Method, apparatus and system for periodic motion detection and monitoring - Patents.com

Abstract

To provide a method, a device, and a system for monitoring vital signs in real time.SOLUTION: There is provided a system for monitoring a movement repeated at a certain place. The system includes a transmitter, a receiver, and a repetitive movement monitor. The transmitter is positioned at a first position at the above place, and is configured to transmit a radio signal through a radio multipath channel influenced by the repetitive movement of an object at the place. The receiver is positioned at a second position at the place, and is configured to receive a radio signal through the radio multipath channel influenced by the repetitive movement of the object at the place. The repetitive movement monitor is configured to monitor a periodic feature of the repetitive movement of the object on the basis of a time sequence of the CI.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application incorporates by reference the entire disclosure of each of the following cases and claims priority to each of the following: U.S. Provisional Patent Application 62 / 593,826, entitled "Method, Apparatus, and System for Object Tracking and Navigation," filed December 1, 2017. U.S. Provisional Patent Application 62 / 678,207, entitled "Method, Apparatus and System for Object Tracking and Motion Monitoring," filed May 30, 2018; U.S. Patent Application 15 / 861,422, entitled "Method, Apparatus, Server, and System for Time Reversal Technology," filed January 3, 2018. U.S. Patent Application 15 / 873,806, entitled "Method, Apparatus, and System for Object Tracking and Navigation," filed January 17, 2018; U.S. Patent Application 16 / 101,444, entitled "Method, Apparatus, and System for Wireless Motion Monitoring," filed August 11, 2018; U.S. Provisional Application No. 62 / 734,224, entitled "Method, Apparatus and System for Wireless Sleep Monitoring," filed September 20, 2018; U.S. Provisional Application No. 62 / 744,093, entitled "Method, Apparatus and System for Wireless Proximity and Presence Monitor," filed October 10, 2018; U.S. Provisional Patent Application 62 / 753,017, entitled "Method, Apparatus, and System for Person Identification Based on Human Wireless Biometrics," filed October 30, 2018. U.S. Patent Application 16 / 200,608, filed November 26, 2018, entitled "Method, Apparatus, Server and System for Vital Signs Detection and Monitoring." U.S. Patent Application 16 / 200,616, filed November 26, 2018, entitled "Method, Apparatus, Server and System for Real-Time Vital Signs Detection and Monitoring." U.S. Patent Application 16 / 203,299, entitled "Apparatus, System, and Method for Event Recognition Based on Wireless Signals," filed November 28, 2018. U.S. Patent Application 16 / 203,317, entitled "Apparatus, System, and Method for Radio Signal-Based Falldown Detection," filed November 28, 2018.

[0002] The present teachings relate generally to periodic motion detection and estimation, and more particularly to vital signs detection and monitoring based on time reversal techniques in rich-scattering environments. [Background technology]

[0003] Regarding periodic motion detection, many important human vital signs, such as respiration, are periodic movements. Vital signs are important indicators of a person's health and well-being, as well as predictors of acute and chronic disease states. Breathing rate is one of the most important vital signs and can be measured in breaths and inhalations per minute. Furthermore, breathing patterns can be highly correlated with human psychological states, such as stress and anxiety.

[0004] Most traditional approaches to monitoring respiration are invasive in that they require physical contact with the human body. For example, in hospitals, patients must wear oxygen masks, nasal cannulas, chest straps, or wearable sensors such as thermistors and pressure sensors. Another example is polysomnography (PSG), used in sleep medicine, which typically requires 22 or more wire attachments to the patient. These dedicated devices are often expensive, bulky, and uncomfortable for the human body, limiting them to clinical settings. While these wired medical systems can measure respiration using wearables attached to the body, such systems are clumsy and uncomfortable. To make matters worse, many patients become anxious or bothered by all the wearables and wires they are wearing, and the system itself will distort the very respiration that needs to be monitored.

[0005] Current noninvasive (noncontact) respiratory monitoring solutions can be categorized as follows: (1) Radar-based respiratory monitors: Doppler radar is often used for respiratory monitoring. They operate by transmitting and receiving a signal with a Doppler shift due to the periodic motion of the object. The respiratory rate can be extracted from the Doppler shift. Disadvantages: These systems use high transmit power, rely on sophisticated and expensive hardware, and use very large bandwidths. A vital signs monitoring system utilizing frequency-modulated continuous wave (FMCW) radar has been disclosed. It used a universal software radio peripheral as the RF front end to transmit frequency-swept signals. However, the additional cost and complexity of dedicated hardware limited the large-scale deployment of FMCW radar. (2) Wireless sensor-based respiratory monitors: Received signal strength (RSS) measurements from 802.15.4-compliant sensors on multiple 802.15.4 channels were also used for respiration detection and respiratory rate estimation. These methods require additional wireless infrastructure, such as dense deployment of wireless sensors. Furthermore, a specific design of the frequency-hopping mechanism is required to support multiple channel measurements. (3) Wi-Fi-based respiratory monitors. RSS is commonly used in Wi-Fi-based respiratory monitors because it is available in most commercial Wi-Fi network interface controllers (NICs). Measurements have also been used in Wi-Fi devices for respiration estimation. However, this method is only accurate when the user places the Wi-Fi-enabled device very close to their chest.

[0006] In addition to the above drawbacks, methods (1) and (2) require the design and manufacture of specialized equipment, such as dedicated radar equipment and sensor network nodes, while method (3) has very low accuracy and sensitivity. Wireless respiratory monitoring systems that monitor respiratory rate and heart rate based on ultra-wideband (UWB) signals have been proposed. However, UWB-based systems have many limitations, including expensive, untested, rare, proprietary, and limited-edition experimental hardware components, a small range due to severe absorption of UWB signals by walls, operation only under line-of-sight (LOS) conditions, the cumbersome and labor-intensive task of placing many sensors in practical locations to cover a reasonable area, and the high cost of installation, maintenance, and repair due to the limited LOS operation. Therefore, a method and apparatus for detecting and monitoring vital signs is needed to solve the above problems and avoid the above drawbacks.

[0007] Nowadays, object (or substance) movement detection is becoming more and more important. For example, for security and / or management purposes, a user may want to detect the movement of any object in the user's house, a supermarket manager may want to detect the movement of any object in the supermarket, a hospital nurse may want to detect the movement of any patient in the hospital.

