers of two transformations, sparse transformations, projections, decompositions, principal component analysis (PCA), neural networks, feature extraction, moving functions, moving window functions for adjacent items in time series, filtering functions, convolutions, mean functions, histograms, variance / standard deviation functions, statistical functions, short-time transformations, discrete transformationsDiscrete Fourier transform, Discrete cosine transform, Discrete sine transform, Hadamard transform, Eigenvalue decomposition, Eigenvalue, Singular value decomposition (SVD), Singular value, Orthogonal decomposition, Matching pursuit, Sparse transform, Arbitrary decomposition, Graph-based processing, Graph-based transform, Graph signal processing, Classification, Class / group / category identification, Labeling, Learning, Machine learning, Detection, Estimation, Feature extraction, Learning network, Feature extraction, Denoising, Signal enhancement, Encoding, Encryption, Mapping, Remapping, Vector quantization, Low-pass filtering, High-pass filtering, Band-pass filtering, Matched filtering, Kalman filtering, Preprocessing, Postprocessing, Particle filtering, FIR filtering, IIR filtering, Autoregressive (AR) filtering, Adaptive filtering, First derivative, Higher derivative, Integration, Zero crossing, Smoothing, Median filtering, Mode filtering, Sampling, Random sampling, Resampling function, Downsampling, Downconverting, Upsampling, Upconverting, Interpolation, Extrapolation, Importance sampling, Monte Carlo sampling, Compressed sensing, Statistics, This may include short-term statistics, long-term statistics, autocorrelation functions, cross-correlation, moment generating functions, time averages, weighted averages, special functions, Bessel functions, error functions, complementary error functions, beta functions, gamma functions, integral functions, Gaussian functions, Poisson functions, etc.

[0190] The steps (or each step) of this disclosure may include machine learning, training, discriminative training, deep learning, neural networks, continuous-time processing, distributed computing, distributed storage, and acceleration using GPUs / DSPs / coprocessors / multicores / multiprocessing.

[0191] Frequency transformations may include Fourier transforms, Laplace transforms, Hadamard transforms, Hilbert transforms, sine transforms, cosine transforms, trigonometric transforms, wavelet transforms, integer transforms, power-of-two transforms, zero-padding and transformation combinations, power Fourier transforms with zero-padding, and / or other transforms. Faster and / or approximate versions of the transforms may be performed. Transforms can be performed using floating-point and / or fixed-point arithmetic.

[0192] Inverse frequency transforms can include inverse Fourier transforms, inverse Laplace transforms, inverse Hadamard transforms, inverse Hilbert transforms, inverse sine transforms, inverse cosine transforms, inverse trigonometric transforms, inverse wavelet transforms, inverse integer transforms, inverse powers of two transforms, combinations of zero-padding and transforms, inverse Fourier transforms with zero-padding, and / or other transforms. Faster and / or approximate versions of the transforms can be performed. Transforms can be performed using floating-point and / or fixed-point arithmetic.

[0193] Quantities / features can be calculated from TSCI. Quantities include motion, position, map coordinates, height, velocity, acceleration, movement angle, rotation, dimensions, volume, time trend, one-time pattern, repeating pattern, developmental pattern, time pattern, mutually exclusive patterns, association / correlation patterns, cause-and-effect, short-term / long-term correlation, trend, slope, preference, statistics, typical behavior, atypical behavior, time trend, time profile, periodic motion, repetitive motion, repetition, trend, change, rapid change, gradual change, frequency, transient, respiration, gait, behavior, event, suspicious event, dangerous event, warning event, warning, belief, proximity, collision, power, signal, signal power, signal strength, signal quantity, received signal strength index (RSSI), signal amplitude, signal phase, signal frequency components, signal frequency band components, channel state information (CSI), map, time, frequency, time-period This may include at least one statistic from among wavenumber, resolution, orthogonal resolution, non-orthogonal resolution, tracking, respiration, palpitations, statistical parameters, cardiopulmonary statistics / analysis (e.g., output response), daily activity statistics / analysis, chronic disease statistics / analysis, medical statistics / analysis, early (or instantaneous or simultaneous or delayed) indication / suggestion / sign / sign / verifier / detection / symptom / condition / state, biometrics, baby, patient, machine, device, temperature, vehicle, parking lot, place, lift, elevator, space, road, fluid flow, home, room, office, house, building, warehouse, storage, system, ventilation, fan, pipe, duct, people, human, car, boat, truck, airplane, drone, downtown, crowd, impulsive event, periodic stationary, environment, vibration, material, surface, three-dimensional, two-dimensional, local, whole, presence, and / or other measurable quantities / variables.

[0194] Sliding window / algorithm

[0195] A sliding time window may have a time-varying window width. To enable rapid acquisition, it may start smaller and increase over time to a steady-state size. The steady-state size may be related to the monitored frequency, repetitive motion, transient motion, and / or STI. Even in the steady state, the window size may be adaptively (and / or dynamically) changed (e.g., adjusted, modified, corrected) based on battery life, power consumption, available computing power, changes in the quantity being monitored, the nature of the motion being monitored, etc.

[0196] The time shift between two sliding time windows in adjacent time cases can be constant / variable / locally adaptive / dynamically adjusted over time (over time). When shorter time shifts are used, any monitoring updates may be more frequent, which can be used for rapidly changing situations, object movements, and / or objects. Longer time shifts can be used for slower situations, object movements, and / or objects.

[0197] Window width / size and / or time shift may be changed (e.g., adjusted, modified, corrected) according to user requests / selections. Time shift may be changed automatically (e.g., controlled by the processor / computer / server / hub device / cloud server) and / or adaptively (and / or dynamically).

[0198] At least one property (e.g., feature value, or feature point) of a function (e.g., autocorrelation function, autocovariance function, cross-correlation function, cross-covariance function, power spectral density, time function, frequency domain function, frequency transform) can be determined (e.g., by an object tracking server, a processor, a type 1 heterogeneous device, a type 2 heterogeneous device, and / or another device). At least one property of this function may include: maximum, minimum, extremum, local maximum, local minimum, local extremum, local extremum with a positive time offset, first local extremum with a positive time offset, nth extremum with a positive time offset, first local extremum with a negative time offset, restricted maximum, restricted minimum, restricted extremum, significant maximum, significant minimum, significant extremum, gradient, derivative, higher-order derivative, maximum gradient, minimum gradient, local maximum gradient, local maximum gradient with a positive time offset, local minimum gradient, restricted maximum gradient, restricted minimum gradient, maximum higher-order derivative, minimum higher-order derivative, restricted higher-order derivative, zero crossing, zero crossing with a positive time offset, n^th zero crossing with a positive time offset, zero crossing with a negative time offset, n^th zero crossing with a negative time offset, restricted zero crossing, zero crossing of the gradient, zero crossing of the gradient of a higher-order derivative, and / or other properties. At least one argument of the function related to at least one property of the function may be identified. A certain quantity (for example, the spatial-temporal information of an object) can be determined based on at least one argument of a function.

[0199] Characteristics (e.g., motion characteristics of an object in a location) include instantaneous characteristics, short-term characteristics, repeating characteristics, repetitive characteristics, history, incremental characteristics, change characteristics, deviation characteristics, phase, amplitude, degree, time characteristics, frequency characteristics, time-frequency characteristics, decomposition characteristics, orthogonal decomposition characteristics, non-orthogonal decomposition characteristics, deterministic characteristics, stochastic characteristics, inferential characteristics, autocorrelation function (ACF), mean, variance, standard deviation, measure of change, spread, variability, deviation, divergence, range, quartile range, total variation, absolute deviation, total deviation, statistics, duration, timing, trend, periodic characteristics, repeating characteristics, long-term characteristics, history characteristics, mean characteristics, latest characteristics, past characteristics, future characteristics, predictive characteristics, position, distance, height, speed, direction, velocity, acceleration, change in acceleration, angle, angular speed, angular velocity, change in angular velocity, object This includes at least one of the following: angular acceleration, change in angular acceleration, object orientation, angle of rotation, deformation of an object, shape of an object, change in shape of an object, change in size of an object, change in structure of an object, and / or change in properties of an object.

[0200] At least one local maximum and at least one local minimum of the function can be identified. At least one local signal-to-noise ratio analogue (SNR analogue) parameter may be calculated for each pair of adjacent local maximums and local minimums. The SNR analogue parameter may be a function (e.g., linear, logarithmic, exponential, monotonic) of the amount of the local maximum (e.g., power, magnitude) over the same amount of the local minimum. It may also be a function of the difference between the amount of the local maximum and the same amount of the local minimum. Significant local peaks can be identified or selected. Each significant local peak may be a local maximum with an SNR analogue parameter greater than threshold T1, and / or a local maximum with an amplitude greater than threshold T2. At least one local minimum and at least one local minimum in the frequency domain can be identified / calculated using a persistence-based approach.

[0201] A set of selected significant local peaks may be chosen from a set of identified significant local peaks based on selection criteria (e.g., quality criteria, signal quality status). The object's properties / STI may be calculated based on the selected set of significant local peaks and the frequency values ​​associated with that set. In one example, the selection criteria may always correspond to selecting the strongest peak within a range. The strongest peak may be selected, but the unselected peaks may still be significant (or even strong).

[0202] Significant unselected peaks may be saved and / or monitored as “reserved” peaks for use in future selections within future sliding time windows. For example, there may be a particular peak (at a specific frequency) that appears consistently over time. Initially, it may not be selected even if significant (because other peaks may become stronger). However, later in time, the peak may become stronger, more dominant, and thus selected. If it becomes “selected,” it can be traced back in time and considered “selected” at an earlier point when it was significant but not selected. In such cases, the traced peak can replace the previously selected peak at an earlier point. The replaced peak may be a relatively weaker peak or a peak that appears temporally isolated (i.e., only appears for a short time).

[0203] In other examples, the selection criteria may not correspond to selecting the strongest peak within the range. Instead, in addition to the "strength" of the peak, the "trace" of peaks that may have occurred in the past, especially those that have been identified for a long time, may also be considered.

[0204] For example, if a finite state machine (FSM) is used, it can select peaks based on the state of the FSM. The decision threshold can be calculated adaptively (and / or dynamically) based on the state of the FSM.

[0205] Similarity scores and / or component similarity scores may be calculated based on a pair of temporally adjacent CIs of a TSCI (e.g., by a server (e.g., a hub device), a processor, a Type 1 device, a Type 2 device, a local server, a cloud server, and / or another device). The pair may be obtained from the same sliding window or from two different sliding windows. Similarity scores may also be based on a pair of temporally adjacent or not-so-adjacent CIs from two different TSCIs. The similarity score and / or component similarity score may be / include the following: time reversal resonating strength (TRRS), correlation, cross-correlation, autocorrelation, correlation indicator, covariance, cross-covariance, autocovariance, dot product of two vectors, distance score, norm, metric, quality metric, signal quality condition, statistical properties, discrimination score, neural network, deep learning network, machine learning, training, discrimination, weighted average, preprocessing, denoising, signal conditioning, filtering, time correction, time adjustment, phase offset compensation, transformation, component-by-component operations, feature extraction, finite state machine, and / or other scores. Properties and / or STI may be determined / calculated based on the similarity score.

[0206] Any threshold can be predetermined, adaptively (and / or dynamically), and / or determined by a finite state machine. Adaptive determination may be based on time, space, location, antenna, path, link, state, battery life, battery level, available power, available computing resources, available network bandwidth, etc.

[0207] Thresholds applied to test statistics to distinguish between two events (or two conditions, or two situations, or two states), A and B, can be determined. Data (e.g., CI, channel status information (CSI), power parameters) can be collected under A and / or B in a training scenario. Test statistics can be calculated based on the data. The distribution of test statistics under A can be compared to the distribution of test statistics under B (reference distribution), and thresholds can be selected according to several criteria. Decision criteria may include maximum likelihood estimation (ML), maximum posterior probability (MAP), discrimination training, minimum type 1 error for a given type 2 error, minimum type 2 error for a given type 1 error, and / or other decision criteria (e.g., quality criteria, signal quality conditions). Thresholds can be adjusted to achieve different sensitivities to A, B, and / or other events / conditions / scenarios / states. Threshold adjustment may be automatic, semi-automatic, and / or manual. Threshold adjustments can be applied once, sometimes, often, periodically, repeatedly, occasionally, sporadically, and / or on demand. Threshold adjustments can be adaptive (and / or dynamically adjusted). Threshold adjustments may depend on objects, object movement / position / orientation / action, object properties / STI / size / characteristics / traits / habits / behavior, location, in / at / of / in / location, features / fixtures / furniture / barriers / materials / machines / living things / objects / boundaries / surfaces / mediums, maps, map constraints (or environment models), events / states / scenes / conditions, time, timing, duration, current state, past history, users, and / or personal preferences, etc.

[0208] The stopping criterion (or criterion for skipping, bypassing, blocking, pausing, passing, or rejecting) of an iterative algorithm may be that the change in the current parameter (e.g., offset value) in the update during an iteration is less than a threshold. The threshold may be 0.5, 1, 1.5, 2, or another number. The threshold may be adaptive (and / or dynamically adjusted). It may change as the iteration progresses. With respect to the offset value, the adaptive threshold may be determined based on the task, the specific initial value, the current time offset value, the regression window, the regression analysis, the regression function, the regression error, the convexity of the regression function, and / or the number of iterations.

[0209] Local extrema can be determined as the corresponding extrema of the regression function within the regression window. Local extrema can be determined based on a set of time offset values ​​and an associated set of regression function values ​​within the regression window. Each of the associated set of regression function values ​​associated with the set of time offset values ​​may be within the range of the corresponding extrema of the regression function within the regression window.

[0210] Searching for local extrema involves robust search, minimization, optimization, statistical optimization, dual optimization, constraint optimization, convex optimization, global optimization, local optimization, energy minimization, linear regression, quadratic regression, higher-order regression, linear programming, nonlinear programming, stochastic programming, combinatorial optimization, constraint programming, constraint satisfaction, calculation of variations, optimal control, dynamic programming, mathematical programming, multi-objective optimization, multimodal optimization, disjunctive programming, spatial mapping, infinite-dimensional optimization, heuristics, metaheuristics, convex programming, semidefinite programming problems, cone programming, quadratic cone programming problems, integer programming, quadratic programming, fractional programming, numerical analysis, simplex algorithms, iterative methods, gradient descent, subgradient methods, coordinate gradient methods, conjugate gradient methods, Newton's algorithm, successive quadratic programming, interior point methods, elliptic methods, shrinking gradient methods, quasi-Newton's method, simultaneous perturbation stochastic approximation, interpolation methods, pattern search methods, line search, non-differential optimization, genetic algorithms, evolutionary algorithms, and dynamic relaxation. This may include hill climbing, particle swarm optimization, gravity search algorithms, simulated annealing, memetic algorithms, differential evolution, dynamic relaxation, stochastic tunneling, tab search, reaction search optimization, curve fitting, least squares, simulation-based optimization, variational calculation, and / or deformation. The search for local extrema may be related to the objective function, loss function, cost function, utility function, fitness function, energy function, and / or energy function.

[0211] Regression can be performed using a regression function to fit sampled data (e.g., CI, CI features, CI components) or another function (e.g., an autocorrelation function) within a regression window. The length and / or position of the regression window may change in at least one iteration. The regression function can be a linear function, a quadratic function, a cubic function, a polynomial function, and / or another function.

[0212] Regression analysis can minimize at least one of the following: error, aggregate error, component error, error in the projection region, error on a selected axis, error on a selected orthogonal axis, absolute error, squared error, absolute deviation, squared deviation, higher-order errors (e.g., cubic, quaternary), robust errors (e.g., squared error for absolute error for smaller errors and for larger errors, or first type of error for smaller errors and second type of error for larger errors), another error, a weighted sum (or weighted average) of absolute / squared errors (e.g., in the case of a radio transmitter with multiple antennas and a radio receiver with multiple antennas, each pair of transmitter and receiver antennas forms a link), mean absolute error, mean squared error, mean absolute deviation, and / or mean squared deviation. Errors associated with different links may have different weights. One possibility is that some links and / or some components with higher noise or lower signal quality metrics may have smaller or larger weights. (Weighted sum of squared errors, weighted sum of higher-order errors, weighted sum of robust errors, weighted sum of other errors, absolute cost, squared cost, higher-order cost, robust cost, other cost, weighted sum of absolute costs, weighted sum of squared costs, weighted sum of higher-order costs, weighted sum of robust costs, and / or weighted sum of other costs).