[0008] Existing systems and methods for detecting object movement lack sufficient accuracy and often lead to false alarms. Existing approaches include passive infrared (PIR), active infrared (AIR), and ultrasonic-based 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 moving people and the background heat. However, PIR sensor-based solutions are prone to false alarms due to their sensitivity to environmental changes such as hot / cold air currents and sunlight. They are easily defeated by blocking body heat emissions (such as wearing an insulated full-body suit). Furthermore, their range is limited and line of sight (LOS) is required, necessitating the use of multiple devices. In AIR-based approaches, an IR emitter transmits an IR beam that is received by an IR receiver. When the beam is interrupted, motion is detected. However, this type of approach is easily visible using a conventional camera or IR detection mechanism, has a limited range, and requires line of sight. Ultrasonic sensors detect human movement by sending ultrasonic waves into space and measuring their return velocity, detecting any frequency changes. However, such approaches are compromised by wearing anechoic clothing. Also, ultrasound cannot penetrate solid objects such as furniture or boxes, creating gaps in the detection field. Even slow movements by a thief may not trigger an alarm. Therefore, existing systems and methods for detecting object movement are not entirely satisfactory. Summary of the Invention

[0009] The present teachings generally relate to periodic motion (e.g., human breathing) detection. More specifically, the present teachings relate to periodic motion detection and monitoring based on time reversal techniques in indoor environments or rich-scattering wireless environments, such as urban metropolitan areas, enclosed environments, underground environments, outdoor locations with barriers, such as parking lots, warehouses, gardens, squares, forests, caves, valleys, etc.

[0010] In one example, a method / apparatus / device / server / client / system for a periodic motion monitoring system uses a processor, a memory communicatively coupled to the processor, and a set of stored instructions to acquire at least one TSCI of a wireless multipath channel. The at least one TSCI is extracted from a wireless signal transmitted over the wireless multipath channel between a type 1 heterogeneous wireless device at a first location within a venue and a type 2 heterogeneous wireless device at a second location within the venue. It is determined that at least a portion of the at least one TSCI within a current sliding time window is associated with current quasi-periodic motion of an object within the venue. The wireless multipath channel is affected within the current sliding time window by the current quasi-periodic motion of the object.

[0011] A current feature of an object within a current sliding time window is calculated based on at least a portion of at least one TSCI within the current sliding time window, another at least a portion of at least one TSCI within a past sliding time window, and at least one of past features of the object within a past sliding time window. At least one TSCI is preprocessed. For each original CI of a particular portion of a particular TSCI within the current sliding time window, N1 frequency-domain components of the original CI may be transformed into N2 time-domain coefficients of the same CI using an inverse frequency transform. Each CI may be associated with an original timestamp. N2 must not be smaller than N1. N2 may be a power of two to enable fast transforms, such as 16, 32, 64, 128, 256, 512, 1024, 2048, etc. Each of the N2 coefficients may be associated with a time delay. In the case of multiple Type 1 devices and / or multiple Type 2 devices, N1 and N2 may be the same or different for different devices. The original timestamps of all original CIs of a particular part of a particular TSCI may be irregular and modified within the current sliding time window, so that the modified timestamps of the time-modified CIs may be uniformly spaced in time. In the case of multiple Type 1 devices and / or multiple Type 2 devices, the modified timestamps may be with respect to the same or different clocks.

[0012] For each of the N time-domain coefficients of a time-corrected CI to be calculated at a particular corrected timestamp, the time-domain coefficients of the time-corrected CI at the particular corrected timestamp may be calculated based on the time-domain coefficients of the original CI at the original timestamp. The timestamp correction may be achieved by calculating the corresponding CI (i.e., the corresponding N time-domain coefficients of each CI) at the corrected (target) timestamp. Interpolation, filtering, and / or weighted averaging may be applied to calculate the CI.

[0013] A specific portion of a specific TSCI within the current sliding time window may be determined to be associated with the current quasi-periodic motion of an object within the location. The specific portion may be those CIs corresponding to the current sliding time window. The specific portion may include N0 CIs. N1 frequency-domain components are obtained for each of the N0 CIs. A matrix G1 of size N1xN0 may be determined, where each of the N0 columns includes a respective N1 frequency element for each CI.

[0014] An original timestamp associated with each CI can be determined. The original timestamps may not be uniformly spaced in time. The original timestamps of all CIs in a particular portion of a particular TSCI in the current sliding time window may be modified so that the modified timestamps of the time-modified CIs are uniformly spaced in time. An operation may be performed on the time-modified CIs with respect to the modified timestamps. A current feature of the object may be calculated based on the output of the operation. For a particular frequency corresponding to one of the N1 frequency components, a time-domain signal may be formed by grouping the particular frequency component of all the time-modified CIs. Then, for the particular frequency, an operation may be applied to the time-domain signal. This may be repeated for all frequencies. Different operations may be applied to different frequencies.

[0015] Spectral analysis of the output of the operation may be performed in the frequency domain. For each original CI of a particular portion of a particular TSCI within the current sliding time window, a frequency transform may be applied to the N2 processed time-domain coefficients to convert the N2 time-domain coefficients into N2 frequency-domain coefficients. The N2 frequency-domain coefficients may be further processed. The at least one TSCI may correspond to various antenna pairs between the Type 1 device and the Type 2 device. The Type 1 device may have at least one antenna. The Type 2 device may also have at least one antenna. Each of the at least one TSCI may be associated with one of the at least one antennas of the Type 1 device and one of the at least one antennas of the Type 2 device. The averaging over antenna links is averaging over at least one TSCI.

[0016] Averaging may be optionally performed on at least one subset of TSCIs corresponding to a subset of antenna pairs. After averaging, a time-domain signal of length N0 is obtained. A frequency transform can be applied to the time-domain signal of length N0 to convert it to the frequency domain. The size of the transform can be an integer power of two greater than or equal to N0. Long-trace peaks should be selected in preference to short-trace peaks, even if they are slightly weaker than short-trace peaks. In particular, short-trace peaks with strong "intensity" may be caused by interference or noise. This is similar to impulse noise, with short duration and very large intensity / energy. Target periodic motion may be long-term behavior and should not have a short duration / trace. The decision threshold (i.e., the threshold for making a decision) can be calculated adaptively (e.g., based on a finite state machine).

[0017] The current features of the object may be made available in real time with or without a time delay (e.g., to software, a software module, a hardware module, a processor, another processor, another device, a remote device, a cloud device) and / or non-real time. The current sliding time window may move by a shift size as time passes. The residual spectral energy may be calculated by subtracting the spectral energy associated with a selected set of significant local peaks from the total spectral energy. An event related to the current features of the object may be detected based on at least one of the residual spectral energy, an adaptive threshold, and a finite state machine.

[0018] The decision threshold (i.e., the threshold for making a decision) can be adaptively calculated based on a finite state machine (FSM). The thresholds (e.g., T1 and T2) can be adaptively calculated based on a finite state machine. The finite state machine can include an initial (INIT) state, a verification state, a PeakFound state, and / or a motion state.

[0019] The INIT state of the finite state machine (FSM) can be entered in at least one predetermined way. A threshold can be adaptively calculated in the INIT state in a first way. An event can be detected based on the threshold. The INIT state may transition to a different state based on at least one transition criterion. The threshold can be adaptively calculated in another way in a different state. In the INIT state, the threshold can be adaptively calculated in a first way. An event can be detected (i.e., if it occurs, a detection operation is applied in an attempt to detect the event) based on the threshold and a selected set of significant local peaks and / or related features. Excessive background interferometric motion can be detected (i.e., a detection operation is applied to detect any excessive background interferometric motion, if any) based on the threshold and the remaining spectral energy. When it is concluded that an event is "not detected" and excessive background interferometric motion is "not detected," the INIT state can remain unchanged. When it is concluded that an event has been previously "detected" and the previously detected event needs to be verified, the INIT state may transition to a verification state. If it is concluded that excessive background interfering motion has been "detected", the INIT state can transition to the motion state.

[0020] In the verification state, the threshold may be adaptively calculated in a second manner. At least one statistic (e.g., a count of consecutive "detected" events or a percentage of "detected" events in N consecutive time instances) may be accumulated and calculated based on a set of selected significant local peaks and associated features in at least one adjacent sliding time window. The at least one statistic may be for verifying the pre-detected event. For example, detection may be applied to each of at least one adjacent sliding time window. The statistic may be a count of the number of times the pre-detected event continues to be concluded as "detected." The verification state may remain unchanged while the at least one statistic (e.g., a count of consecutive "detected" events or a percentage of "detected" events in N consecutive time instances) is accumulated until sufficient statistics for verification can be collected. Based on the threshold and the at least one statistic, the pre-detected event may be verified (i.e., an operation may be applied to verify the pre-detected event). If the pre-detected event is concluded to be "verified," the verification state may transition to a PeakFound state. When the verification can be concluded as "not verified," the verification state can transition to the INIT state. The verification state can remain unchanged if the verification cannot be concluded. For example, a total of N consecutive time instances can be concluded as "detected," or a percentage of "detected" in N consecutive time instances can be concluded as "verified" if it is greater than a certain threshold.

[0021] In the peak found state, the threshold may be adaptive in a third way. A verified event (i.e., an event verified immediately before entering the peak found state) may be detected based on a threshold and a selected set of significant local peaks and / or associated features. Excessive background interfering motion may be detected based on a threshold and remaining spectral energy. If the verified event is concluded to be "verified," the peak found state may remain unchanged. When the verified event can be concluded to be "not detected" for multiple time instances, the peak found state may transition to the INIT state. When excessive background interfering motion can be concluded to be "detected" for multiple time instances (e.g., three times), the peak found state may transition to the motion state.

[0022] In the motion state, the threshold may be adaptively calculated in a fourth manner. Excessive background interfering motion may be detected based on the threshold and the remaining spectral energy. When excessive background interfering motion is concluded to be "detected," the motion state may remain unchanged. When excessive background interfering motion may be concluded to be "not detected" for multiple time instances, the motion state may transition to the INIT state. When excessive background interfering motion is concluded to be "not detected" for multiple time instances, the motion state may also transition to a previous state. The previous state is the state from which the current motion state started.

[0023] In one embodiment, a system for monitoring movement of an object within a location is disclosed. The system includes a transmitter, a receiver, and a vital sign estimator. The transmitter is located at a first location within the location and configured to transmit a wireless signal over a wireless multipath channel affected by quasi-periodic movement of an object within the location. The receiver is located at a second location within the location and configured to receive the wireless signal over the wireless multipath channel affected by the quasi-periodic movement of an object within the location and acquire at least one TSCI of the wireless multipath channel in the wireless signal. The vital sign estimator is configured to determine that at least a portion of at least one TSCI within a current sliding time window is associated with quasi-periodic movement of an object within the location based on at least a portion of at least one TSCI within a current sliding time window, at least a portion of at least one TSCI within a past sliding time window, and at least one of past features associated with the quasi-periodic movement of the object within the past sliding time window, and calculate current features associated with the quasi-periodic movement of the object in the current sliding time window.

[0024] In another embodiment, a method for monitoring movement of objects within a location is disclosed, the method including receiving a wireless signal over a wireless multipath channel affected by quasi-periodic movement of objects within the location, acquiring at least one TSCI of the wireless multipath channel based on the wireless signal, determining that at least a portion of the at least one TSCI (TSCI) within a current sliding time window is associated with the quasi-periodic movement of the objects within the location, and calculating a current feature associated with the quasi-periodic movement of the objects in the current sliding time window based on at least a portion of the at least one TSCI within the current sliding time window, at least a portion of the at least one TSCI within a past sliding time window, and at least one of past features associated with the quasi-periodic movement of the objects in the past sliding time window.

[0025] In yet another embodiment, a receiver for a motion monitoring system is disclosed. The motion monitoring system includes a transmitter, a receiver, and a vital sign estimator. The receiver includes a radio circuit, a processor communicatively coupled to the radio circuit, a memory communicatively coupled to the processor, and a set of instructions stored in the memory. The radio circuit is configured to receive radio signals over a wireless multipath channel affected by quasi-periodic motion of objects within a location, the radio signals being asynchronously transmitted by the transmitter. The set of instructions, when executed, cause the processor to obtain at least one TSCI of the wireless multipath channel based on the radio signals. At least a portion of the at least one TSCI (TSCI) within a current sliding time window is associated with the quasi-periodic motion of objects within the location. At least a portion of the at least one TSCI in the current sliding time window is used by the vital signs estimator to calculate current features related to the quasi-periodic motion of the object in the current sliding time window based on at least a portion of the at least one TSCI in the current sliding time window, at least a portion of the at least one TSCI in the past sliding time window, and at least one of past features related to the quasi-periodic motion of the object in the past sliding time window.

[0026] In yet another embodiment, an estimator for a motion monitoring system is disclosed. The motion monitoring system includes a transmitter, a receiver, and an estimator. The estimator includes a processor, a memory communicatively connected to the processor, and a set of instructions stored in the memory. When executed, the set of instructions cause the processor to: acquire at least one TSCI of a wireless multipath channel from the motion monitoring system receiver, the receiver extracting the at least one TSCI from a wireless signal received from the motion monitoring system transmitter over the wireless multipath channel affected by quasi-periodic motion of an object within a location; determine that at least a portion of the at least one TSCI within a current sliding time window is associated with the quasi-periodic motion of the object within the location; and calculate current features associated with the quasi-periodic motion of the object in the current sliding time window based on at least a portion of the at least one TSCI within the current sliding time window, at least a portion of the at least one TSCI within a past sliding time window, and at least one of past features associated with the quasi-periodic motion of the object within the past sliding time window.