[0213] The regression errors being determined may be absolute errors, squared errors, higher-order errors, robust errors, yet another error, a weighted sum of absolute errors, a weighted sum of squared errors, a weighted sum of higher-order errors, a weighted sum of robust errors, and / or a weighted sum of yet another error.

[0214] The time offset associated with the maximum (or minimum) regression error of a regression function for a particular function within a regression window can be the updated current time offset in an iteration.

[0215] Local extrema can be found based on a quantity that includes the difference between two different errors (e.g., the difference between the absolute error and the squared error). Each of the two different errors may include the absolute error, the squared error, a higher-order error, a robust error, another error, a weighted sum of absolute errors, a weighted sum of squared errors, a weighted sum of higher-order errors, a weighted sum of robust errors, and / or a weighted sum of another error.

[0216] The quantity may be compared to reference data or reference distributions such as the F-distribution, the central F-distribution, another statistical distribution, thresholds, thresholds related to probability / histograms, thresholds related to probability / histograms for finding false peaks, thresholds related to the F-distribution, thresholds related to the central F-distribution, and / or thresholds related to another statistical distribution.

[0217] The regression window may be determined based on at least one of the following: the movement of an object (e.g., change of location / position), a quantity associated with the object, at least one characteristic of the object and / or STI related to the movement of the object, the estimated location of local extrema, noise characteristics, estimated noise characteristics, signal quality metric, F-distribution, central F-distribution, another statistical distribution, threshold, preset threshold, threshold related to probability / histogram, threshold related to desired probability, threshold related to the probability of finding a false peak, threshold related to F-distribution, threshold related to central F-distribution, threshold related to another statistical distribution, the condition that the quantity at the center of the window is maximum within the regression window, the condition that for the first time within the regression window there is only one of the local extrema of a particular function for a particular value, another regression window, and / or other conditions.

[0218] The width of the regression window can be determined based on the specific local extrema being searched for. Local extrema include the first local maximum, second local maximum, higher-order local maximum, first local maximum with a positive time offset, second local maximum with a positive time offset, higher-order local maximum with a positive time offset, first local maximum with a negative time offset, second local maximum with a negative time offset, second local maximum with a negative time offset, higher-order local maximum with a negative time offset, first local minimum, second local minimum, higher-order local minimum, first local minimum with a positive time offset, This may include a second local minimum with a positive time offset value, a higher-order local minimum with a positive time offset value, a first local minimum with a negative time offset value, a second local minimum with a negative time offset value, a higher-order local minimum with a negative time offset value, a first local extremum, a second local extremum, a higher-order local extremum, a first local extremum with a positive time offset value, a second local extremum with a positive time offset value, a first local extremum with a negative time offset value, a second local extremum with a negative time offset value, and a higher-order extremum with a negative and / or negative time offset value.

[0219] The current parameter (e.g., time offset value) may be initialized based on the target value, target profile, trend, historical trend, current trend, target velocity, velocity profile, target velocity profile, historical velocity trend, motion or movement of the object (e.g., change of location / position), at least one characteristic of the object and / or STI related to the object's motion, position quantity of the object, initial velocity of the object related to the object's motion, a predefined value, initial width of the regression window, duration, value based on the signal's carrier frequency, value based on the signal's subcarrier frequency, signal bandwidth, total antenna value related to the channel, noise characteristics, signal h metric, and / or adaptive (and / or dynamically adjusted) value. The current time offset may be at the center, left, right, and / or another fixed relative position of the regression window.

[0220] In a presentation, information may be displayed along with a map of the location (or an environmental model). Information includes location, zones, regions, areas, coverage regions, corrected location, approximate location, location on map of places (WRT), location on segmented locations, direction, route, route on map and / or segmentation, trace (e.g., location within a time window such as the last 5 seconds or the last 10 seconds; the time window duration can be adaptively (and / or dynamically) adjusted, and the time window duration can be adaptively (and / or dynamically) adjusted with respect to velocity and acceleration), route history, approximate regions / zones along the route, history / summary of past locations, history of past locations of interest, frequently visited areas, customer traffic, flock distribution, flock behavior, flock control information, velocity, acceleration, motion statistics, respiratory rate, heart rate, presence / absence of motion, presence or absence of people or pets or objects, presence or absence of vital signs, gesture control (control of devices using gestures), location-based gesture control, location-based operation information, and objects of interest (e.g., pets, people, self-guided machines / devices, vehicles, drones, cars, boats, rotating Identity (ID) or identifier of a vehicle (car, autonomous vehicle, fan-equipped machine, air conditioner, TV, machine with moving parts), user identification (e.g., person), user information, location / velocity / acceleration / direction / movement / gestures / gesture control / motion tracing, user ID or identifier, user activity, user state, user sleep / rest characteristics, user emotional state, user vital signs, environmental information of the location, weather information of the location, earthquake, explosion, storm, rain, fire, temperature, collision, impact, vibration, event, door opening event, door closing event, window opening Events may include window closing events, fall events, combustion events, freezing events, water-related events, wind-related events, air movement events, accident events, pseudo-periodic events (e.g., running on a treadmill, jumping, jump rope, somersaults, etc.), recurring events, crowding events, vehicle events, and user gestures (e.g., hand gestures, arm gestures, foot gestures, body gestures, head gestures, face gestures, mouth gestures, eye gestures, etc.).

[0221] The position can be two-dimensional (e.g., using two-dimensional coordinates) or three-dimensional (e.g., using three-dimensional coordinates). The position can be relative (e.g., with respect to a map or environmental model) or relational (e.g., the middle between point A and point B, around a corner, on the upper floor, on a table, on the ceiling, on the floor, on a sofa, close to point A, a distance R from point A, within a radius of R from point A, etc.). The position can be represented in Cartesian coordinates, polar coordinates, and / or another representation.

[0222] Information (e.g., a location) can be marked with at least one symbol. The symbol may change over time. The symbol may blink and / or pulsate, with or without changing color / intensity. The size can change over time. The orientation of the symbol can change over time. The symbol can be a number that reflects an instantaneous quantity (e.g., a user's vital signs / respiratory rate / heart rate / gesture / situation / state / movement / motion, temperature, network traffic, network connectivity, the state of a device / machine, the remaining power of a device, the state of a device). The rate of change, size, orientation, color, intensity, and / or symbol can each reflect the respective movement. The information can be presented visually and / or described verbally (e.g., using pre-recorded audio or speech synthesis). The information can be described in text. The information can also be presented in a mechanical way (e.g., an animated gadget, the movement of a movable part).

[0223] The user interface (UI) device can be a smartphone (e.g., an iPhone, an Android phone), a tablet (e.g., an iPad), a laptop (e.g., a notebook computer), a personal computer (PC), a device with a graphical user interface (GUI), a smart speaker, a device with audio / sound / speaker capabilities, a virtual reality (VR) device, an augmented reality (AR) device, a smart car, an in-vehicle display, a voice assistant, an in-vehicle voice assistant, etc.

[0224] Maps (or environmental models) may be two-dimensional, three-dimensional, and / or higher-dimensional (e.g., time-varying two-dimensional / three-dimensional maps / environmental models). Walls, windows, doors, entrances, exits, and restricted areas may be marked on the map or model. Maps may include floor plans of facilities. Maps or models may have one or more layers (overlays). Maps / models may be maintenance maps / models including water pipes, gas pipes, wiring, cable wiring, air ducts, crawl spaces, ceiling layouts, and / or basement layouts. Locations can be divided into multiple zones / areas / geographic areas / sectors / sections / territories / districts / administrative areas / sites / neighborhoods / areas / stretches / spacious areas, such as bedrooms, living rooms, storage rooms, walkways, kitchens, dining rooms, foyers, garages, ground floors, second floors, restrooms, offices, conference rooms, reception areas, various office areas, various warehouse areas, and various facility areas. Segments / areas / areas can be presented in the map / model. Different areas may be color-coded. Different areas may be presented with characteristics (e.g., color, brightness, color intensity, texture, animation, blinking, blinking speed, etc.). Logical segmentation of locations can be performed using at least one heterogeneous type 2 device or server (e.g., a hub device) or a cloud server.

[0225] Here is an example of the disclosed system, apparatus, and method. Stefan and his family want to install the disclosed wireless motion detection system to detect motion within their 2,000-square-foot two-story townhouse in Seattle, Washington. Since his house is two stories, Stefan decides to use one Type 2 device (named A) and two Type 1 devices (named B and C) on the first floor. The first floor centers around three rooms: a kitchen, a dining room, and a living room, with the dining room in the middle and arranged linearly. The kitchen and the living room are on opposite sides of the house. He places the Type 2 device (A) in the dining room, one Type 1 device (B) in the kitchen, and the other Type 1 device (C) in the living room. With the installation of this device, using the motion detection system, he specifically partitions the first floor into three zones: the dining room, the living room, and the kitchen. When motion is detected by the AB pair and the AC pair, the system analyzes the motion information and associates the motion with one of the three zones.

[0226] When Stefan and his family go out on weekends (for example, when they go camping on a long weekend), Stefan turns on a motion detection system using a mobile phone app (e.g., an Android phone app or an iPhone app). When the system detects motion, an alert signal is sent to Stefan (e.g., SMS text message, email, push message to the mobile phone app, etc.). If Stefan pays a monthly fee (e.g., $10 / month), the service company (e.g., a security company) receives the alert signal via a wired network (e.g., broadband) or wireless network (e.g., home WiFi, LTE, 3G, 2.5G, etc.) and takes security actions for Stefan (e.g., calling him to confirm the problem, sending someone to check the house, contacting the police on Stefan's behalf, etc.). Stefan loves his elderly mother very much and cares about her well-being when she is home alone. When his mother is home alone while the rest of the family is out (for example, going to work, shopping, or going on vacation), Stefan uses his mobile app to turn on a motion detection system to ensure she is okay. He then uses the mobile app to monitor his mother's movements around the house. When Stefan uses the mobile app to see his mother moving around the house between three areas, he knows she is okay, according to her daily routine. Stefan appreciates that the motion detection system can help him monitor his mother's well-being while he is away from home.

[0227] On a typical day, his mother wakes up around 7 a.m. She plans to spend about 20 minutes in the kitchen making breakfast. Then she eats breakfast in the dining room for about 30 minutes. After that, she sits on the sofa in the living room and does her daily exercises before watching her favorite TV shows. The motion detection system allows Stefan to see the timing of movements in each of the three areas of the house. When the movements match the daily routine, Stefan has a rough idea that his mother is doing well. However, if the movement pattern seems unusual (for example, no exercise until 10 a.m., staying in the kitchen for too long, or remaining motionless for too long), Stefan suspects something is wrong and calls his mother to check on her. Stefan may even ask someone else (for example, a family member, neighbor, paid staff member, friend, social worker, or service provider) to check on his mother.

[0228] Sometimes, Stefan feels like he's just relocating a Type 2 device. He simply unplugs the device from its original AC power outlet and plugs it into a different one. He's pleased that the wireless motion detection system is plug-and-play and that relocation doesn't affect the system's operation. Once powered on, it works immediately.

[0229] On another occasion, Stefan was convinced that our wireless motion detection system could actually detect movement with very high accuracy and very low alarms, and that he could actually monitor movement on the first floor using the mobile app. He decided to install a similar configuration on the second floor (i.e., one Type 2 device and two Type 1 devices) to monitor the bedrooms on the second floor. Again, he found the system setup to be extremely easy, as it only required plugging the Type 2 and Type 1 devices into the AC power outlets on the second floor. No special installation was needed. And he could monitor movement on both the first and second floors using the same mobile app. Each Type 2 device on the first / second floor could interact with all the Type 1 devices on both the first and second floors. Stefan was happy to see that as he doubled his investment in Type 1 and Type 2 devices, the combined system had more than twice the capacity.

[0230] According to various embodiments, each CI (CI) may include at least one of the following: channel state information (CSI), frequency domain CSI, frequency representation of CSI, frequency domain CSI associated with at least one subband, time domain CSI, intradomain CSI, channel response, channel response estimate, channel impulse response (CIR), channel frequency response (CFR), channel characteristics, channel filter response, CSI of a radio multipath channel, radio multipath channel information, timestamp, auxiliary information, data, metadata, user data, account data, access data, security data, session data, status data, management data, family data, identity (ID), identifier, device data, network data, neighbor data, environmental data, real-time data, sensor data, stored data, encrypted data, compressed data, protected data, and / or another CI. In one embodiment, the disclosed system has hardware components (e.g., a radio transmitter / receiver with an antenna, analog circuits, a power supply, a processor, memory) and corresponding software components. According to various embodiments of this disclosure, the disclosed system includes a Bot (referred to as a Type 1 device) and an Origin (referred to as a Type 2 device) for vital sign detection and monitoring. Each device comprises a transceiver, a processor, and memory.

[0231] The disclosed system can be applied in many cases. For example, a Type 1 device (transmitter) may be a small WiFi-enabled device placed on a table. It may also be a WiFi-enabled television (TV), set-top box (STB), smart speaker (e.g., Amazon Echo), smart refrigerator, smart microwave, mesh network router, mesh network satellite, smartphone, computer, tablet, smart plug, etc. For example, a Type 2 (receiver) may be a WiFi-enabled device placed on a table. It may also be a WiFi-enabled television (TV), set-top box (STB), smart speaker (e.g., Amazon Echo), smart refrigerator, smart microwave, mesh network router, mesh network satellite, smartphone, computer, tablet, smart plug, etc. Type 1 and Type 2 devices may be placed in / near a conference room to count people. Type 1 and Type 2 devices may be used in a health monitoring system for the elderly to monitor daily activities and any signs of symptoms (e.g., dementia, Alzheimer's disease). Type 1 and Type 2 devices may be used in an infant monitor to monitor the vital signs (respiration) of a living infant. Type 1 and Type 2 devices may be placed in bedrooms to monitor sleep quality and any sleep apnea. Type 1 and Type 2 devices may be placed in vehicles to monitor the health of passengers and drivers, detect driver sleep, and detect any babies left inside the vehicle. Type 1 and Type 2 devices may be used in logistics to prevent human trafficking by monitoring people hidden in trucks and containers. Type 1 and Type 2 devices may be deployed by emergency services in disaster areas to search for trapped victims in rubble. Type 1 and Type 2 devices may be placed in an area to detect the breathing of any intruder. There are numerous applications for wireless respiratory monitoring that is not wearable.

[0232] The hardware module may be configured to include a Type 1 transceiver and / or a Type 2 transceiver. The hardware module may be sold / used under a variable brand for designing, building, and selling the final commercial product. Products using the disclosed systems and / or methods may include home / office security products, sleep monitoring products, WiFi products, mesh products, TVs, STBs, entertainment systems, HiFi, speakers, home appliances, lamps, stoves, ovens, microwave ovens, tables, chairs, beds, shelves, tools, appliances, torches, vacuum cleaners, smoke detectors, sofas, pianos, fans, doors, windows, door / window handles, locks, smoke detection devices, car accessories, computing devices, office supplies, air conditioners, heaters, pipes, connectors, surveillance cameras, access points, computer equipment, mobile devices, LTE devices, 3G / 4G / 5G / 6G devices, UMTS devices, 3GPP devices, GSM devices, EDGE devices, TDMA devices, FDMA devices, CDMA devices, WCDMA devices, TD-SCDMA devices, game devices, glasses, glass panels, VR goggles, necklaces, watches, waistbands, belts, wallets, pens, hats, clothing, implantable devices, tags, parking tickets, smartphones, etc.

[0233] The summary may include: analysis, output response, selected time window, subsampling, transformation, and / or projection. The presentation may include presenting at least one of the following: monthly / weekly / daily views, simplified / detailed views, cross-sectional views, small / large form factor views, color-coded views, comparison views, summary views, videos, web views, audio announcements, and other presentations related to the periodic / repetitive characteristics of repeating motion.

[0234] Type 1 / Type 2 devices include antennas, devices with antennas, devices with enclosures (e.g., radios, antennas, data / signal processing units, wireless ICs, circuits), devices interfaced / attached / connected / linked to another device / system / computer / telephone / network / data aggregator, devices with user interfaces (UI) / graphical UI / displays, devices with wireless transceivers, devices with wireless transmitters, devices with wireless receivers, Internet of Things (IoT) devices, devices with wireless networks, devices with both wired and wireless network functions, devices with wireless integrated circuits (ICs), Wi-Fi devices, devices with Wi-Fi chips (e.g., compliant with 802.11a / b / g / n / ac / ax standards), Wi-Fi access points (APs), Wi-Fi clients, Wi-Fi routers, Wi-Fi repeaters, Wi-Fi hubs, Wi-Fi mesh network routers / hubs / APs, wireless mesh network routers, ad-hoc network devices, wireless mesh network devices, and mobile devices (e.g., 2G / 2.5G / 3G / 3G).5G / 4G / LTE / 5G / 6G / 7G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA), cellular devices, base stations, mobile network base stations, mobile network hubs, mobile network compatible devices, LTE devices, devices with LTE modules, mobile modules (e.g., circuit boards with mobile enable chips (ICs) such as Wi-Fi chips, LTE chips, BLE chips), devices with mobile modules, smartphones, companion devices for smartphones (e.g., dongles, attachments, plug-ins), dedicated devices, plug-in devices, AC-powered devices, battery-powered devices, devices with processor / memory / instruction sets, smart devices / gadgets / Items: may be clocks, stationery, pens, user interfaces, paper, mats, cameras, televisions (TVs), set-top boxes, microphones, speakers, refrigerators, ovens, machines, telephones, wallets, furniture, doors, windows, ceilings, floors, walls, tables, chairs, beds, nightstands, air conditioners, heaters, pipes, ducts, cables, carpets, decorations, gadgets, USB devices, plugs, dongles, lamps / lights, tiles, ornaments, bottles, vehicles, automobiles, AGVs, drones, robots, laptops, tablets, computers, hard drives, network cards, equipment, rackets, balls, shoes, wearable devices, clothing, eyeglasses, hats, necklaces, food, pills, small devices that move within the body of a living organism (e.g., blood vessels, lymphatic fluid, digestive system), and / or other devices. Type 1 devices and / or Type 2 devices may be communicatively coupled to the Internet, another device that accesses the Internet (e.g., a smartphone), a cloud server (e.g., a hub device), an edge server, a local server, and / or storage. Type 1 and / or Type 2 devices may operate under local control, be controlled by another device via a wired / wireless connection, operate automatically, or be controlled by a central system located remotely (e.g., away from home).

[0235] In one embodiment, a Type B device may be a transceiver that can function as both an origin (Type 2 device, Rx device) and a bot (Type 1 device, Tx device), i.e., a Type B device may be both a Type 1 (Tx) device and a Type 2 (Rx) device (e.g., simultaneously or alternately), and may be, for example, a mesh device, a mesh router, etc. In one embodiment, a Type A device may be a transceiver that can function only as a bot (Tx device), i.e., it may be only a Type 1 device or only a Tx, and may be, for example, a simple IoT device. This may have the functionality of an origin (Type 2 device, Rx device), but in embodiments it functions only as a bot in some way. All Type A and Type B devices form a tree structure. The root may be a Type B device that can access a network (e.g., the Internet). For example, this may be connected to a broadband service via a wired connection (e.g., Ethernet, cable modem, ADSL / HDSL modem) or a wireless connection (e.g., LTE, 3G / 4G / 5G, WiFi, Bluetooth, microwave link, satellite link, etc.). In one embodiment, all Type A devices are leaf nodes. Each Type B device may be a root node, a non-leaf node, or a leaf node.

[0236] Type 1 devices (transmitters, or Tx) and Type 2 devices (receivers, or Rx) may reside on the same device (e.g., an RF chip / IC), or simply on the same device. The devices can operate in high-frequency bands such as 28 GHz, 60 GHz, and 77 GHz. An RF chip may have dedicated Tx antennas (e.g., 32 antennas) and dedicated Rx antennas (e.g., another 32 antennas).

[0237] A single Tx antenna can transmit a radio signal (e.g., a series of probe signals, perhaps at 100 Hz). Alternatively, all Tx antennas can be used to transmit a radio signal using beamforming (in Tx), resulting in the radio signal being focused in a specific direction (e.g., for energy efficiency, to boost the signal-to-noise ratio in that direction, or for low-power operation when "scanning" in that direction, or when an object is known to be in that direction).

[0238] The radio signal encounters objects within a location (e.g., a room) (e.g., a living person lying on a bed 4 feet away from the Tx / Rx antenna, breathing, and heartbeat). Object movement (e.g., pulmonary movement according to respiratory rate, or vascular movement according to heartbeat) can affect / modulate the radio signal. All Rx antennas can be used to receive the radio signal.

[0239] Beamforming (in Rx and / or Tx) may be applied (digitally) to "scan" different directions. Many directions may be scanned or monitored simultaneously. Along with beamforming, "sectors" (e.g., direction, orientation, bearing, zone, region, segment) may be defined in relation to a Type 2 device (e.g., with respect to the center position of the antenna array). For each probe signal (e.g., pulse, ACK, control packet, etc.), channel information or CI (e.g., channel impulse response / CIR, CSI, CFR) is acquired / calculated for each sector (e.g., from an RF chip). In breath detection, CIR can be collected in a sliding window (e.g., 30 seconds, with a 100 Hz pulsating / probing speed, there may be 3000 CIRs over 30 seconds).

[0240] A CIR can have many taps (e.g., N1 components / tap). Each tap may be associated with a time lag or time-of-fright (e.g., the time it takes to hit a person 4 feet away and back). When breathing in a certain direction at a certain distance (e.g., 4 feet), one can look up the CIR in "a certain direction" and then look up the tap corresponding to "a certain distance". Then, from that tap in the CIR, the breathing rate and heart rate can be calculated.

[0241] Each tap within the sliding window (for example, the 30-second window in "Component Time Series") can be considered a time function (for example, "Tap Function," "Component Time Series"). When searching for strong periodic behavior (for example, perhaps corresponding to respiration in the range of 10 bpm to 40 bpm), each tap function can be examined.

[0242] Type 1 devices and / or Type 2 devices may have external connections / links and / or internal connections / links. External connections (e.g., connection 1110) may be associated with 2G / 2.5G / 3G / 3.5G / 4G / LTE / 5G / 6G / 7G / NBIoT, UWB, WiMAX, Zigbee, 802.16, etc. Internal connectivity (e.g., 1114A and 1114B, 1116, 1118, 1120) can be associated with WiFi, IEEE 802.11 standards, 802.11a / b / g / n / ac / ad / af / ag / ah / ai / aj / aq / ax / ay, Bluetooth 1.0 / 1.1 / 1.2 / 2.0 / 2.1 / 3.0 / 4.0 / 4.1 / 4.2 / 5, BLE, mesh networks, and IEEE 802.16 / 1 / 1a / 1b / 2 / 2a / a / b / c / d / e / f / g / h / i / j / k / l / m / n / o / p / standards.

[0243] Type 1 devices and / or Type 2 devices are powered by batteries (e.g., AA batteries, AAA batteries, coin cell batteries, button cell batteries, miniature batteries, battery banks, power banks, car batteries, hybrid batteries, vehicle batteries, container batteries, non-rechargeable batteries, rechargeable batteries, NiCd batteries, NiMH batteries, lithium-ion batteries, zinc-carbon batteries, zinc chloride batteries, lead-acid batteries, alkaline batteries, batteries with wireless chargers, smart batteries, solar batteries, boat batteries, plain batteries, other batteries, temporary energy storage devices, capacitors, flywheels).

[0244] Any device may be powered by DC or direct current (for example, from batteries, generators, power converters, solar panels, rectifiers, DC-DC converters as described above, with various voltages such as 1.2V, 1.5V, 3V, 5V, 6V, 9V, 12V, 24V, 40V, 42V, 48V, 110V, 220V, 380V, etc.), and therefore may have a DC connector or connector having at least one pin for DC power.

[0245] Any device may be powered by AC or alternating current (e.g., household wall outlets, transformers, inverters, Shore Power, with various voltages such as 100V, 110V, 120V, 100-127V, 200V, 220V, 230V, 240V, 220-240V, 100-240V, 250V, 380V, 50Hz, 60Hz, etc.) and therefore may have an AC connector or connector having at least one pin for AC power. Type 1 devices and / or Type 2 devices may be located in or out of place (e.g., installed, positioned, moved).

[0246] For example, in a vehicle (e.g., an automobile, truck, lorry, bus, special vehicle, tractor, excavator, digger, teleporter, bulldozer, crane, forklift, electric vehicle, AGV, emergency vehicle, cargo, freight car, trailer, container, boat, ferry, ship, submarine, aircraft, airship, lift, monorail, train, tram, railway vehicle, railcar, etc.), the Type 1 device and / or the Type 2 device may be an embedded device embedded in the vehicle, or an add-on device (e.g., an aftermarket device) plugged into a port inside the vehicle (e.g., an OBD port / socket, USB port / socket, accessory port / socket, 12V auxiliary power outlet, and / or 12V cigarette lighter port / socket).

[0247] For example, one device (e.g., the Type 2 device) may be plugged into a 12V cigarette lighter / accessory port or an OBD port or a USB port (e.g., of an automobile / truck / vehicle), and the other device (e.g., the Type 1 device) may also be plugged into a 12V cigarette lighter / accessory port or an OBD port or a USB port. The OBD port and / or the USB port can provide power, signaling, and / or network for the (automobile / truck / vehicle). The two devices can jointly monitor passengers including children / babies inside the vehicle. They may be used to count passengers, recognize the driver, and detect the presence of passengers at specific seats / places inside the vehicle.

[0248] In another embodiment, one device may be plugged into a 12V cigarette lighter / accessory port or an OBD port or a USB port of an automobile / truck / vehicle, while the other device may be plugged into a 12V cigarette lighter / accessory port or an OBD port or a USB port of another automobile / truck / vehicle.

[0249] In another example, many devices of the same Type A (e.g., Type 1 or Type 2) may exist in many different types of vehicles / portable devices / smart gadgets (e.g., automated guided vehicles / AGVs, shopping / luggage / mobile carts, parking tickets, golf carts, bicycles, smartphones, tablets, cameras, recording devices, smartwatches, roller skates, shoes, jackets, goggles, hats, eyewear, wearables, Segways, scooters, luggage tags, cleaning machines, vacuum cleaners, pet tags / colors / wearables / implants), each device may be plugged into a vehicle's 12V accessory port / OBD port / USB port or embedded in the vehicle. There may also be one or more other Type B devices (e.g., if A is Type 2, then B is Type 1, and if A is Type 1, then B is Type 2) installed in locations such as gas stations, streetlights, street corners, tunnels, multi-story parking lots, and scatter locations covering large areas such as factories / stadiums / stations / shopping malls / construction sites. Type A devices can be positioned, tracked, or monitored based on TSCI.

[0250] The area / location does not need to have local connectivity such as broadband service or Wi-Fi. Type 1 and / or Type 2 devices may be portable. Type 1 and / or Type 2 devices may support plug and play.

[0251] A pairwise radio link can be established between many pairs of devices, forming a tree structure. In each pair (and associated link), the device (second device) may be a non-leaf (Type B) device. The other device (first device) may be a leaf (Type A or Type B) or a non-leaf (Type B) device. In the link, the first device acts as a bot (Type 1 device or transmitting device) for transmitting radio signals (e.g., probe signals) to the second device via a radio multipath channel. The second device may act as an origin (Type 2 device or Rx device) for receiving radio signals, obtaining TSCI, and calculating "analysis about the link" based on the TSCI.

[0252] This disclosure discloses a motion tracking system for discovering the position, arrangement, or trajectory of a moving object without using satellites. In some embodiments, the disclosed system uses sensors, which may include an initial measuring unit (IMU), accelerometer, gyroscope, magnetometer, barometer, etc., combined with a mapping engine (e.g., a particle filter) to track motion. This can be easily implemented on existing smartphones (all with IMUs) or numerous tablets or notebook computers using simple software upgrades without requiring additional hardware infrastructure.

[0253] In some embodiments, the system uses data obtained from sensors to calculate the distance and direction of motion, and then uses a map engine to improve tracking accuracy or correct tracking error based on particle filters.

[0254] During the training phase, the user may be asked to walk a known distance (e.g., 10 m) so that the system can capture acceleration information. During walking, one stride consists of two steps: a left step with the left foot and a right step with the right foot. Thus, there should be two “oscillating” or “cycle” accelerations. In some embodiments, the system can analyze the user’s acceleration to recognize or identify a step or stride. The system can then calculate the stride length or step length as a known distance, step, and / or stride count.

[0255] During the operational phase, the system can analyze real-time acceleration to recognize / identify each stride or step within the measurement time window. The system can calculate distance by multiplying the known stride length (or step) by the number of strides within the measurement time window. Then, the system can apply dead reckoning to calculate a new position based on the old position, bearing from the gyro sensor, and distance.

[0256] Figure 1 shows the overall architecture of the tracking system 100, which tracks the movement of an object based on an inertial sensor (e.g., IMU) embedded in the moving device. In some embodiments, the system first calculates the distance and direction of movement from the IMU. It then combines this with an indoor map to fuse the two estimation types for continuous tracking.

[0257] Figure 1 provides an overview of the architecture of a tracking system according to several embodiments of the present disclosure. As shown in Figure 1, in exemplary use scenarios, a client, which can be a mobile body, wearable, robot, automated guided vehicle (AGV), or any other device equipped with an inertial unit (IMU), can move within a location, for example, by a moving user or a vehicle carrying the client. The client reads its built-in inertial unit to obtain measured values ​​or other sensing information. A core engine of the system running on the client infers the distance and orientation traveled from the measured values ​​and combines them to track the client's continuous location.

[0258] The right-hand portion of Figure 1 shows the system workflow on a mobile client. After the system performs distance and direction estimation, it merges the estimated travel distance and direction into a map-enhanced tracking module. In some embodiments, the system takes an indoor map as input and converts it into a weighted graph. The output graph is then fed into a new graph-based particle filter (GPF), which leverages the geometric constraints imposed by the map to jointly learn the precise 2D position and orientation as the target moves. The position estimation is then displayed to the user along with the map. Since the disclosed GPF uses only ordinary indoor maps (e.g., floor plan images), it can be easily scaled to large buildings at minimal cost.

[0259] Figure 2 shows the operation performed by a motion tracking system using an IMU according to some embodiments of the present disclosure. As shown in Figure 2, the system includes a sensor block 210, a sensor engine 220, a map engine 230, and a UI 240. The sensor block 210 may include an IMU subblock and a barometer (for altitude estimation). The IMU can measure one of the following: acceleration, Gyr (gyroscope reading), Mag (magnetometer reading), Grv (gravity reading), or Ori (orientation reading).

[0260] In some embodiments, several functions may exist between the IMU and the sensor engine 220. For example, `sensor_acquisition()` may be executed by the system to obtain sensor readings from the sensor engine 220, and `sensor_analysis()` may be executed by the system to analyze the sensor readings.

[0261] In some embodiments, the sensor engine 220 can execute several functions to estimate motion-related parameters. For example, the sensor engine 220 can execute the sensor filter() function to filter out noisy readings, and based on the sensor readings, it can execute the sensor angle(), sensor distance(), and sensor altitude() functions to estimate the distance traveled, the angle of traveled, and the altitude, respectively.

[0262] In some embodiments, the map engine 230 can correct arbitrary position errors based on particle filtering. For example, the map engine 230 can execute a map analysis() function to analyze map data uploaded to the cloud, a map graph() function to convert the map into a graph, and a map filter() function to build map-based graph particle filtering.