[0027] In another embodiment, a system for monitoring repetitive motion within a location is disclosed. The system includes a transmitter, a receiver, and a repetitive motion monitor. The transmitter is disposed at a first location within the location and configured to transmit a wireless signal over a wireless multipath channel affected by the repetitive motion of an object within the location. The receiver is disposed at a second location within the location and receives the wireless signal over the wireless multipath channel affected by the repetitive motion of the object within the location, and obtains a time series correlation index (TSCI) of the wireless multipath channel based on the wireless signal. The repetitive motion monitor is configured to monitor a periodic characteristic of the repetitive motion of the object based on the TSCI.

[0028] In another embodiment, a method for monitoring repetitive motion within a location is disclosed, the method including receiving a wireless signal over a wireless multipath channel affected by the repetitive motion of an object within the location, obtaining a time series correlation index (TSCI) of the wireless multipath channel based on the wireless signal, and monitoring a periodic characteristic of the repetitive motion of the object based on the TSCI.

[0029] In yet another embodiment, a receiver for a motion monitoring system is disclosed. The motion monitoring system includes a transmitter, a receiver, and a repetitive motion monitor. The receiver includes a radio circuit, a processor communicatively coupled to the radio circuit, a memory communicatively coupled to the processor, and a set of instructions stored in the memory. The radio circuit is configured to receive a radio signal over a wireless multipath channel affected by repetitive motion of an object within a location. The radio signal is transmitted by the transmitter. The set of instructions, when executed, cause the processor to obtain a time series correlation index (TSCI) of the wireless multipath channel based on the radio signal. The TSCI is used by the repetitive motion monitor of the motion monitoring system to monitor periodic characteristics of the repetitive motion.

[0030] In yet another embodiment, a monitor for a motion monitoring system is disclosed. The motion monitoring system includes a transmitter, a receiver, and a monitor. The monitor includes a processor, a memory communicatively connected to the processor, and a set of instructions stored in the memory. The set of instructions, when executed, cause the processor to obtain TSCI of a wireless multipath channel from a receiver of the motion monitoring system, the receiver extracting the TSCI from a wireless signal received from the transmitter of the motion monitoring system over the wireless multipath channel affected by repetitive motion of an object at a location, and monitoring a periodic characteristic of the repetitive motion of the object based on the TSCI.

[0031] Another concept relates to software for implementing the present teachings for wireless CI-based vital signs and other periodic motion detection and monitoring in rich-scattering environments.

[0032] The present teachings generally relate to object motion detection and monitoring, and more particularly, to wireless CI-based object motion detection and monitoring in rich scattering environments, such as indoor environments or outdoor locations with barriers, such as metropolitan areas, closed environments, underground environments, parking lots, storage areas, gardens, squares, forests, caves, valleys, etc.

[0033] In one embodiment, a system is disclosed having at least one processor and a memory having stored therein a set of instructions for detecting object movement within a location, the system comprising: a first wireless device configured to transmit a wireless signal over a wireless multipath channel affected by object movement within the location; a second wireless device of a different type from the first wireless device configured to receive the wireless signal over the wireless multipath channel affected by object movement within the location and to obtain a time series correlation index (TSCI) of the wireless multipath channel based on the wireless signal; and a motion detector configured to detect object movement within the location based on motion information related to the object movement, wherein the motion information associated with the first and second wireless devices is calculated based on the TSCI by at least one of the motion detector and the second wireless device.

[0034] In yet another embodiment, a wireless monitoring system method is disclosed. The wireless monitoring system includes at least one asynchronous heterogeneous Type 1 wireless device and at least one asynchronous heterogeneous Type 2 wireless device. The wireless monitoring system includes at least one pair of Type 1 and Type 2 devices. Each pair includes one of the at least one asynchronous heterogeneous Type 1 wireless device and one of the at least one asynchronous heterogeneous Type 2 wireless device. Each of the at least one Type 2 wireless device is in a respective specific pair of at least one Type 1 and Type 2 device. Each Type 2 wireless device is associated with at least one respective specific Type 1 wireless device via at least one respective specific pair of Type 1 and Type 2 devices. For each Type 2 wireless device and for each of at least one respective specific pair of Type 1 and Type 2 devices comprising a Type 2 wireless device, the method includes asynchronously obtaining at least one respective TSCI of a wireless multipath channel affected by object movement within a location, wherein the at least one respective TSCI (TSCI) associated with the particular pair of Type 1 and Type 2 wireless devices is asynchronously extracted from a respective asynchronous heterogeneous wireless signal, and the respective asynchronous heterogeneous wireless signal is asynchronously transmitted from a respective one of the at least one respective specific Type 1 wireless device to at least one asynchronous heterogeneous Type 2 wireless device over the wireless multipath channel using a respective first heterogeneous processor, a respective first heterogeneous memory, and a respective first heterogeneous instruction set of the Type 1 wireless device.The method further includes at least one of using a third processor, a third memory, and a third instruction set of the Type 3 device to individually and asynchronously monitor movement of objects within a location based on TSCIs associated with a pair of Type 1 and Type 2 devices that includes a specific Type 2 device; jointly and asynchronously monitoring movement of objects based on TSCIs associated with any of at least one pair of Type 1 and Type 2 devices associated with the specific Type 2 wireless device; jointly and asynchronously monitoring movement of objects based on TSCIs associated with any of at least one pair of Type 1 and Type 2 devices associated with one of the at least one respective specific Type 1 wireless devices; and collectively and asynchronously monitoring movement of objects based on TSCIs associated with any of at least one pair of Type 1 and Type 2 devices.

[0035] In yet another embodiment, a specific asynchronous heterogeneous Type 2 device of a wireless monitoring system is disclosed. The wireless monitoring system includes at least one asynchronous heterogeneous Type 1 device and at least one asynchronous heterogeneous Type 2 device. The wireless monitoring system includes at least one pair of Type 1 and Type 2 devices, each pair including at least one Type 1 device and at least one Type 2 device. The specific asynchronous heterogeneous Type 2 device is at least one specific pair of Type 1 and Type 2 devices of the wireless monitoring system. Each Type 2 device is grouped with at least one specific Type 1 wireless device via at least one respective specific pair of Type 1 and Type 2 devices.a second heterogeneous processor communicatively coupled to the second heterogeneous processor; and a second heterogeneous memory communicatively coupled to the second heterogeneous processor, and a second heterogeneous instruction set stored in the second heterogeneous memory, which, when executed, causes the second heterogeneous processor to asynchronously protect at least one respective set of CIs (TSCIs) of the wireless multipath channel for each of at least one particular pairing of Type 1 and Type 2 devices, and to protect at least one respective set of CIs (TSCIs) of the wireless multipath channel for each of at least one particular pairing of Type 1 and Type 2 devices, and to protect at least one respective set of CIs (TSCIs) of the wireless multipath channel for each of at least one particular pairing of Type 1 and Type 2 devices. and a second heterogeneous instruction set stored in a second heterogeneous memory, the TSCIs being asynchronously derived from the respective asynchronous heterogeneous radio signals asynchronously received by the radio circuit, wherein the movement of the mass within the site is monitored individually and asynchronously based on TSCIs associated with pairings of Type 1 and Type 2 devices that include a particular Type 2 device using a third processor, third memory, and third instruction set of the Type 3 device; the movement of the mass is monitored jointly and asynchronously based on TSCIs associated with any pairings of Type 1 and Type 2 devices that include one of the at least one particular Type 1 device; and the movement of the mass is monitored collectively and asynchronously based on TSCIs associated with any pairings of Type 1 and Type 2 devices.

[0036] In yet another embodiment, a specific asynchronous heterogeneous Type 1 device of a wireless monitoring system is disclosed. The wireless monitoring system includes at least one asynchronous heterogeneous Type 1 device and at least one asynchronous heterogeneous Type 2 device. The wireless monitoring system includes at least one combination of Type 1 and Type 2 devices, each combination including at least one Type 1 device and at least one Type 2 device. The specific asynchronous heterogeneous Type 1 device is at least one specific combination of Type 1 and Type 2 devices of the wireless monitoring system. The specific Type 1 device is paired with at least one specific Type 2 device via at least one specific combination of Type 1 and Type 2 devices. The specific asynchronous heterogeneous Type 1 device includes a radio circuit, a first processor communicatively coupled to the radio circuit, a first memory communicatively coupled to the first processor, and a first instruction set stored in the first memory. The first set of instructions, when executed, causes the first processor to use the radio circuitry to asynchronously transmit asynchronous heterogeneous radio signals from the specific type 1 device to at least one specific type 2 device over a wireless multipath channel affected by material movement within the site, and for each of the at least one specific type 2 device, at least one series of CIs (TSCIs) of the wireless multipath channel collected from the asynchronous heterogeneous radio signals transmitted by the radio circuitry is fetched by the type 2 device using a second processor, a second memory, and a second set of instructions of the type 2 device.The movement of material within the site is tracked individually and asynchronously based on TSCIs associated with any combination of Type 1 and Type 2 devices, including a specific Type 1 device, using a third processor, third memory, and third instruction set of the Type 3 device; the movement of material is tracked jointly and asynchronously based on TSCIs associated with any combination of Type 1 and Type 2 devices, including a specific Type 1 device; the movement of material is tracked jointly and asynchronously based on TSCIs associated with any combination of Type 1 and Type 2 devices, including one of at least one specific Type 2 device; and the movement of material is tracked collectively and asynchronously based on TSCIs associated with any combination of Type 1 and Type 2 devices.

[0037] In yet another embodiment, a particular asynchronous heterogeneous Type 1 device of a wireless monitoring system is disclosed. The wireless monitoring system includes at least one asynchronous heterogeneous Type 1 device and at least one asynchronous heterogeneous Type 2 device. The wireless monitoring system includes at least one doublet of Type 1 and Type 2 devices, each doublet including one of the at least one asynchronous heterogeneous Type 1 device and one of the at least one asynchronous heterogeneous Type 2 device. The particular asynchronous heterogeneous Type 3 device includes a third processor communicatively coupled to at least one of the at least one asynchronous heterogeneous Type 1 device and the at least one asynchronous heterogeneous Type 2 device, a third memory communicatively coupled to the third processor, and a third instruction set stored in the third memory.The third instruction set, when executed, causes the third processor to, for each Type 2 device, and for each of at least one doublet of Type 1 and Type 2 devices that includes the Type 2 device, asynchronously receive, using a respective second processor, a respective second memory, and a respective second instruction set of the Type 2 device, at least one respective TSCI of a wireless multipath channel affected by movement of an item at a location that is asynchronously received by the Type 2 device, wherein the at least one respective TSCI associated with each doublet of the Type 1 and Type 2 devices transmits, over the wireless multipath channel, from a respective Type 1 device of the respective doublet to at least one asynchronous heterogeneous Type 2 device using a respective first processor, a respective first memory, and a respective first instruction set of the respective Type 1 device. and causing the third processor to at least one of individually and asynchronously track the movement of an item at a location based on TSCIs associated with a particular doublet of Type 1 and Type 2 devices, including a particular Type 2 device and a particular Type 1 device; jointly and asynchronously track the movement of an item based on TSCIs associated with any of at least one doublet of Type 1 and Type 2 devices associated with a particular Type 2 device; jointly and asynchronously track the movement of an item based on TSCIs associated with any of at least one doublet of Type 1 and Type 2 devices associated with a particular Type 1 device; and collectively and asynchronously track the movement of an item based on TSCIs associated with any of at least one doublet of Type 1 and Type 2 devices.

[0038] Other novel features are set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by the manufacture or operation of the embodiments. The novel features of the present teachings may be realized or attained by practice or use of various aspects of the methodology, instrumentalities and combinations which are described in the detailed embodiments discussed below. [Brief explanation of the drawings]