[0263] UI block 240 may be used to display the real-time position of a moving object being monitored.

[0264] Figure 3 shows a workflow for a sensor engine in a motion tracking system, such as the sensor engine 220 in Figure 2, according to some embodiments of the present disclosure. As shown in Figure 3, the sensor engine may acquire an acceleration time series from the IMU, then perform data preprocessing 310 and atomic event extraction 320, and convert the preprocessed acceleration into atomic events.

[0265] Figure 4 shows exemplary atomic events extracted by a sensor engine in a motion tracking system according to some embodiments of the present disclosure. As shown in Figure 4, the three basic atomic events can be defined as peak, trough, and zero crossing, respectively. The peak event 410 occurs at the maximum value of the processed acceleration data. The trough event 420 occurs at the minimum value of the processed acceleration data. The zero crossing event 430 occurs at the zero point where the processed acceleration data intersects, either from negative to positive or positive to negative.

[0266] In some embodiments, three extended atomic events are further defined by considering temporal constraints: far_peak, far_valley, and time_out. Figure 5 shows exemplary temporal constraints for extracted atomic events according to some embodiments of the present disclosure.

[0267] Referring back to Figure 3, the sensor engine can use a finite state machine (FSM) 330 to characterize acceleration transitions during the step cycle. While the normal human gait cycle varies with individual and velocity, it follows a typical template as seen from inertial data. The step cycle consists of two stages, namely the stance and the swing phase, which can be further broken down into seven stages. The stance phase begins with the first heel contact of one foot and ends when the toes of the same foot leave the ground. The swing phase is immediately followed by the forward swinging motion of the leg and continues until the next heel contact. Intuitively, the stride cycle consists of two steps, and the stride length is defined accordingly. In one embodiment, two consecutive steps are not distinguished, and therefore the step length is calculated as the stride length, which is generally half of the generally defined stride length. Ideally, during walking, the acceleration induced by the walking motion increases to a large value first, then decreases to a negative value, and finally returns to approximately zero.

[0268] Figure 6 illustrates a finite state machine (FSM) for step detection according to several embodiments of the present disclosure. As shown in Figure 6, the FSM includes five different states: S_ZC: Initial and default state when a zero crossing is detected. S_PK: State when acceleration is above the peak. S_P2V: State where a zero crossing occurs when acceleration decreases from the peak to the potential trough. S_VL: State in the acceleration trough. S_DT: State when a step is requested.

[0269] Six basic events can be defined to determine state transitions, all of which can be identified from inertial sensor data. E_PK: Peak detected. E_VL: Valley detected. E_ZC: Zero crossing observed. E_FPK: A distant peak is detected after an E_PK event with a large time difference exceeding a threshold, but without a preceding intermediate event. E_FVL: A valley defined similarly to E_FPK. E_TIMEOUT: A timeout event is triggered if the FSM remains in one state for too long.

[0270] The first three events characterize key characteristics of the acceleration pattern during walking, while the latter three are derived from the first three, coupled with temporal information to counteract noise and non-walking motion interference.

[0271] In some embodiments, by default, the system remains in its current state, either transitioning to another state or remaining unchanged until an event occurs. Each state transitions based on a specific event, as marked in Figure 6. All states except the default S_ZC are associated with a timeout event. The state S_DT returns to S_ZC when new data arrives. E_FPK and E_FVL are introduced to handle cases of two consecutive peaks or troughs caused by noisy sensor readings and user motion interference. For example, if the subsequent peak is too close to the previous one, the algorithm treats it as a walking strain and maintains the same state; otherwise, it is like a more random movement and the state is reset to S_ZC. The design of this algorithm achieves many strong aspects. For each detected step, the algorithm outputs timing information for the detected step: the time point in the corresponding S_ZC is the start time, while the time entering S_DT represents the end time. Figure 7 shows the steps on the processed acceleration data curve, including the start and end times of the steps based on step detection using FSM.

[0272] This algorithm is efficient and has only a few states. It decomposes relatively noisy sensor readings into several essential events that can be identified without relying on many subject-dependent parameters, such as not being highly dependent on absolute acceleration.

[0273] In some embodiments, raw sensor data is processed into a series of events of interest as input to the FSM described above. However, a key challenge here is that the ideal acceleration pattern of walking varies greatly depending on the walking pattern and device position (e.g., handheld, in pocket, or in a backpack). Moreover, the sensor data is noisy and can drift over time. A series of preprocessing steps can be performed to handle various sensor patterns. The accelerometer is processed along the x, y, and z axes for each sample, a=(a x ,ay , a z ) reports 3D sensor values as shown. The reported acceleration is in the device frame (not the Earth frame) and includes both the induced and gravitational components of motion. It may be necessary to compensate for gravity and transform the acceleration to the Earth's reference frame. Fortunately, modern IMUs have done an excellent job of extracting the gravitational component as a fused sensor (usually named a gravity sensor) based on accelerometers and gyroscopes or magnetometers that report the gravitational vector g = (g x , g y , g z ). Therefore, the magnitude of the projected acceleration without sensor orientation can be easily obtained as follows TIFF2026108641000001.tif1830. The time series of the acceleration magnitude is given as A = [a(t1), a(t2), ···, a(t M )], where a(t i ) is the reading at time t i . By removing the moving average trend, the drift of the gravity and potential sensors can be further removed. Since there is no need to process the data online for stride length estimation, a relatively long window of 2 seconds can be used to calculate the moving average. Then, a window of 0.25 seconds can be used to further smooth the trend-removed data.

[0274] Next, zero-crossing and peak detection can be performed to identify all events of interest from the data series (valley detection is performed in the same way as peak detection by multiplying the data by -1). As a result of this process, at time t i , E = [e(t1), e(t2), ··· e(t Q ), where e(t iA time series of events occurs, denoted as )∈{E_PK,E_VL,E_ZC}. The events are sparse over time, as there are typically three E_ZC, one E_PK, and one E_VL within a standard step. This event series is then fed into the FSM for step detection. The other three events, namely E_FPK, E_FVL, and E_TIMEOUT, are detected within the FSM by examining the timestamps of two consecutive E_PK and E_VL and the duration of the state itself, respectively. For example, E_FPK is e(t i-1 ) = e(t i )=E_PK and |t i -t i-1 |>th max_gap , occurs in the case of, and here th max_gap This indicates a threshold that can be determined by human walking behavior. By including events (i.e., specific relative patterns in a sequence of accelerations) rather than absolute acceleration thresholds, the disclosed FSM is more generalized and robust to different walking patterns and sensor positions.

[0275] Referring back to Figure 3, the sensor engine further performs step verification 340 after step detection using FSM. In some embodiments, additional constraints are applied to verify the true step. For example, from step detection, the system determines the step start time t s , end time t e The system can have a time series of acceleration data A, and corresponding peak and trough information. The system can have step energy, TIFF2026108641000002.tif2059 Step residuals, This can be calculated as TIFF2026108641000003.tif2055. The verification result may also be shown as the step likelihood l with k=0 for the false step. The step length Δd = Δd0 * l, where Δd0 is the default value.

[0276] After step verification 340, the system can generate step detection results, step likelihood, and step length. In some embodiments, gait detection may be performed, for example, after step verification. In some embodiments, the user status is detected as gait if continuous steps are observed. Otherwise, the user status is switched to non-gait. Steps are considered continuous if the time of one step and the time of the previous step are within the maximum rest gap. The gait status may be enabled if there are multiple consecutive steps. When the gait status is deactivated, the number of consecutive steps is reset to 0. The gait status is enabled when the first step is detected.

[0277] As shown in Figure 3, the sensor engine performs further parameter adaptation 350 after step verification. The parameters used for step count and step length estimation may be dynamically adapted and personalized for individual users. As more data is accumulated for users, the model becomes more accurate and reliable. In some embodiments, the parameters are fitted online using real-time streaming data. In some embodiments, n is expressed as the number of observed steps, and the average step time is It may be updated based on TIFF2026108641000004.tif11128. The mean values ​​of peak height, valley height, step energy, and step residual can also be updated in the same way. The variance of step time is The parameters can be updated based on TIFF2026108641000005.tif15138. The variance values ​​for peak_height, valley_height, step_energy, and step_residual can also be updated similarly. To adapt to the latest user behavior, the algorithm only needs to consider the most recent N steps, i.e., n=1 when n>N. To further track the latest object movement, new atomic events can be extracted based on the updated parameters.

[0278] In some embodiments, the system can estimate the direction of motion based on a given initial direction θ(0) and calculate the change in direction from the gyroscope data. In some embodiments, first the gyroscope readings are projected onto an Earth reference frame, and then only the change in orientation in the XY plane is considered. Thus, the direction at time t is, It is estimated to be TIFF2026108641000006.tif2071. Errors accumulate over time but are later mitigated in map engine 230.

[0279] In some embodiments, the system can also perform altitude estimation. It can detect changes in altitude and infer changes in floor levels. In some embodiments, the system can estimate altitude information from barometer data. A barometer is a scientific instrument used to measure atmospheric pressure in a given environment. For example, at low altitudes below sea level, atmospheric pressure decreases by approximately 1.2 kPa for every 100 meters. Barometers are available as low-cost sensors in modern smartphones. While barometers may not be suitable for estimating absolute altitude, they are sufficient for detecting changes in height, such as estimating floor transitions of moving objects.

[0280] Referring back to Figure 2, the map engine 230 can compare the distance traveled within a certain window estimated by the map engine 230 with the distance traveled estimated by the raw output of the sensor engine 220. If the two distances are too different, the map engine 230 may get stuck at a certain point. When this condition is detected, the map engine 230 can iteratively increase the search area for potential particles with each movement step until the search area reaches a predetermined maximum value. In doing so, the map engine 230 is more likely to survive under various scenarios.

[0281] Figure 8 shows a flowchart of an exemplary method 800 for sensor-based motion tracking according to several embodiments of the present disclosure. In various embodiments, method 800 may be performed by the system disclosed above. In operation 802, at least one sensing information (S1) is generated, for example, by a sensor. In operation 804, at least one time-series SI (TSSI) is obtained, for example, from a sensor and analyzed by a processor. This processor may be communicative and / or physically coupled to the sensor. In operation 806, the movement of an object in a location is tracked based on at least one TSSI. In some embodiments, the sensor may be physically coupled to the object. In operation 808, an incremental distance related to the movement of the object in a first incremental period is calculated based on at least one TSSI. In operation 810, the orientation related to the movement of the object in a second incremental period is calculated based on at least one TSSI. In operation 812, the next position of the object in the next time is calculated based on the current position of the object in the current time, the current orientation of the object in the current time, the incremental distance, orientation, and location map. The sequence of operations in Figure 8 can be modified according to various embodiments of this disclosure.

[0282] The following numbered sections provide examples for motion tracking.

[0283] Item 1. A tracking system / method / device / software comprising: at least one sensor capable of generating at least one sensing information (S1); a memory and a processor communicatively coupled to the at least one sensor; a memory; and a set of instructions stored in the memory, which, when executed by the processor, causes the processor to: acquire at least one time-series SI (TSSI) from the at least one sensor; analyze at least one TSSI; track the movement of an object in a place based on at least one TSSI; calculate an incremental distance related to the movement of the object in an incremental period and a direction related to the movement of the object in another incremental period based on at least one TSSI; and calculate the next position of the object in the next time based on at least one of the current position of the object in the current time, the current orientation of the object in the current time, the incremental distance, the orientation, or a map of the place.

[0284] In some embodiments, the object may be a human being or a smart device having sensors (e.g., an IMU). The object's movement may be a human walking or running. The sensors may be an initial measuring unit (IMU), an accelerometer, a gyroscope, a magnetometer, or a barometer. An accelerometer may provide acceleration that can be analyzed to generate a “step” or “stride.” A gyroscope may provide orientation / direction. A magnetometer may be used to improve the accuracy of direction. A barometer can be used to determine whether the user is moving up or down stairs.

[0285] Item 2. A tracking system / method / device / software as described in Item 1, wherein at least one sensor comprises at least one of an inertial measuring unit (IMU), accelerometer, gyroscope, magnetometer, or barometer, and at least one SI comprises at least one of force, acceleration, accelerating force, acceleration in a momentary rest frame, direction, angle, angular velocity, angle, magnetic field direction, magnetic field strength, magnetic field change, or air pressure.

[0286] Section 3. A tracking system / method / device / software according to Section 1, wherein the direction of motion related to the motion of an object in another incremental period is calculated based on at least one of the following: SI from a gyroscope, SI from a magnetometer, at least one direction in another incremental period, a direction prior to another incremental period, a direction after another incremental period, a direction related to the start of another incremental period, a direction related to the end of another incremental period, the direction of motion at the current time, the direction of motion at the next time, the direction of motion at another time, a weighted average of two or more directions related to another incremental period, or the median of two or more directions related to another incremental period.

[0287] Section 4. A tracking system / method / device / software as described in Section 1, further comprising a set of instructions causing a processor to determine a basic rhythmic motion related to the motion of an object; identify at least one rhythmic motion of a TSSI related to the basic rhythmic motion; track the motion of an object based on the rhythmic motion of the TSSI; and calculate an incremental distance based on the rhythmic motion of the TSSI.

[0288] Section 5. A tracking system / method / device / software as described in Section 4, further comprising: calculating a time series of intermediate quantities (IQ) based on at least one TSSI; identifying another rhythmic movement of a time series of IQ (TSIQ) related to a basic rhythmic movement; tracking the movement of an object based on another rhythmic movement of the TSIQ; and calculating an incremental distance based on another rhythmic movement of the TSIQ.

[0289] Section 6. A tracking system / method / device / software as described in Section 5, further comprising a set of instructions to cause a processor to identify a time window of at least one TSSI or TSIQ stable rhythmic motion related to a stable basic rhythmic motion related to the motion of an object, and to cause a timestamp to be added to the time window if at least one of the following is satisfied: the weighted mean of IQ in a sliding window around the timestamp is greater than a first threshold, the feature of the autocorrelation function of IQ around the timestamp is greater than a second threshold, and another criterion associated with the timestamp.

[0290] Section 7. A tracking system / method / device / software as described in Section 5, further comprising a set of instructions causing a processor to identify at least one local characteristic point of IQ within a time window, wherein the local characteristic point includes at least one of a maximum, a minimum, a zero crossing of IQ from positive to negative, or a zero crossing of IQ from negative to positive; to divide the time window into at least one segment based on a timestamp associated with at least one local characteristic point, wherein each segment spans from one local characteristic point to another; and to identify at least one of a full cycle, a rhythmic cycle, a compound cycle, an extended cycle, a double cycle, a quad cycle, a partial cycle, a half cycle, a quarter cycle, a gait cycle, a stride cycle, a step cycle, or a hand cycle.

[0291] Section 8. A tracking system / method / device / software as described in Section 5, comprising: a processor calculating a time series of mean subtracted IQ (MSIQ) by subtracting a time series of local mean values ​​from TSIQ, the local mean being obtained by applying a moving average filter to TSIQ; dividing the time series of MSIQ within a time window (TSMSIQ) into at least one preliminary segment containing MSIQ having the same polarity; and identifying at least one local characteristic point of the MSIQ in each preliminary segment, the local characteristic point being a maximum, minimum, reflection point, positive to negative The set of instructions further includes identifying an MSIQ zero crossing, an MSIQ zero crossing from negative to positive, the centroid, median, first quartile, third quartile, percentile, or mode of a preliminary segment; dividing the TSMSIQ into at least one segment based on at least local characteristic points; and identifying at least one of a full cycle, rhythm cycle, compound cycle, extended cycle, double cycle, quad cycle, partial cycle, half cycle, quarter cycle, gait cycle, stride cycle, step cycle, or hand cycle.

[0292] Section 9. A tracking system / method / device / software as described in Section 7 or 8, wherein the processor provides a first period from a first maximum value of TSIQ (or TSMSIQ or TSSI) to a second maximum value of TSIQ (or TSMSIQ or TSSI), a second period from a first maximum value of TSIQ (or TSMSIQ or TSSI) to a local minimum value of TSIQ (or TSMSIQ or TSSI), a second period from a first maximum value of TSIQ (or TSMSIQ or TSSI) to a zero crossing of TSIQ (or TSMSIQ or TSSI), and TSIQ (or TSMSIQ or TSSI) The third period up to the second local maximum of TSSI, the fourth period from the first local minimum of TSIQ (or TSMSIQ or TSSI) to the second local minimum of TSIQ (or TSMSIQ or TSSI), the fifth period from the first local minimum of TSIQ (or TSMSIQ or TSSI) to the local maximum of TSIQ (or TSMSIQ or TSSI), the sixth period from the first local minimum of TSIQ (or TSMSIQ or TSSI) to the second local minimum of TSIQ (or TSMSIQ or TSSI), the zero crossing of TSIQ (or TSMSIQ or TSSI), the sixth period up to the second local minimum of TSIQ (or TSMSIQ or TSSI), TSIQ The system further includes a set of instructions that cause a complete cycle of TSIQ (or TSMSIQ or TSSI) to be identified as at least one of the following: a seventh period from the first zero crossing of TSIQ (or TSMSIQ or TSSII) to a local maximum of TSIQ (or TSMSIQ or TSSII), a second zero crossing of TSIQ (or TSMSIQ or TSSII), a local minimum of TSIQ (or TSMSIQ or TSSII), a third zero crossing of TSIQ (or TSMSIQ or TSSII), an eighth period from the first zero crossing to a local minimum, a second zero crossing, a local maximum, a third zero crossing, a ninth period from the first negative-to-positive zero crossing to a positive-to-negative zero crossing, a second negative-to-positive zero crossing, a tenth period from the first positive-to-negative zero crossing to a negative-to-positive zero crossing, a second positive-to-negative zero crossing, or a cycle of oscillating motion.

[0293] Section 10. A tracking system / method / device / software as described in Section 9, further comprising a set of instructions that causes a processor to recognize a complete cycle based on a finite state machine (FSM) having local characteristic points as inputs for state transitions.

[0294] Item 11. A tracking system / method / device / software as described in Item 9, further comprising a set of instructions causing a processor to perform at least one of the following: identifying a rhythmic cycle as a complete cycle; identifying a compound cycle as two or more complete cycles; identifying an extended cycle as two or more complete cycles; identifying a double cycle as two consecutive cycles; identifying a quad cycle as four consecutive cycles; identifying a partial cycle as identifying at least two local characteristic points that match at least one of the first period, the second period, the third period, the fourth period, the fifth period, the sixth period, the seventh period, the eighth period, the ninth period, the tenth period, or the cycle of the oscillating motion; identifying a step cycle as a complete cycle; identifying a stride cycle as a double cycle; identifying a gait cycle as either a step cycle or a stride cycle; recognizing a hand cycle as either a complete cycle or a double cycle. Item 12. A tracking system / method / device / software as described in Item 7 or 8, further comprising a set of instructions causing a processor to compute at least one cycle feature based on at least one of the following: IQ within a time window, at least one local characteristic point of IQ, at least one segment, or at least one cycle.

[0295] Section 13. A tracking system / method / device / software as described in Section 12, wherein the at least one cycle feature includes at least one of the following gait-velocity related features, the gait-velocity related feature includes: at least one gait velocity associated with each timestamp in a time window, the gait velocity being preprocessed; at least one average velocity associated with each timestamp in a time window, which is at least one of the average, weighted average, and trimmed average of the gait velocity in a subwindow around the timestamp; at least one maximum velocity associated with each timestamp in a time window, which is the maximum of the gait velocity in a subwindow around the timestamp; at least one minimum velocity associated with each timestamp in a time window, which is the minimum of the gait velocity in a subwindow around the timestamp; at least one velocity variance, associated with each timestamp in a time window, which is the variance of the gait velocity in a subwindow around the timestamp; and each associated with each timestamp in a time window. The X percentile of the sample distribution of mean subtracted velocity in the subwindow of the time window around the timestamp, where mean subtracted velocity is the average velocity minus the gait velocity, and X is a number between 0 and 100, and at least one positive maximum velocity deviation, each associated with the timestamp and being the maximum value of mean subtracted velocity in the subwindow of the time window around the timestamp, and at least one negative maximum velocity deviation, each associated with the timestamp and being the maximum negative value of mean subtracted velocity in the subwindow of the time window around the timestamp, and at least one velocity peak variance, each associated with the timestamp and being the variance of at least one local peak (maximum) of gait velocity in the subwindow of the time window around the timestamp, and at least one velocity trough variance, each associated with the timestamp and being the variance of at least one local trough (minimum) of gait velocity in the subwindow of the time window around the timestamp, and each associated with the timestamp,The k-th local peak of the autocorrelation function (ACF) of gait velocity at the timestamp, where k is a positive integer, is at least one k-th peak of velocity ACF, and each of the k-th peaks of average velocity ACF is associated with the timestamp and is at least one of the mean, weighted mean, and trimmed mean of the k-th peak of velocity ACF in a subwindow of the time window around the timestamp, and each of the k-th peaks of velocity ACF is associated with the timestamp and is at least one of the variances of the k-th peak of velocity ACF in a subwindow of the time window around the timestamp, is at least one k-th peak variance of velocity ACF, and each of the k-th local peaks of gait velocity at the timestamp and is associated with the timestamp and is the difference between the k-th local peak of gait velocity and the (k-1)-th local peak of ACF, where k is a positive integer, is at least one k-th peak difference of velocity ACF, and each of the k-th peak differences of velocity ACF in a subwindow of the time window around the timestamp, is at least one The k-th peak-difference and, each associated with a timestamp, the variance of the k-th peak-difference of the velocity ACF in a sub-window of the time window around the timestamp, at least one velocity ACF peak-difference variance, each associated with a timestamp, the count of significant velocity ACF peaks at the timestamp, at least one velocity ACF peak count, each associated with a timestamp, the mean, weighted mean, and trimmed mean of the velocity ACF peak counts in a sub-window of the time window around the timestamp, at least one velocity mean k-th peak-difference variance, each associated with a timestamp, the variance of the velocity ACF peak count in a sub-window of the time window around the timestamp, and each associated with a timestamp,At least one velocity ACF peak-count-pdf is associated with a timestamp and at least one recurrent plot (RP) At least one recurrent plot (RP) feature comprising a diagonal line, and at least one other RP feature, and at least one scaled velocity-peak ACF, each of which is a velocity ACF associated with a timestamp of a local peak (maximum) of gait velocity in a subwindow of the time window, wherein the velocity ACF is scaled such that its first peak occurs at a selected time lag, and at least one scaled velocity-peak ACF feature comprises at least one scaled The velocity-peak ACF features, where the subwindow is at least one of the following: the entire time window, the sliding window, at least one gait cycle, at least one step segment, and at least one other subwindow; at least one scaled peak ACF feature; and at least one harmonic ratio, each associated with a gait cycle, which is the ratio of the sum of even harmonic amplitudes of the Fourier transform of the gait velocity in the gait cycle to the sum of odd harmonic amplitudes of the Fourier transform of the gait velocity in the gait cycle; and at least one harmonic ratio, each associated with a gait cycle,The features include at least one harmonic feature calculated based on a function of the even terms of the frequency transform of gait velocity in the gait cycle and a function of the odd terms of the frequency transform, at least one generalized harmonic feature each associated with the gait cycle and calculated based on a first function of the even terms of the gait feature transform in the gait cycle and a second function of the odd terms of the gait feature transform, at least one function of the above features, and a function of another feature.

[0296] Section 14. A tracking system / method / device / software as described in Section 12, wherein the at least one cycle feature includes at least one of the following gait-acceleration related features, each associated with a timestamp in a time window, and each gait acceleration is the derivative of the gait velocity, each associated with a timestamp in a time window, at least one average acceleration is the mean, weighted mean, and trimmed mean of the gait acceleration in a subwindow of the time window around the timestamp, each associated with a timestamp in a time window, at least one maximum acceleration is the maximum value of the gait acceleration in a subwindow of the time window around the timestamp, each associated with a timestamp in a time window, and each is the minimum value of the gait acceleration in a subwindow of the time window around the timestamp. At least one minimum acceleration, each associated with a timestamp in the time window and being the variance of gait acceleration in a sub-window of the time window around the timestamp; at least one acceleration variance, each associated with a timestamp in the time window and being the X percentile of the sample distribution of mean subtracted acceleration in a sub-window of the time window around the timestamp, where mean subtracted acceleration is gait acceleration minus mean acceleration, and X is a number between 0 and 100; at least one acceleration deviation, each associated with a timestamp and being the maximum value of mean subtracted acceleration in a sub-window of the time window around the timestamp; at least one maximum acceleration deviation, each associated with a timestamp and being the maximum negative value of mean subtracted acceleration in a sub-window of the time window around the timestamp; at least one acceleration peak variance, each associated with a timestamp, being the variance of at least one local peak (maximum) of gait acceleration in a sub-window of the time window around the timestamp;At least one acceleration trough variance, each associated with a timestamp, and at the timestamp, the k-th local peak of the autocorrelation function (ACF) of gait acceleration, where k is a positive integer; at least one acceleration ACF k-th peak, each associated with a timestamp, and at least one of the mean, weighted mean, and trimmed mean of the acceleration ACF k-th peak, each associated with a timestamp; at least one acceleration ACF k-th peak, each associated with a timestamp, and the variance of the acceleration ACF k-th peak, each associated with a timestamp, and at least one acceleration ACF k-th peak variance, each associated with a timestamp, and at the timestamp, the difference between the k-th local peak and the (k-1)-th local peak of the ACF of gait acceleration, where k is a positive integer; at least one acceleration ACF k-th peak difference, each associated with a timestamp, and at the timestamp, the acceleration ACF At least one average acceleration ACF peak-difference, which is at least one of the mean, weighted mean, and trimmed mean of the k-th peak-difference; at least one acceleration ACF peak-difference variance, which is the variance of the acceleration ACF peak-difference in a sub-window of the time window around the timestamp, which is at least one acceleration ACF peak count, which is at least one of the mean, weighted mean, and trimmed mean of the acceleration ACF peak counts in a sub-window of the time window around the timestamp, which is at least one average acceleration ACF peak-difference, which is at least one of the mean, weighted mean, and trimmed mean of the acceleration ACF peak counts in a sub-window of the time window around the timestamp, which is at least one acceleration ACF peak count, which is at least oneAt least one recurrent plot (RP) is a two-dimensional plot of R(i,j), each associated with a timestamp, where i and j are both running time indices in the subwindows of the time window around the timestamp, and R(i,j) is a scalar similarity function between a first feature vector at time i and a second feature vector at time j, where each of the first and second feature vectors is a feature vector at time t, each associated with a timestamp, where k is a non-negative integer, and the recurrence rate, determinism, entropy, mean At least one recurrent plot (RP) feature, each comprising a diagonal and at least one of other RP features, is an acceleration ACF associated with a timestamp of a local peak (maximum) of gait acceleration in a subwindow of the time window, the acceleration ACF being scaled such that its first peak occurs at a selected time lag, at least one scaled acceleration peak ACF feature, each of which is a scaled acceleration peak ACF feature, where the subwindow is at least one of the entire time window, a sliding window, at least one gait cycle, at least one step segment, and at least one other subwindow, at least one scaled peak ACF feature, each associated with a gait cycle, is the ratio of the sum of even harmonics to the sum of odd harmonics of the Fourier transform of gait acceleration in the gait cycle.At least one harmonic ratio, each associated with a gait cycle and calculated based on a function of the even terms and odd terms of the frequency transform of gait acceleration in the gait cycle; at least one generalized harmonic feature, each associated with a gait cycle and calculated based on a first function of the even terms and second function of the odd terms of the gait feature transform in the gait cycle; a function of at least one of the above features; and a function of another feature.

[0297] Section 15. A tracking system / method / device / software as described in Section 12, wherein at least one gait feature comprises at least one of the following step-related features, where each step-related feature is: at least one gait velocity, each associated with a timestamp in a time window; at least one step length, each associated with a timestamp in a time window and being the integral of the gait velocity over each step segment around the timestamp; at least one step period, each associated with a timestamp in a time window and being the duration of each step segment around the timestamp; at least one step frequency, each associated with a timestamp in a time window and being inversely proportional to the duration of each step around the timestamp; at least one step-wise average velocity, each associated with a timestamp in a time window and being at least one of the average, weighted average, and trimmed average of the gait velocity in each step segment around the timestamp; at least one step-wise maximum velocity, each associated with a timestamp in a time window and being the maximum gait velocity in each step segment around the timestamp; and at least one step-wise maximum velocity, each associated with a timestamp in a time window and being the minimum gait velocity in each step segment around the timestamp. At least one stepwise minimum velocity, each associated with a timestamp within a time window and being the variance of the gait velocity within each step segment around the timestamp; at least one stepwise velocity variance, each associated with a timestamp within a time window and being the X percentile of the sample distribution of the mean subtracted velocity in each step segment around the timestamp, where the mean subtracted velocity is the gait velocity minus the stepwise average velocity, and X is a number between 0 and 100; at least one stepwise velocity deviation, each associated with a timestamp within a time window and being the mean of the gait acceleration in each step segment around the timestamp;At least one of the weighted mean and trimmed mean, where the gait acceleration is the derivative of the gait velocity; at least one stepwise mean acceleration, each associated with a timestamp in the time window and the maximum gait acceleration in each step segment around the timestamp; at least one stepwise maximum acceleration, each associated with a timestamp in the time window and the minimum gait acceleration in each step segment around the timestamp; and at least one stepwise minimum acceleration, each associated with a timestamp in the time window and the variance of the gait accelerations in each step segment around the timestamp. At least one stepwise acceleration variance, each associated with a timestamp within a time window, and the X percentile of the sample distribution of mean subtracted acceleration in each step segment around the timestamp, where mean subtracted acceleration is gait acceleration minus stepwise mean acceleration, and X is a number between 0 and 100, at least one stepwise acceleration deviation, each associated with a timestamp within a time window, and step length, step duration, step frequency, stepwise mean velocity, stepwise maximum velocity, stepwise minimum velocity, stepwise velocity variance, stepwise velocity deviation, stepwise mean acceleration, stepwise maximum acceleration, stepwise minimum acceleration, stepwise acceleration variance value, stepwise acceleration deviation, stepwise velocity peak variance value, stepwise velocity trough variance value, stepwise velocity ACF k-th peak, stepwise mean velocity ACF k-th peak, stepwise velocity ACF k-th peak variance value, stepwise velocity ACF k-th peak difference, stepwise mean velocity ACF k-th peak difference, stepwise velocity ACF k-th peak-difference variance, stepwise velocity ACF peak count, stepwise mean velocity ACF k-th peak-difference, stepwise velocity ACF k-th peak-difference variance, stepwise velocity ACF peak-count-pdf, stepwise recurrent plot (RP),Stepwise recurrent plot (RP) features, stepwise scaled velocity-peak ACF, stepwise scaled peak ACF features, stepwise harmonic ratio, stepwise harmonic features, stepwise generalized harmonic features, stepwise symmetry measure, at least one step statistic which is a function of at least one of the above statistics, a function of another statistic, and a function of another step statistic, at least one mean step statistic which is associated with a timestamp in a time window and is at least one of the mean, weighted mean, and trimmed mean of step statistics in a subwindow of the time window around the timestamp, at least one mean step statistic which is associated with a timestamp in a time window and is at least one of the mean, weighted mean, and trimmed mean of step statistics in a subwindow of the time window around the timestamp At least one odd mean step statistic is at least one of the mean, weighted mean, and trim mean of the step statistics of a step segment, each associated with a timestamp in the time window, and at least one even mean step statistic is at least one of the mean, weighted mean, and trim mean of the step statistics of an even step segment in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one maximum step statistic is the maximum of the step statistics in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one odd maximum step statistic is the maximum of the step statistics of an odd step segment in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one even maximum step statistic is the maximum of the step statistics of an even step segment in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one minimum step statistic is the minimum of the step statistics in a subwindow of the time window around the timestamp,At least one odd minimum step statistic, each associated with a timestamp in the time window and being the minimum step statistic of odd step segments in the subwindow of the time window around the timestamp; at least one even minimum step statistic, each associated with a timestamp in the time window and being the minimum step statistic of even step segments in the subwindow of the time window around the timestamp; at least one step statistical variance, each associated with a timestamp in the time window and being the variance of step statistics in the subwindow of the time window around the timestamp; at least one odd step statistical variance, each associated with a timestamp in the time window and being the variance of step statistics in odd step segments in the subwindow of the time window around the timestamp; at least one even step statistical variance, each associated with a timestamp in the time window and being the variance of step statistics in even step segments in the subwindow of the time window around the timestamp; Each is associated with a timestamp within a time window and is the X percentile of the sample distribution of the mean subtracted step statistics in the subwindows of the time window around the timestamp, where the mean subtracted step statistics for odd step segments are obtained by subtracting the odd mean step statistics from the step statistics of the odd step segments, where X is at least one step statistical deviation that is a number between 0 and 100, each is associated with a timestamp within a time window and is the X percentile of the sample distribution of the mean subtracted step statistics for odd step segments in the subwindows of the time window around the timestamp, where the mean subtracted step statistics for odd step segments are obtained by subtracting the odd mean step statistics from the step statistics of the odd step segments, where X is at least one odd step statistical deviation that is a number between 0 and 100, each is associated with a timestamp within a time window,The X percentile of the sample distribution of the mean subtracted step statistics for even step segments in a subwindow of the time window around a timestamp, where the mean subtracted step statistics for an even step segment is the step statistics of the even step segment minus the even mean step statistics, where X is a number between 0 and 100. At least one even step statistics deviation, each associated with a timestamp in the time window, which is at least one of the mean step statistics, maximum step statistics, minimum step statistics, step statistics variance, step statistics deviation and another statistic of step statistics. At least one odd statistics of step statistics, each associated with a timestamp in the time window, which is at least one of the odd mean step statistics, odd maximum step statistics, odd minimum step statistics, odd step statistics variance, odd step statistics deviation and another odd statistic of step statistics. Associated with the even mean step statistic, even maximum step statistic, even minimum step statistic, even step statistic variance, even step statistic deviation, and at least one of the other even statistics of the step statistic, at least one even statistic, left step statistic, right step statistic, front left step statistic, front right step statistic, rear left step statistic, rear right step statistic, wavefront step statistic, odd wavefront step statistic, even wavefront step statistic, even statistics of the step statistic and step The ratio of odd statistics to step statistics, the difference between even statistics and odd statistics to step statistics, the similarity between even statistics and odd statistics to step statistics, and at least one function of the step statistics, odd statistics to step statistics, and even statistics to step statistics, the ratio of the function of even statistics to the function of odd statistics to step statistics, the first function of the second function of even statistics to step statistics to the third function of odd statistics to step statistics, and another statistic of step statistics.