[0039] [Figure 1] Figure 1 shows the floorplan of an experiment according to one embodiment of the present invention: (a) One person, NLOS (b) One person, NLOS, fan operation (c) Two people, NLOS (d) Three people, NLOS. [Figure 2] FIG. 1 illustrates state transitions of an FSM scheme with a running fan, according to one embodiment of the present invention. [Figure 3] FIG. 10 illustrates estimation of respiration while a subject is standing for 5 seconds according to an embodiment of the present invention. [Figure 4] FIG. 1 illustrates an exemplary flow for acquiring a vital signs monitor (respiratory rate estimation) using time reversal techniques, according to an embodiment of the present invention. [Figure 5] FIG. 1 illustrates an FFT-based respiration tracking algorithm, according to an embodiment of the present invention, whose proposed architecture consists of the following steps: [Figure 6] FIG. 10 illustrates filter coefficients for a CFR sampling rate of 10 Hz and a passband of 0.133 to 0.7 Hz with 81 taps, according to one embodiment of the present invention. [Figure 7A] 1A and 1B illustrate CIR variation over time at one channel tap (a) before bandpass filtering (b) after bandpass filtering, in accordance with one embodiment of the present invention. [Figure 7B] 1A and 1B illustrate CIR variation over time at one channel tap (a) before bandpass filtering (b) after bandpass filtering, in accordance with one embodiment of the present invention. [Figure 8]2-D spectrum diagram of a respiratory signal across all CIR taps on one antenna link, according to one embodiment of the present invention. [Figure 9] FIG. 4 illustrates a spectrum after performing link averaging, according to one embodiment of the present invention. [Figure 10] FIG. 10 illustrates a procedure for an FSM associated with different people selecting peaks, according to one embodiment of the present invention. [Figure 11] FIG. 1 illustrates an FSM for tracking a person's respiration, according to one embodiment of the present invention. [Figure 12] FIG. 10 shows the results of respiration tracking for a single-person NLOS scenario (a) with FSM (b) without FSM, according to an embodiment of the present invention. [Figure 13] FIG. 10 illustrates a comparison with and without an FSM with the fan running, in accordance with one embodiment of the present invention. [Figure 14] FIG. 10 illustrates CDF performance when operating the fan in a single-occupancy NLOS scenario with a breathing rate of 18 BPM, according to an embodiment of the present invention. [Figure 15] FIG. 10 illustrates CDF performance when operating the fan under a single-occupancy NLOS scenario with a breathing rate of 20 BPM, according to an embodiment of the present invention. [Figure 16] FIG. 10 illustrates respiration rate estimation for two people with FSM under (a) C_window=300 (b) C_window=450 (c) C_window=600 according to an embodiment of the present invention. [Figure 17] FIG. 10 illustrates two-person respiration rate estimation without FSM under (a) C_window=300 (b) C_window=450 (c) C_window=600 according to an embodiment of the present invention. [Figure 18] FIG. 1 illustrates an exemplary method for object tracking, according to one embodiment of the present invention. [Figure 19] FIG. 2 is a diagram illustrating an example of spatiotemporal information according to an embodiment of the present invention. [Figure 20] 1A and 1B illustrate examples of tasks performed based on spatiotemporal information, according to one embodiment of the present invention. [Figure 21] FIG. 4 illustrates another exemplary method for object tracking, according to one embodiment of the present invention. [Figure 22] 1 illustrates an exemplary presentation of spatiotemporal information at a location, according to one embodiment of the present invention, and also illustrates an exemplary scenario in which object motion is detected based on spatiotemporal information within a location, according to one embodiment of the present invention. [Figure 23] 4A and 4B show illustrations of antenna matching based on velocity estimation, in accordance with one embodiment of the present invention; [Figure 24] 3 is a flow chart illustrating a method for antenna matching based on velocity estimation, in accordance with one embodiment of the present invention. [Figure 25] 3 is a flowchart of an exemplary method for motion detection, according to one embodiment of the present invention. [Figure 26] 3 is an exemplary block diagram of a first wireless device, in accordance with one embodiment of the present invention. [Figure 27] 4 is a flowchart of an exemplary method performed by a first wireless device, according to one embodiment of the present invention. [Figure 28] 4 is an exemplary block diagram of a second wireless device, in accordance with one embodiment of the present invention. [Figure 29] 4 is a flowchart of an exemplary method performed by a second wireless device, in accordance with one embodiment of the present invention. [Figure 30] 3 illustrates an exemplary motion detector, according to one embodiment of the present invention. [Figure 31] 4 illustrates an exemplary motion detection algorithm, according to one embodiment of the present invention. [Figure 32] 1 illustrates an exemplary method for object motion detection, according to one embodiment of the present invention. [Figure 33] 1 is an illustrative flowchart for an object motion localization method according to one embodiment of the present invention. [Figure 34] 1 illustrates an exemplary flow for detecting indoor events using time reversal techniques, according to one embodiment of the present invention. [Figure 35]FIG. 1 illustrates an exemplary network environment for detecting and monitoring vital signs within a location, according to one embodiment of the present invention. [Figure 36] 1 is an exemplary block diagram of a system for vital signs detection and monitoring, according to one embodiment of the present invention. [Figure 37] 1 is an exemplary block diagram of a vital signs estimator for vital signs detection and monitoring, according to one embodiment of the present invention; [Figure 38] 1 is a flowchart of an exemplary method for vital signs detection and monitoring, according to one embodiment of the present invention. [Figure 39] FIG. 1 illustrates an exemplary finite state machine (FSM) for a single-person respiratory monitor, according to one embodiment of the present invention. [Figure 40] FIG. 1 illustrates an exemplary procedure for an FSM relating to different people selecting peaks, according to one embodiment of the present invention. [Figure 41] 1 is a flowchart of an exemplary method for vital signs estimation, according to one embodiment of the present invention. [Figure 42] FIG. 1 illustrates an exemplary system state controller, which is an FSM, for a single-user respiratory monitor, in accordance with one embodiment of the present invention. [Figure 43] FIG. 1 illustrates exemplary state transitions of an FSM for a single-person respiratory monitor, in accordance with one embodiment of the present invention. [Figure 44] FIG. 10 illustrates exemplary experimental results for a comparison between a scheme with and without an FSM, with a fan running, in accordance with an embodiment of the present invention. [Figure 45] FIG. 2 illustrates an exemplary state transition of an FSM scheme with a running fan, in accordance with one embodiment of the present invention. [Figure 46] FIG. 10 illustrates an exemplary respiration estimation while a subject stood up for 5 seconds, according to an embodiment of the present invention. [Figure 47] FIG. 1 illustrates an exemplary method for vital signs detection and monitoring, according to one embodiment of the present invention. [Figure 48] 1 is an exemplary block diagram of a repeating motion monitor, according to one embodiment of the present invention; [Figure 49] 1 is a flowchart of an exemplary method for estimating and monitoring repetition rate (e.g., respiratory rate), according to an embodiment of the present invention. [Figure 50] 5A-5C illustrate exemplary autocorrelation functions of a received signal under different scenarios, in accordance with an embodiment of the present invention. [Figure 51] FIG. 10 illustrates exemplary features extracted from a derived autocorrelation function for respiration detection and estimation, according to one embodiment of the present invention. [Figure 52] FIG. 1 illustrates an exemplary scheme for extraction and maximization of a respiratory signal, according to an embodiment of the present invention. [Figure 53] 1 illustrates an exemplary procedure for repetition rate (e.g., breathing rate) estimation for multiple people, according to an embodiment of the present invention. [Figure 54] 1 is an exemplary diagram of devices in a motion monitoring system, according to one embodiment of the present invention. [Figure 55] FIG. 1 illustrates an exemplary respiration signal based on real-world measurements, according to an embodiment of the present invention. [Figure 56] 10A-10C illustrate the benefits of the disclosed scheme for extraction and maximization of a respiratory signal, in accordance with an embodiment of the present invention. [Figure 57A] 4A and 4B illustrate comparison results between wake and sleep states according to one embodiment of the present invention. [Figure 57B] 4A and 4B illustrate comparison results between wake and sleep states according to one embodiment of the present invention. [Figure 58A] 10A-10C illustrate respiration rate performance in different sleep stages, according to one embodiment of the present teachings. [Figure 58B] 10A-10C illustrate respiration rate performance in different sleep stages, according to one embodiment of the present teachings. [Figure 59] 1 illustrates an exemplary network environment for a sleep monitor, according to one embodiment of the present invention. [Figure 60] FIG. 1 illustrates an exemplary algorithm design for a sleep monitor, according to one embodiment of the present invention. [Figure 61]1 is an exemplary interior bird's-eye view of a motor vehicle for seat occupancy detection and people counting according to an embodiment of the present invention; [Figure 62A] 10A-10C illustrate the change in CI due to different seat occupancy situations in a vehicle, according to one embodiment of the present teachings. [Figure 62B] 10A-10C illustrate the change in CI due to different seat occupancy situations in a vehicle, according to one embodiment of the present teachings. [Figure 62C] 10A-10C illustrate the change in CI due to different seat occupancy situations in a vehicle, according to one embodiment of the present teachings. [Figure 62D] 10A-10C illustrate the change in CI due to different seat occupancy situations in a vehicle, according to one embodiment of the present teachings. DETAILED DESCRIPTION OF THE INVENTION