[0298] Section 16. A tracking system / method / device / software as described in Section 12, wherein at least one gait feature comprises at least one of the following stride-related features, the stride-related feature being: at least one gait velocity, each associated with a timestamp in a time window; at least one stride length, each associated with a timestamp in a time window and being the integral of the gait velocity over each gait cycle around the timestamp; at least one stride duration, each associated with a timestamp in a time window and being the duration of each gait cycle around the timestamp; at least one stride frequency, each associated with a timestamp in a time window and being inversely proportional to each stride duration around the timestamp; a stride-wise average velocity, each associated with a timestamp in a time window and being at least one of the average, weighted average, and trimmed average of the gait velocity in each gait cycle around the timestamp; and the maximum gait velocity, each associated with a timestamp in a time window and being the maximum gait velocity in each gait cycle around the timestamp. At least one stridewise maximum velocity, each associated with a timestamp within a time window and being the minimum stridewise minimum velocity within each gait cycle around the timestamp; at least one stridewise velocity variance, each associated with a timestamp within a time window and being the variance of the gait velocity within each gait cycle around the timestamp; at least one stridewise velocity deviation, each associated with a timestamp within a time window and being at least one of the mean, weighted mean, and trimmed mean of the gait acceleration within each gait cycle around the timestamp;Gait acceleration is the derivative of gait velocity, and is the maximum gait acceleration in each gait cycle around the timestamp, each associated with a timestamp within a time window, and the maximum gait acceleration in each gait cycle around the timestamp, each associated with a timestamp within a time window, and the minimum gait acceleration in each gait cycle around the timestamp, and the minimum gait acceleration in each gait cycle around the timestamp, each associated with a timestamp within a time window, and the variance of the gait acceleration in each gait cycle around the timestamp. At least one stridewise acceleration variance, each associated with a timestamp within a time window and being the X-percentile of the sample distribution of mean subtracted acceleration in each gait cycle around the timestamp, where mean subtracted acceleration is gait acceleration minus stridewise mean acceleration, and X is a number between 0 and 100. At least one stridewise acceleration deviation, each associated with a timestamp within a time window, stride length, stride duration, stride frequency, stridewise mean velocity, stridewise maximum velocity, stridewise minimum velocity, stridewise velocity variance, stridewise velocity deviation, stridewise mean acceleration, stridewise maximum acceleration, stridewise minimum acceleration, stridewise acceleration variance, stridewise acceleration deviation, stridewise velocity peak variance, stridewise velocity trough variance, stridewise velocity ACF k-th peak, stridewise mean velocity ACF k-th peak variance, stridewise velocity ACF k-th peak-difference, stridewise average velocity ACF k-th peak-difference, stridewise velocity ACF k-th peak-difference variance, stridewise velocity ACF peak count, stridewise average velocity ACF k-th peak-difference, stridewise velocity ACF k-th peak-difference variance, stridewise velocity ACF peak count-pdf, stridewise recurrent plot (RP), stridewise recurrent plot (RP) features,Stridewise scaled velocity-peak ACF, stridewise scaled peak ACF features, stridewise harmonic ratio, stridewise harmonic features, stridewise generalized harmonic features, stridewise symmetry measure, at least one stride statistic which is a function of at least one of the above statistics, a function of another statistic, and a function of another stride statistic, each associated with a timestamp in a time window and at least one mean stride statistic which is the mean, weighted mean, and trimmed mean of the stride statistics in a subwindow of the time window around the timestamp, each associated with a timestamp in a time window and of odd gait cycles in a subwindow of the time window around the timestamp At least one odd-mean stride statistic that is at least one of the mean, weighted mean, and trimmed mean of the ride statistics, each associated with a timestamp in the time window and at least one even-mean stride statistic that is at least one of the mean, weighted mean, and trimmed mean of the stride statistics of even-gait cycles in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one maximum stride statistic that is the maximum value of the stride statistics in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one maximum stride statistic that is the maximum value of the stride statistics of odd-gait cycles in a subwindow of the time window around the timestamp At least one odd maximum stride statistic, each associated with a timestamp within the time window, and at least one even maximum stride statistic, which is the maximum value of the stride statistic for even gait cycles in a subwindow of the time window around the timestamp, each associated with a timestamp within the time window, and at least one minimum stride statistic, which is the minimum value of the stride statistic in a subwindow of the time window around the timestamp, each associated with a timestamp within the time window,At least one odd minimum stride statistic, each associated with a timestamp in the time window, and at least one even stride statistic, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one odd stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and at least one stride statistical variance, each associated with a timestamp in the time window, and The X percentile of the sample distribution of mean subtracted stride statistics in the subwindow of the time window around the timestamp, where the mean subtracted stride statistics for odd gait cycles are obtained by subtracting the odd mean stride statistics from the stride statistics for odd gait cycles, where X is at least one stride statistic deviation between 0 and 100, each associated with a timestamp within the time window, and the X percentile of the sample distribution of mean subtracted stride statistics for odd gait cycles in the subwindow of the time window around the timestamp, where the mean subtracted stride statistics for odd gait cycles are obtained by subtracting the odd mean stride statistics from the stride statistics for odd gait cycles, where X is at least one odd stride statistic deviation between 0 and 100, each associated with a timestamp within the time window, and the X percentile of the sample distribution of mean subtracted stride statistics for even gait cycles in the subwindow of the time window around the timestamp,The average subtracted stride statistic for even gait cycles is obtained by subtracting the even mean stride statistic from the stride statistic for even gait cycles, where X is a number between 0 and 100. At least one even stride statistic deviation, each associated with a timestamp in the time window, is at least one of the stride statistic, which is the mean stride statistic, maximum stride statistic, minimum stride statistic, stride statistic variance, stride statistic deviation, and another stride statistic. At least one odd stride statistic, each associated with a timestamp in the time window, is at least one of the odd mean stride statistic, odd maximum stride statistic, odd minimum stride statistic, odd stride statistic variance, odd stride statistic deviation, and another odd stride statistic. At least one odd stride statistic, each associated with a timestamp in the time window, is at least one of the odd mean stride statistic, odd maximum stride statistic, odd minimum stride statistic, odd stride statistic variance, odd stride statistic deviation, and another odd stride statistic. Value, even stride statistic variance, even stride statistic deviation, and at least one even statistic of the stride statistic which is at least one of the other even statistic of the stride statistic, left stride statistic, right stride statistic, front left stride statistic, front right stride statistic, rear left stride statistic, rear right stride statistic, wavefront stride statistic, odd wavefront stride statistic, even wavefront stride statistic, ratio of even statistic of the stride statistic to odd statistic of the stride statistic, stride statistic The difference between even stride statistics and odd stride statistics, the similarity between even stride statistics and odd stride statistics, and at least one function of stride statistics, odd stride statistics, and even stride statistics, the ratio of the function of even stride statistics to the function of odd stride statistics, the first function of the second function of even stride statistics and the third function of odd stride statistics, and another statistic of stride statistics.

[0299] Section 17. A tracking system / method / device / software as described in Section 12, wherein at least one gait feature comprises at least one of the following stride sequence-related features, the stride sequence-related feature being: at least one gait velocity, each associated with a timestamp in a time window; at least one stride sequence length, each associated with a timestamp in a time window and being the integral of the gait velocity over each extended gait cycle around the timestamp; at least one stride sequence duration, each associated with a timestamp in a time window and being the duration of each extended gait cycle around the timestamp; at least one stride sequence frequency, each associated with a timestamp in a time window and being inversely proportional to each stride sequence duration around the timestamp; at least one stride sequence-wise average velocity, each associated with a timestamp in a time window and being at least one of the average, weighted average, and trimmed average of the gait velocity in each extended gait cycle around the timestamp; and at least one maximum gait velocity, each associated with a timestamp in a time window and being inversely proportional to each extended gait cycle around the timestamp. At least one stride-sequence-wise maximum velocity, at least one stride-sequence-wise minimum velocity, each associated with a timestamp within a time window and being the minimum gait velocity within each extended gait cycle around the timestamp, at least one stride-sequence-wise velocity variance, each associated with a timestamp within a time window and being the variance of the gait velocity within each extended gait cycle around the timestamp, at least one stride-sequence-wise velocity deviation, each associated with a timestamp within a time window and being the X percentile of the sample distribution of the mean subtracted velocity in each extended gait cycle around the timestamp, where the mean subtracted velocity is the gait velocity minus the stride-sequence-wise mean velocity, and X is a number between 0 and 100.Each is associated with a timestamp within a time window and is at least one of the mean, weighted mean, and trim mean of gait acceleration in each extended gait cycle around the timestamp, where the gait acceleration is the derivative of gait velocity; each is associated with a timestamp within a time window and is at least one stride sequence-wide maximum acceleration which is the maximum gait acceleration in each extended gait cycle around the timestamp; and each is associated with a timestamp within a time window and is the minimum gait acceleration in each extended gait cycle around the timestamp. At least one stride-sequence-wise minimum acceleration, at least one stride-sequence-wise acceleration variance, each associated with a timestamp in the time window and being the variance of gait acceleration in each extended gait cycle around the timestamp, at least one stride-sequence-wise acceleration deviation, each associated with a timestamp within the time window and being the X percentile of the sample distribution of mean subtracted acceleration in each extended gait cycle around the timestamp, where mean subtracted acceleration is gait acceleration minus stride-sequence-wise mean acceleration, and X is a number between 0 and 100, each time window Associated with the timestamp within the node, and including stride sequence length, stride sequence duration, stride sequence frequency, stride sequence wide average velocity, stride sequence wide maximum velocity, stride sequence wide minimum velocity, stride sequence wide velocity variance, stride sequence wide velocity deviation, stride sequence wide average acceleration, stride sequence wide maximum acceleration, stride sequence wide minimum acceleration, stride sequence wide acceleration variance, stride sequence wide acceleration deviation, stride sequence wide velocity peak variance, stride sequence wide velocity trough variance, stride sequence wide velocity ACF k-th peak, stride sequence wide average velocity ACF k-th peak, stride sequence wide velocity ACF k-th peak variance,Stride Sequence Wise Velocity ACF k-th Peak-Difference, Stride Sequence Wise Average Velocity ACF k-th Peak-Difference, Stride Sequence Wise Velocity ACF k-th Peak-Difference Variance, Stride Sequence Wise Velocity ACF Peak Count, Stride Sequence Wise Average Velocity ACF k-th Peak-Difference, Stride Sequence Wise Velocity ACF k-th Peak-Difference Variance, Stride Sequence Wise Velocity ACF Peak-Count-pdf, Stride Sequence Wise Recurrent Plot, Stride Sequence Wise Recurrent Plot (RP) Features, Stride Sequence Wise Scaled Velocity-Peak ACF, Stride Sequence Wise Scaled Peak ACF Features, Stride Sequence Wise Harmonic Ratio, Stride Sequence Wise Harmonic Features, Stride Sequence Wise Generalized Harmonic Features, Stride Sequence Wise Symmetry Measurement, At least one stride sequence statistic which is a function of at least one of the above statistics, a function of another statistic, and a function of another stride sequence statistic, each associated with a timestamp in a time window and stride in a subwindow of the time window around the timestamp At least one average stride sequence statistic, which is at least one of the mean, weighted mean, and trimmed mean of sequence statistics, each associated with a timestamp in the time window, and at least one odd average stride sequence statistic, which is at least one of the mean, weighted mean, and trimmed mean of stride sequence statistics for odd extended gait cycles in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window, and at least one even average stride sequence statistic, which is at least one of the mean, weighted mean, and trimmed mean of stride sequence statistics for even extended gait cycles in a subwindow of the time window around the timestamp, each associated with a timestamp in the time window,At least one maximum stride sequence statistic that is the maximum value of the stride sequence statistics in the subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one odd maximum stride sequence statistic that is the maximum value of the stride sequence statistics of odd extended gait cycles in the subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one even maximum stride sequence statistic that is the maximum value of the stride sequence statistics of even extended gait cycles in the subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one minimum stride sequence statistic that is the minimum value of the stride sequence statistics in the subwindow of the time window around the timestamp, each associated with a timestamp in the time window and at least one odd minimum stride sequence statistic that is the minimum value of the stride sequence statistics of odd extended gait cycles in the subwindow of the time window around the timestamp, each associated with a timestamp in the time window and around the timestamp At least one even minimum stride sequence statistic, which is the minimum value of the stride sequence statistic for even extended gait cycles within a subwindow of the time window; at least one stride sequence statistical variance, each associated with a timestamp in the time window and being the variance of the stride sequence statistic in a subwindow of the time window around the timestamp; at least one odd stride sequence statistical variance, each associated with a timestamp in the time window and being the variance of the stride sequence statistic for odd extended gait cycles within a subwindow of the time window around the timestamp; at least one even stride sequence statistical variance, each associated with a timestamp in the time window and being the variance of the stride sequence statistic for even extended gait cycles within a subwindow of the time window around the timestamp.Each is associated with a timestamp within a time window, and is the X percentile of the sample distribution of mean subtracted stride sequence statistics in the subwindow of the time window around the timestamp, where the mean subtracted stride sequence statistics for odd extended gait cycles is obtained by subtracting the odd mean stride sequence statistics from the stride sequence statistics for odd extended gait cycles, where X is at least one stride sequence statistics deviation between 0 and 100, each is associated with a timestamp within a time window, and is the X percentile of the sample distribution of mean subtracted stride sequence statistics for odd extended gait cycles in the subwindow of the time window around the timestamp, where the mean subtracted stride sequence statistics for odd extended gait cycles is obtained by subtracting the odd mean stride sequence statistics from the stride sequence statistics for odd extended gait cycles, where X is at least one odd stride sequence statistics deviation between 0 and 100, each is associated with a timestamp within a time window, and X percentile of the sample distribution of mean subtracted stride sequence statistics for even extended gait cycles in a subwindow of the time window around the imstamp, where the mean subtracted stride sequence statistics for even extended gait cycles is the stride sequence statistics for even extended gait cycles minus the mean even stride sequence statistics, where X is a number between 0 and 100. At least one even stride sequence statistics deviation, each associated with a timestamp in the time window, and at least one of the stride sequence statistics, each associated with a timestamp in the time window, which is the mean stride sequence statistics, maximum stride sequence statistics, minimum stride sequence statistics, stride sequence statistics variance, stride sequence statistics deviation, and another statistic of stride sequence statistics.and at least one odd statistic of the stride sequence statistics, each associated with a timestamp in the time window, even mean stride sequence statistics, even maximum stride sequence statistics, even minimum stride sequence statistics, even stride sequence statistic variance, even stride sequence statistic deviation, and another even statistic of the stride sequence statistics, total stride sequence statistics, posterior stride sequence statistics, wavefront stride sequence, The statistics are: a statistic, a wavefront stride sequence statistic for phase 1, a wavefront stride sequence statistic for phase 2, a wavefront stride sequence statistic for phase 3, a wavefront stride sequence statistic for phase 4, a wavefront stride sequence statistic for the nth phase, the ratio of even statistics to odd statistics of the stride sequence statistics, the difference between even statistics and odd statistics of the stride sequence statistics, the similarity between even statistics and odd statistics of the stride sequence statistics, and at least one function of the stride sequence statistics, odd statistics of the stride sequence statistics, and even statistics of the stride sequence statistics, the ratio of the function of even statistics to the function of odd statistics of the stride sequence statistics, the first function of the second function of even statistics of the stride sequence statistics, the third function of odd statistics of the stride sequence statistics, and another statistic of the stride sequence statistics.

[0300] Item 18. A tracking system / method / device / software described in Item 12 that communicates at least one cycle feature to a server.

[0301] Item 19. A tracking system / method / device / software as described in Item 12, which includes communicating cycle features to a user device and generating a presentation of cycle features or a history of cycle features for the user of the user device.

[0302] Item 20. A tracking system / method / device / software for Item 1 that communicates the next location of an object to the server at the next time.

[0303] Item 21. A tracking system / method / device / software as described in Item 1, which includes communicating the next location of an object to a user device, and generating a presentation of at least one of the following: the next location, the movement trajectory, or the user tracking of the user device.

[0304] Item 22. A tracking system / method / device / software as described in Item 7, 8, or 9, which includes calculating the amount of cycles in an incremental period and calculating the incremental distance as the product of the product of the amount of cycles and the distance of the cycles.

[0305] In some embodiments, the cycle distance can be determined by some form of training. For example, during the calibration or training process, an object (e.g., a human user) walks while a reference distance may be obtained using another positioning system, which may include, for example, GPS with triangulation or trilateration when the object is walking outdoors, CSI-based positioning if available, next-generation positioning using WiFi signals (e.g., based on 802.11ay), UWB-based positioning, radar-based positioning, or millimeter-wave-based positioning. To calculate the cycle distance, the system may analyze the rhythmic behavior of TSIQ or TSSI, count the number of cycles (quantity) within a time window, and then calculate the cycle distance by dividing the reference distance within the time window by the number of cycles within the time window.

[0306] Item 23. A tracking system / method / device / software as described in Item 22, which includes calculating the cycle distance based on at least one of the following: user input, training values ​​obtained during the training phase, a calibration or training process using a “reference distance” from another localization method, or a value adaptively calculated based on stable, rhythmic movement over a recent period.

[0307] Item 24. A tracking system / method / device / software as described in Item 1, in which the next position of an object at the next time is calculated using a particle filter.

[0308] Section 25. A tracking system / method / device / software as described in Section 1, for calculating the next position of the object at the next time, includes initializing an initial value (N_0) of the first candidate position of the object at the beginning (t=0); iteratively calculating a first dynamic number (N_(i+1)) of the first candidate position of the object at the next time (t=(i+1)) based on a second dynamic number (N_i) of the second candidate position of the object at the present time (t=i); and calculating the next position of the object at the next time based on at least one of the first dynamic number of the first candidate position at the next time and the second dynamic number of the second candidate position at the present time.

[0309] Item 26. The region tracking system / method / device / software of item 25, wherein the object moves within the area represented by the map during the growth period, the map is a multi-dimensional map represented as a multi-dimensional array A, and the reachability of each position of the region is represented by the corresponding array element a which is a logical value between 0 and 1. When the corresponding array element satisfies a = 0, the position of the region is unreachable and prohibited. When the corresponding array element satisfies a = 1, the position of the region is fully reachable. When the corresponding array element satisfies 0 < a < 1, the position of the region is partially reachable. Each of the next position, the current position, the first dynamic number of the first candidate position, and the second dynamic number of the second candidate position is a point within the region, represented as the corresponding array element a having a > 0. The direction of the movement of the object at any position is locally represented as one of several allowable directions.

[0310] In some embodiments, map constraints (e.g., based on matrix A) can be used to reject or eliminate unreachable candidate positions. For example, assume there are Ni legitimate candidate positions at time t = i. The system can calculate Ni predicted positions for time t = (i + 1). Each predicted position is checked against matrix A. If it is unreachable, it is rejected or eliminated. The remaining predicted positions can then become legitimate candidate positions. If the number of remaining legitimate candidate positions is too small, new legitimate candidate positions can be generated based on some probabilistic models and rules. As a result, the number of candidate positions can vary in different iterations, i.e., in different dynamic numbers. However, their total number never falls below a certain minimum number.

[0311] In some embodiments, the system can generate predicted candidate positions as the sum of the displacement vector (magnitude = "incremental distance" in "direction") and the "current candidate position". Alternatively, this sum may be used as a parameter to a probability distribution, e.g., a Gaussian distribution with a mean as a calculated sum and a variance value related to a measure of the "goodness" of the current candidate position. For example, the variance value may be a function of the reachability value in matrix A. High reachability (e.g., A=1) may correspond to a larger variance, while low reachability (e.g., A=0.1) may correspond to a smaller variance. The next position may be calculated by combining the remaining legitimate (reachable) candidate positions at time t=(i+1), for example, using a weighted mean with associated weights, mean, median, maximum likelihood, centroid, or MAP.

[0312] Section 27. A tracking system / method / device / software as described in Section 26, wherein calculating the next position of an object at the next time includes calculating a first dynamic number of weights associated with each first candidate position, each weight being a function of at least one of the following: the current position, the first candidate position, the corresponding second candidate position associated with the first candidate position, the direction of movement, and the distance between the first candidate position and a first unreachable array element a in the direction said; and calculating the next position of the object based on the first dynamic number of the first candidate positions and the first dynamic number of the associated weights.

[0313] Item 28. A tracking system / method / device / software according to Item 27, wherein each weight is a monotonic non-decreasing function of the distance between a first candidate position and a first unreachable array element a in the aforementioned direction.

[0314] Item 29. The tracking system / method / device / software described in Item 27, wherein each weight is a bounded function of the distance between a first candidate position and a first unreachable array element a in the aforementioned direction.

[0315] 30. A tracking system / method / device / software as described in 27, wherein the next position of an object at the next time is calculated as a weighted average of a first dynamic number of first candidate positions.

[0316] Item 31. A tracking system / method / device / software as described in Item 27, wherein the next position of an object at the next time is calculated as one of the first candidate positions.

[0317] 32. A tracking system / method / device / software according to 37, further comprising the step of normalizing the weights to generate normalized weights, for calculating the next position of the object at the next time.

[0318] Section 33. A tracking system / method / device / software as described in Section 32, further comprising calculating the next position of an object at the next time, comprising: calculating a weighted cost of each first candidate position for the remainder of a first dynamic number of first candidate positions based on normalized weights, wherein the weighted cost is the weighted sum of the pairwise distances between the first candidate position and each of the remainder of the first candidate positions; and selecting the first candidate position with the minimum weighted cost as the position of the object at the next time.

[0319] Item 34. A tracking system / method / device / software according to item 6, wherein calculating the next position of an object at the next time includes: when the predicted value of the second candidate position is fully reachable with the associated array element a = 1, calculating a predicted value for each of the second candidate positions of the object based on at least one of the second candidate position, the incremental distance, and the incremental period; when the predicted value of the second candidate position has the associated array element a = 0 and is prohibited, creating a first candidate position of the object based on the predicted value of the second candidate position; when the predicted value of the second candidate position has an associated array element satisfying 0 < a < 1 and is partially reachable, labeling the second candidate position as "rejected" without generating a first candidate position; generating a random number between 0 and 1; and when the random number is less than a, creating a first candidate position of the object based on the predicted value of the second candidate position.

[0320] Item 35. A tracking system / method / device / software according to item 14, wherein calculating the next position of an object at the next time further includes: when the number of first candidate positions is less than a threshold, probabilistically taking the predicted value of the non-rejected second candidate position according to a probability distribution based on at least one of the weight associated with the predicted value, the weight associated with the second candidate position, the array element of the multi-dimensional array A associated with the predicted value, and the array element of the multi-dimensional array A associated with the second candidate position, to generate a new first candidate position of the object.

[0321] Item 36. A tracking system / method / device / software according to item 14, wherein calculating the next position of an object at the next time further includes: when the number of first candidate positions is less than a threshold, probabilistically taking the non-rejected second candidate position, and generating a new first candidate position of the object with a probability based on the weight associated with the predicted value of the non-rejected second candidate position.

[0322] Section 37. A tracking system / method / device / software described in Section 14, which calculates the next position of an object at the next time, further includes, if the quantity of first candidate positions is less than a threshold, calculating a provisional next position based on the first candidate positions, and probabilistically generating a new first candidate position of the object in the neighborhood of the provisional next position based on a probability distribution.

[0323] Section 38. A tracking system / method / device / software described in Section 14, which calculates the next position of an object at the next time, further comprises, if the quantity of first candidate positions is less than a threshold, probabilistically generating a new first candidate position of the object based on predictions of positions sampled in the neighborhood of the current position based on a probability distribution.

[0324] 39. The tracking system / method / device / software described in 39, wherein the neighborhood includes at least one of the second candidate locations that are not rejected.

[0325] Item 40. The tracking system / method / device / software described in Item 18 is a weighted sum of a set of probability density functions, each centered on one of the second candidate positions that are not rejected, where the probability distribution is the probability distribution.

[0326] Item 41. A tracking system / method / device / software as described in Item 20, where the weight of each PDF associated with the second candidate position in the weighted sum is a function of the array element associated with the second candidate position.

[0327] Section 42. A tracking system / method / device / software as described in Section 1, further comprising: maintaining a dynamic number of candidate positions at any time; modifying the dynamic number of candidate positions by at least one of initializing at least one candidate position; updating at least one candidate position; adding at least one candidate position; pausing at least one candidate position; stopping at least one candidate position; resuming at least one paused candidate position; reinitializing at least one stopped candidate position; removing at least one candidate position; and calculating the next position of an object in the next time based on at least one of the dynamic number of candidate positions in the next time and the dynamic number of candidate positions in another time.

[0328] Item 43. The tracking system / method / device / software described in Item 22, wherein the dynamic number of candidate locations is limited by at least one of the upper and lower limits.

[0329] Item 44. A tracking system / method / device / software as described in Item 22, further comprising adding at least one candidate location if the dynamic number of candidate locations is below a lower limit.

[0330] Section 45. A method / apparatus / system of a wireless monitoring system as described in Section 7, 8, or 9, further comprising verifying a complete cycle as the target type of the cycle.

[0331] 46. ​​A method / apparatus / system of a wireless monitoring system as described in 45, further comprising calculating a likelihood score associated with a complete cycle based on at least one of TSIQ, TSMSIQ, or TSSI, and verifying the complete cycle as a target cycle based on the likelihood score.

[0332] Item 47. A method / apparatus / system of a wireless monitoring system as described in Item 46, further comprising verifying a complete cycle as the target cycle if the likelihood score satisfies the verification conditions related to the target cycle.

[0333] Item 48. A method / apparatus / system of a wireless monitoring system as described in Item 47, further comprising verifying a complete cycle as a target cycle if the likelihood score is greater than a threshold.

[0334] Section 49. A method / apparatus / system of a wireless monitoring system as described in Section 46, further comprising: calculating a period relating to a complete cycle having a corresponding start time and a corresponding end time of the period; calculating at least one feature of the TSIQ and TSMSIQ within the period, which is a sum, a weighted sum, a sum of magnitudes, a weighted sum of magnitudes, a local maximum, a zero crossing, a local minimum, a maximum derivative, a minimum derivative, and a zero derivative; calculating at least one cycle statistic based on the calculated features within the period; and calculating a likelihood based on the period, at least one cycle statistic, at least one feature, or a function of the cycle statistic or feature.

[0335] Section 50. A method / apparatus / system of a wireless monitoring system as described in Section 49, comprising calculating a likelihood score of 0 when any of the following occurs: the duration of a period exceeds a first threshold; the cycle statistic is greater than a second threshold; the maximum is greater than a second threshold; the cycle statistic is less than a third threshold; the minimum is less than a third threshold; and the ratio of the maximum quantity in the most recent time window to the minimum quantity in the most recent time window exceeds a fourth threshold, where the quantity is one of one of the following: a feature, a function of two or more features, or the difference between the maximum and minimum values ​​in the complete cycle and cycle statistic in the most recent time window, and the cycle statistic is in the N-sigma region around its mean in the most recent time window. The feature is outside the 4-sigma region around its mean in the recent time window, where sigma is the standard deviation in the most recent time window and N is an integer, further including that the feature is outside the 4-sigma region around its mean in the most recent time window, sigma is its standard deviation in the most recent time window, the sum of its magnitudes is outside the 4-sigma region around its mean in the most recent time window, sigma is its standard deviation in the most recent time window, the sum of its magnitudes is outside the 4-sigma region around its mean in the most recent time window, and sigma is its standard deviation in the most recent time window.

[0336] Item 51. A method / apparatus / system of a wireless monitoring system as described in Item 46, further comprising calculating an incremental distance related to the movement of an object over a period related to a complete cycle as the product of a likelihood score and a hypothetical incremental distance.

[0337] Item 52. A method / apparatus / system of a wireless monitoring system as described in Item 45, further comprising: the object being a human being; the object's movement being a human walking or running movement; the target type of the cycle being a human walking or running step cycle; and calculating the user status as "walking" or "running" based on verification of the complete cycle as a step cycle.

[0338] Item 53. A wireless monitoring system method / apparatus / system as described in Item 52, further comprising changing the user status from “non-walking” to “walking” or from “non-running” to “running” based on verification of a complete cycle as a step c...

Claims

1. A wireless motion monitoring system, A transmitter configured to transmit a first radio signal over a wireless multipath channel of a location, A receiver configured to receive a second radio signal via the aforementioned wireless multipath channel, wherein the second radio signal differs from the first radio signal due to the wireless multipath channel and the movement of an object at the location, and It is a processor, Based on the second wireless signal, time-series channel information (TSCI) of the wireless multipath channel is obtained, Calculating spatiotemporal information (STI) based on the aforementioned TSCI, Monitoring the movement of the object based on the TSCI and STI, Executing tasks based on the aforementioned monitoring, and A system including a processor configured to generate a response based on the aforementioned task.