[0040] In the following detailed description, numerous specific details are set forth by way of example to provide a thorough understanding of the relevant teachings. However, it will be apparent to those skilled in the art that the present teachings may be practiced without such details. In other instances, well-known methods, procedures, components, and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present teachings.

[0041] Detection and estimation of periodic movements (e.g., breathing) The present invention relates generally to periodic motion detection and estimation. More specifically, the present teachings relate to periodic motion detection and monitoring based on time reversal techniques in indoor environments or outdoor locations with barriers such as urban metropolitan areas, closed environments, underground environments, parking lots, warehouses, gardens, squares, forests, caves, valleys, etc., rich and scattering wireless environments.

[0042] In one example, a method / apparatus / device / server / client / system for monitoring periodic motion includes acquiring at least one TSCI of a wireless multipath channel using a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory. The at least one TSCI is extracted from a wireless signal transmitted over the wireless multipath channel between a type 1 heterogeneous wireless device at a first position within a location and a type 2 heterogeneous wireless device at a second position within the location. It is determined that at least a portion of the at least one TSCI within a current sliding time window is associated with current quasi-periodic motion of an object within the location. The wireless multipath channel is affected within the current sliding time window by the current quasi-periodic motion of the object.

[0043] A current characteristic of the object within the current sliding time window is calculated based on at least one of at least a portion of at least one TSCI within the current sliding time window, another at least a portion of at least one TSCI within a past sliding time window, and past characteristics of the object within a past sliding time window. The at least one TSCI is preprocessed. A computational workload associated with the method is shared among the processor, the type 1 heterogeneous wireless device, the type 2 heterogeneous wireless device, and the other processor. Time-domain processing and frequency-domain processing of the at least one TSCI may be performed in the current sliding time window. At least one peak may be detected in the frequency domain. The current characteristic of the object may be calculated based on the at least one peak in the frequency domain.

[0044] For each original CI of a particular portion of a particular TSCI within the current sliding time window, N1 frequency-domain components of the original CI may be transformed into N2 time-domain coefficients of the same CI using an inverse frequency transform. Each CI may be associated with an original timestamp, where N2 must not be less than N1. Each of the N2 coefficients may be associated with a time delay. The original timestamps of all original CIs of a particular portion of a particular TSCI may be modified in the current sliding time window, so that the modified timestamps of the time-modified CIs may be uniformly spaced in time.

[0045] For each of the N2 time-domain coefficients of a time-modified CI to be calculated at a particular modified timestamp, the time-domain coefficients of the time-modified CI at the particular modified timestamp may be calculated based on the time-domain coefficients of the original CI at the original timestamp. A particular portion of a particular TSCI within the current sliding time window may be determined to be associated with the current quasi-periodic motion of the object within the location. The particular portion may include N0 Cls. For each of the N0 CIs, N1 frequency-domain components may be obtained. A matrix G1 of size N1 x N0 may be determined, where each of the N0 columns may include N1 frequency elements for each CI. The original timestamp associated with each CI may be determined. The original timestamps of all CIs in the particular portion of a particular TSCI in the current sliding time window may be modified so that the modified timestamps of the time-modified CIs are uniformly spaced in time. Spectral analysis of the output of the operation may be performed in the frequency domain. For each original CI in the particular portion of a particular TSCI within the current sliding time window, the N2 time-domain coefficients of the same CI may be converted to N2 frequency-domain coefficients using a frequency transform.

[0046] The Type 1 device has at least one antenna. The Type 2 device may also have at least one antenna. Each of the at least one TSCI may be associated with one of the at least one antennas of the Type 1 device and one of the at least one antennas of the Type 2 device. The averaging over the antenna link is averaging over the at least one TSCI. At least one local minimum and at least one local minimum in the frequency domain may be identified. At least one local signal-to-noise ratio (SNR-like) parameter may be calculated for each pair of adjacent maximum and minimum. In some examples, different portions of the frequency range may be treated differently. Past peaks (including "reserved" peaks) within a past sliding time window may be analyzed to obtain a "predicted" peak within the current sliding time window. A likely local region or "prediction region" within the frequency range (e.g., an immediate local region around a past peak or "prediction peak") may be identified. Instead of searching the entire frequency range for a peak, a fast local search in likely local regions may be performed in the prediction region. The width of the prediction region may be adaptively changed over time. As predictions become more and more accurate, the predicted region may be reduced in size to save computation while maintaining accuracy. In search of new peaks (not represented in past peaks), a fast global search (possibly with lower accuracy) may also be performed.

[0047] A "predicted" peak can be obtained by zero-order prediction, i.e., it can simply be a past peak. A "predicted" peak can also be obtained by first-order prediction, second-order prediction, higher-order prediction, and / or another prediction. Multiple predictions can be made and combined to form a hybrid prediction. For example, a person may initially be at rest and breathing at 18 breaths per minute (bpm). The breathing rate may naturally fluctuate around 18 bpm over time. The person may get up and start moving (e.g., walking, swimming, running, doing push-ups, exercising, etc.), resulting in a brief increase in breathing rate from 18 bpm to 30 bpm. The slowly fluctuating breathing rate (approximately 18 bpm) may be tracked by the "predicted" peak. However, 30 bpm may be a new peak not predicted by past peaks. A global search is used to discover new peaks. Past peaks (including important but unselected and / or reserved ones) can be analyzed and peaks at different time instances can be correlated to form a "trace" of the evolving periodic movement. If a peak at time t is close enough to a previous peak at time t-1 (the frequency difference is less than a threshold), the two peaks can be "associated" or "connected" to the same trace. In the previous example, if a new peak suddenly appears at 30 bpm and the previous peak at 18 bpm suddenly disappears, the new peak at 30 bpm will be connected or associated with the 18 bpm trace. However, if 18 bpm remains when 30 bpm suddenly appears, the 30 bpm may be considered an entirely new trace. Preference may be given to surrounding local areas such as long traces, persistent traces, strong traces, close traces, traces that cross each other, and intermittent traces with a long history.