2. The system according to claim 1, wherein the processor further Selecting an operating mode from among several supported operating modes related to the task to become the current operating mode of the task, A system configured to monitor the status of at least one of the objects based on the TSCI and the current operating mode, wherein the response is generated based on the current operating mode of the task.

3. The system according to claim 2, The current operating mode is a user absence mode for monitoring the location when the user of the system is not present at that location. The aforementioned processor further, Under the aforementioned user-absent mode, the object is an intruder. Setting the sensitivity for monitoring the movement of the aforementioned object, Enable or disable at least one of the transmitter or at least one other receiver of the system. Setting the timetable for the user absence mode with the respective settings related to each period of the timetable, Setting parameters, thresholds, and timings for the aforementioned user absence mode. Setting the method and mode of notification to the aforementioned user, or Setting up monitoring of the at least one status of the object related to the user absence mode, Perform at least one of the following, Monitoring the TSCI and STI for patterns indicating the presence of the intruder, The above response, Sending notifications to designated users, To generate an alarm, To trigger an alarm animation, Play the warning message. To engage in dialogue with the aforementioned intruder, To require the intruder to provide the identification information of a legitimate user, Sounding the siren of the aforementioned system, To protect the aforementioned location, A system configured to include at least one of the following and to perform the following actions.

4. The system according to claim 2, The current operating mode is a user-at-home mode for monitoring the location when the user of the system is at the location. The aforementioned processor further, Under the aforementioned user home mode, the object is identified as the user. Setting the sensitivity setting for monitoring the movement of the aforementioned object, Enable or disable at least one of the transmitter or at least one other receiver of the system. Set the timetable for the user at-home mode with the respective settings related to each period of the timetable. Setting parameters, thresholds, and timings for the aforementioned user home mode. To set the method and mode of notification to the aforementioned user, Setting up monitoring of at least one status of the object related to the user's home mode, Perform at least one of the following, Monitoring the user's actions at the aforementioned location based on the TSCI and STI, and The above response, To identify the location of the aforementioned user, To monitor the user's movements, To monitor at least one of the user's daily routines, habits, or behaviors. Monitoring the deviation of the user from at least one of the daily routine, habits, or behaviors, Detecting additional users, To monitor the movements of the aforementioned additional users, Monitoring the behavior of the aforementioned additional users, To monitor the interaction between the aforementioned user and the additional user, To detect dangerous actions by the aforementioned user, To detect the user's fall, To monitor the vital signs of the aforementioned user, To monitor at least one of the user's respiration or heart rate, To monitor the sleep of the aforementioned user, Monitoring at least one of the user's movements, dance, exercise, pace, pauses, or rests. To detect the user's gestures, To engage in dialogue, exchange, or interaction with the aforementioned user, To notify, report, or remind the aforementioned user, Instructing at least one user device to perform the aforementioned dialogue, exchange, interaction, notification, report, or reminder, In the aforementioned dialogue, exchange, interaction, notification, report, or reminder, at least one presentation or user interface is generated on the at least one user device. Danger, fall, event, situation, state, gesture, verifying the user's commands, exchanges or interactions based on the dialogue, or Communicating information about the user's actions to at least one of the following: other users, other users' devices, servers, cloud servers, local servers, storage, network storage, distributed storage, blockchain, databases, or analytical modules. A system configured to include at least one of the following and to perform the following actions.

5. The system according to claim 2, If no user of the system is present at the location and the user does not wish for the siren to sound, the current operating mode is vigilance mode for monitoring the location. The aforementioned processor further, Regarding the fact that the aforementioned object is an intruder, under the vigilance mode, Setting a sensitivity setting for monitoring the movement of the aforementioned object, Enable or disable at least one of the transmitter or at least one other receiver of the system. Setting the timetable for the vigilance mode with the respective settings related to each period of the timetable, Setting parameters, thresholds, and timings for the vigilance mode, Setting the method and mode of notification to the aforementioned user, or Setting up the monitoring of the at least one state of the object related to the vigilance mode, To do at least one of the following, The monitoring of the TSCI and STI relating to the pattern indicating the presence of the intruder, wherein the response is Sending notifications to designated users, To generate an alarm, To trigger an alarm animation, Play the warning message. To engage in dialogue with the aforementioned intruder, To require the intruder to provide the identification information of a legitimate user, Sounding the siren of the aforementioned system, To protect the aforementioned location, Monitoring, which includes at least one of the following: The above response, To generate an alarm, To trigger an alarm animation, Play the warning message. Engaging in dialogue with the aforementioned intruder, To require the intruder to provide the identification information of a legitimate user, Sounding the siren of the aforementioned system, To protect the aforementioned location, Set it to exclude at least one of the following, A system configured to perform the following actions.

6. The system according to claim 2, If the user of the system is absent from the location and the user wishes to be notified of the movement of the detected object, the current operating mode is guard mode for monitoring the location. The aforementioned processor further, Regarding the fact that the aforementioned object is an intruder, under the guard mode, Setting a sensitivity setting for monitoring the movement of the aforementioned object, Enable or disable at least one of the transmitter or at least one other receiver of the system. Setting the timetable for the guard mode with the respective settings related to each period of the timetable, Setting parameters, thresholds, and timings for the aforementioned guard mode, To set the method and mode of notification to the aforementioned user, Setting up the monitoring of the at least one state of the object related to the guard mode, Perform at least one of the following, Monitoring the TSCI and STI related to the pattern indicating the presence of the intruder, The above response, To send a notification to the aforementioned user, Based on the confirmation from the aforementioned user, further actions will be taken. Setting it to include at least one of the following: switching to a different operating mode, A system configured to perform the following actions.

7. The system according to claim 2, The current operating mode is a power-saving mode for monitoring the location. The aforementioned processor further, The transmission of the first wireless signal from the transmitter, The reception of the second wireless signal by the receiver, The acquisition of the TSCI based on the second wireless signal, The calculation of the STI based on the TSCI, The monitoring of the movement of the object based on the TSCI and the STI, The execution of the aforementioned task, or The occurrence of the response based on the task, A system configured to pause or stop at least one of the following.

8. The system according to claim 2, The current operating mode is (a) a user challenge mode for monitoring the location when the object is detected at the location and the object is unidentified, or (b) a user interaction mode for monitoring the location when the object is a user. The aforementioned processor further, In the user challenge mode, the response is Sending notifications to designated users, To generate an alarm, To trigger an alarm animation, Play the warning message. Engaging in dialogue with the intruder, To require the aforementioned object to display the identification information of a legitimate user, To protect the aforementioned location, Switching to a different operating mode, Set to include at least one of the following, In the user interaction mode, the response is To engage in dialogue, exchange, or interaction with the aforementioned user, To notify, report, or remind the aforementioned user, Instructing at least one user device to perform the aforementioned dialogue, exchange, interaction, notification, report, or reminder, In the aforementioned dialogue, exchange, interaction, notification, report, or reminder, at least one presentation or user interface is generated on the at least one user device. Verifying at least one of the following: danger, fall, event, situation, state, sleep-related state, behavior, dance, exercise, gait, rest, and deviation from at least one of a daily routine, habit or behavior, movement, gesture, command, exchange or interaction of the user based on the dialogue, or Communicating information about the user's actions to at least one of the following: other users, other users' devices, servers, cloud servers, local servers, storage, network storage, distributed storage, blockchain, databases, or analytical modules. Set to include at least one of the following, and In response to the aforementioned response, monitoring the movement of the object, A system configured to perform the following actions.

9. The system according to claim 2, wherein the processor is User selection of the aforementioned system, The aforementioned user preferences, Input by the user to the user device, The user's selection in the user interface (UI), The user presses a button on the UI, The verbal selection by the user, Presentation to the aforementioned user, Presentation using the aforementioned UI, Interaction, exchange, or communication with the aforementioned user, Interaction, exchange, or communication based on the user device, Each is a finite state machine (FSM) having at least one state associated with its respective supported operating mode. A finite state machine (FSM) that includes states related to the order of supported operating modes. At least one state of the object being monitored, A timetable relating to at least one of the tasks, the user, the object, or the location, The presence or absence of the user at the aforementioned location, Communication with other systems, Power on, power off, system reset, or The need for power saving in the aforementioned system, A system further configured to change the current operating mode of the task from the operating mode to a different operating mode among the supported operating modes, based on at least one of the following.

10. The system according to claim 9, wherein the processor further Associating at least one registered wirelessly discoverable item with the system, If no movement is detected for a certain period of time and at least one registered wirelessly discoverable item is not detected, the user at home mode will generate at least one of the following: a question, presentation, notification, dialogue, exchange, or interaction, wherein the question will be communicated to the user device to ask whether the user intends to switch to user absent mode. After the user confirms the switch or after the user does not reject the switch within the timeout period, the current operating mode of the task is changed from user at home mode to user absent mode. A system configured to perform the following actions.

11. The system according to claim 9, wherein the processor further A system configured to change the current operating mode of the task from user-at-home mode to user-absent mode after no movement is detected for a certain period of time in user-at-home mode.

12. The system according to claim 9, wherein the processor further Associating at least one registered wirelessly discoverable item with the system, When any of the at least one registered wirelessly discoverable item is detected, the system will generate a notification, greeting, dialogue, exchange, interaction, or presentation to welcome the user in user absence mode. A system configured to change the current operating mode of the task from the user absent mode to the user present mode when at least one of the registered wirelessly discoverable items is detected.

13. The system according to claim 9, wherein the processor further If movement is detected in user-unattended mode, the system will wait for a predetermined period before issuing an alarm. If the user chooses to switch the current operating mode from user-at-home mode to user-absent mode, the system will wait for a predetermined period before changing the current mode to user-absent mode. A system configured to wait for an additional predetermined period before changing the current operating mode to the user absent mode if the user chooses to switch the current operating mode from user at home mode to user absent mode, but does not leave the location within a predetermined period.

14. The system according to claim 1, wherein the processor further Receiving emergency messages and Based on the aforementioned emergency message, Interacting with, communicating with, or interacting with the user of the aforementioned system. To notify, report, or remind the aforementioned user, Instructing at least one of the user's devices to perform the aforementioned dialogue, exchange, interaction, notification, report, or reminder; In the aforementioned dialogue, exchange, interaction, notification, report, or reminder, at least one presentation or user interface is generated on the at least one user device. Verifying the emergency message, danger, fall, event, situation, state, gesture, and the user's commands, exchanges, or interactions based on the dialogue and the emergency situation related thereto, To begin evacuation, Activate the emergency alarm system. Activate the emergency response system. Turn on the emergency lights. Activate the emergency broadcast system. To start the emergency messaging system, Activate the priority notification system. Sounding the siren, Sounding an alarm, Displaying visual warnings, Activating an animated alarm, The system will notify or generate a personalized alarm at at least one of the following locations: the user of the system, or one or more designated contact persons. Requesting emergency services, or Requesting the dispatch of at least one first responder to at least one of the following locations: the aforementioned location, a designated location, the user's home address, or the user's current location. A system configured to perform at least one of the following emergency actions.

15. The system according to claim 1, The transmitter is located in the first device. The receiver is placed in the second device. At least one of the first device or the second device is a device that has one role in the system, The processor is further configured to modify one of the first and second devices of the system based on at least one of a software update, firmware update, software upgrade, or wireless software upgrade. At least one of the first device or the second device is connected to communicate with at least one of a wired network or a wireless network. At least one of the first or second devices includes at least one of the following: an audible alarm generator, a dialogue generator, a conversation engine, a siren, a bell, a speaker, a status indicator, a path sound indicator, a position sound indicator, a light, a safety passage light, a colored light, an alarm light, a warning light, or a timed light. The processor controls the first device and the second device. Set the second device before the first device. Set the first device before the second device. The first device and the second device are configured simultaneously. It is further configured to be set in one of the following ways, The aforementioned processor, The first device, a location related to the first device, a region related to the first device, The second device, a location related to the second device, a region related to the second device, The pair of the first device and the second device, Locations related to the aforementioned pair, Region related to the aforementioned pair, Further configured to generate a label for at least one of the following: The aforementioned label is, User input to the system during the system setup stage, The TSCI, STI, task, or response after the setting step, It is generated based on at least one of the following: The aforementioned location is, A multi-floor structure, Multiple floors of the aforementioned multi-floor structure, The interior space of the structure The external space immediately adjacent to the aforementioned structure, It includes at least one of the following: The aforementioned processor, The generation of a presentation related to the monitoring of the user interface (UI) of the user device of the user of the system, and Acquisition of user input from the user via the aforementioned UI, A system further configured to perform the following actions.

16. The system according to claim 1, The aforementioned task is, To generate at least one of the following: a presentation, reminder, notification, report, interaction, exchange, or interaction based on at least one of the following: a scheduled event, unread message, unread news item, schedule, timetable, news from subscribed channels, user settings, event, alarm clock, or situation, or Changing the mode of the system based on at least one of the following: the movement of the object, the STI, the plan, the timetable, the state, the detected event, the recognized situation, or the trigger event. It includes at least one of the following: At least one of the presentation, reminder, notification, report, dialogue, exchange, interaction, or mode is communicated to at least one of the user's user device, the user's smartphone, the user's tablet, the user's computer, smart speaker, smart device with display, smart device with speaker, key fob, smartwatch, smart wearable, smart display, smart device, smart smoke detector, smart doorbell, smart TV, or miniature surveillance camera. A system in which at least one of the reminder, notification, report, dialogue, exchange, interaction, or mode is determined based on at least one of the analysis of a historical record of at least one of the following: time, day of the week, month and day, year and month and day, location of the user device, state of the system, prediction based on machine learning, or analysis of at least one of the following: TSCI, STI, or analysis calculated based on STI.

17. The system according to claim 1, wherein the processor is Filtering at least one of the TSCI, STI, or analyses calculated based on the TSCI or STI based on at least one of a threshold or parameter, Recognizing a pattern based on the TSCI or STI, To generate an SU account for a superuser (SU) to control and manage the said system with the highest level of authority and rights, To generate RU accounts for each regular user (RU) for controlling and managing the aforementioned system, The aforementioned SU, Entering the RU information related to the RU account, Assigning the respective authority levels and rights to the RU account for monitoring the movement of the object based on the TSCI and the STI, wherein each authority level and right is less than or equal to the highest authority level and right of the SU, or Assigning the RU account to access the aforementioned task or a subtask of the aforementioned task, A system further configured to set up each RU account based on at least one of the following.

18. The system according to claim 1, wherein the system is interoperable with a third-party system, The aforementioned processor, Sharing at least one of the following with the third-party system: the STI, the monitoring of the object's movement, the task information, or the response; A system further configured to enable or configure a device of the third-party system as an additional transmitter for transmitting additional radio signals to the receiver in the system.

19. The system according to claim 1, wherein the processor is Calculating the location of the movement of the object based on the TSCI and the STI, Based on the TSCI and STI, it is determined in real time whether or not there is object movement. A system further configured to generate a presentation of a history, trend, or temporal overview of at least one of the following related to a period: the STI, the task or the response, the monitoring of the movement, the location of the movement, and the analysis of the movement calculated based on the STI or the TSCI.

20. A method for a wireless monitoring system, Transmitting multiple radio signals asynchronously via a wireless multipath channel of a location, each radio signal being transmitted from one of the multiple first devices of the wireless monitoring system, The receiving of the plurality of radio signals via the radio multipath channel by a plurality of second devices of the radio monitoring system, wherein each received radio signal is different from the respective transmitted radio signal due to the radio multipath channel and the movement of objects at the location. Based on each received radio signal, each of the multiple time-series channel information (TSCI) of the radio multipath channel is acquired, Based on each TSCI, multiple spatiotemporal information (STI) is calculated, Monitoring the first state of the object's movement based on a first subset of the plurality of TSCIs and a first subset of the plurality of STIs, Monitoring the second state of the object's movement based on a second subset of the plurality of TSCIs and a second subset of the plurality of STIs, Performing a first task based on monitoring the first condition of the aforementioned movement, Performing a second task based on monitoring the second condition of the aforementioned movement, To generate a first response based on the first task, A method comprising generating a second response based on the second task described above.

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