[0048] While all antenna links may be combined or averaged for analysis, individual antennas and / or subsets of antennas may be analyzed separately, and strong / persistent peaks may be identified. These may be considered along with other significant peaks. A finite state machine (FSM) may be applied to all combinations (individually, subsets, and / or the entire antenna set). The FSM may be different for different combinations. The states used in the FSM may be different for different combinations. The FSM may be identical for all different combinations. Different predicted peaks may be used with different combinations. Different combinations may have different peaks, different traces, etc. Different combinations may differ in any and / or all steps. For example, if a finite state machine (FSM) is used, it may always select the strongest peak at that frequency in any state of the FSM.

[0049] The current features of the object may be made available (e.g., to software, a software module, a hardware module, a processor, another processor, another device, a remote device, a cloud device) in real time with or without a time delay and / or non-real time. The current sliding time window may move by a shift size as time passes. The residual spectral energy may be calculated by subtracting the spectral energy associated with a selected set of significant local peaks from the total spectral energy. An event related to the current features of the object may be detected based on at least one of the residual spectral energy, an adaptive threshold, and a finite state machine.

[0050] Finite State Machines for Periodic Motion Detection and Estimation The decision threshold (i.e., the threshold for making a decision) can be adaptively calculated based on a finite state machine (FSM). The thresholds T1 and T2 can be adaptively calculated based on a finite state machine. The finite state machine can include an INIT state, a verification state, a peak found state, and / or a motion state.

[0051] The INIT state of the finite state machine (FSM) can be entered in at least one predetermined way. A threshold can be adaptively calculated in the INIT state in a first way. An event can be detected based on the threshold. The INIT state can transition to other states based on at least one transition criterion. The threshold can be adaptively calculated in other ways in other states. In the INIT state, the threshold can be adaptively calculated in a first way. An event can be detected (i.e., if it occurs, a detection operation is applied in an attempt to detect the event) based on the threshold and a selected set of significant local peaks and / or associated features. Excessive background interferometric motion can be detected (i.e., a detection operation is applied to detect any excessive background interferometric motion, if any) based on the threshold and the remaining spectral energy. When it is concluded that an event is "not detected" and excessive background interferometric motion is "not detected," the INIT state can remain unchanged. When it is concluded that an event has been previously "detected" and the previously detected event needs to be verified, the INIT state may transition to a verification state. If it is concluded that excessive background interfering motion has been "detected," the INIT state can transition to the MOTION state.

[0052] In the verification state, the threshold may be adaptively calculated in a second manner. At least one statistic (e.g., a count of consecutive "detected" events or a percentage of "detected" events in N consecutive time instances) may be accumulated and calculated based on a set of selected significant local peaks and associated features in at least one adjacent sliding time window. The at least one statistic may be for verifying the pre-detected event. For example, detection may be applied to each of at least one adjacent sliding time window. The statistic may be a count of the number of times the pre-detected event continues to be concluded as "detected." The verification state may remain unchanged while at least one statistic (e.g., a count of consecutive "detected" events or a percentage of "detected" events in N consecutive time instances) may be accumulated until sufficient statistics can be collected for verification. Based on the threshold and the at least one statistic, the pre-detected event may be verified (i.e., an operation may be applied to verify the pre-detected event). If the pre-detected event is concluded as "verified," the verification state may transition to the peak found state. When the verification can be concluded as "not verified," the verification state can transition to the INIT state. If the verification is not finished, the verification state remains unchanged. For example, if a total of N consecutive instances are concluded as "detected," or if the percentage of "detected" in N consecutive time instances is greater than a certain threshold, a "verified" state can be concluded.

[0053] In the peak found state, the threshold may be adaptive in a third way. A verified event (i.e., an event verified immediately before entering the peak found state) may be detected based on a threshold and a selected set of significant local peaks and / or associated features. Excessive background interfering motion may be detected based on a threshold and remaining spectral energy. If the verified event is concluded to be "verified," the peak found state may remain unchanged. When the verified event can be concluded to have been "not detected" for multiple time instances, the peak found state may transition to the INIT state. When excessive background interfering motion can be concluded to have been "detected" for multiple time instances (e.g., three times), the peak found state may transition to the motion state.

[0054] In the motion state, the threshold may be adaptively calculated in a fourth manner. Excessive background interfering motion may be detected based on the threshold and the remaining spectral energy. When excessive background interfering motion is concluded to be "detected," the motion state may remain unchanged. When excessive background interfering motion may be concluded to be "not detected" for multiple time instances, the motion state may transition to the INIT state. When excessive background interfering motion is concluded to be "not detected" for multiple time instances, the motion state may also transition to a previous state. The previous state is the state from which the current motion state was initiated.

[0055] When multiple periodic movements with multiple respective traces are monitored, each trace may have a different FSM, i.e., one peak associated with one of the traces may be in a verify state, while another peak associated with another trace may be in a peak found state.

[0056] Additional states can be defined. For example, a "JustLost" state (for a trace that has just been lost) or a "TemporarilyLost" state (for an intermittent trace that is temporarily lost) can be added between the transition from PeakFound to INIT. The motion state is divided into two states: "Motion from PeakFound" for a peak (or trace) entering the Motion state from PeakFound, "Motion to Init" for a peak (or trace) entering the Motion state from INIT, "Verification to Motion" for a peak (or trace) entering the Operating state from a Verification state, and "Non-INIT to Motion" for a peak (or trace) entering the Operating state from a non-INIT state. A peak (or trace) entering the INIT state from PeakFound can have an "Init after PeakFound" state. There can be a "Verification to Motion" state, which is a possible transition of the verification state. There is also a "HugeMotion" state for large motion and a "SmallMotion" state for moderate motion.

[0057] There may be multiple motion states (e.g., motion states) with different characteristics of interfering background activity. There may be multiple verification states (e.g., verification states) with different characteristics of the detected peak. There may be multiple initial / standby states (e.g., INIT states) with different initial and / or default characteristics. There may be several low-power states with low computational effort to conserve power. There may be a low data rate state with low data traffic to the network and / or storage. There may be several cooperative states in which multiple Type 1 and / or multiple Type 2 devices cooperate to find reliable periodic motion. There may be several low-priority states, high-priority states, and / or variable-priority states in which priorities are assigned to peaks / traces based on trust, reliability, value, commercial value, user class, user characteristics, Type 1 device location, Type 2 device location, trace classification, motion characteristics associated with the trace, etc. Machine learning, training, discriminative training, deep learning, neural networks, continuous time processing, distributed computing, distributed storage, GPU / DSP / coprocessor / multi-core / multi-processing acceleration can be applied to any / every step of this disclosure. For various combinations of past and current states, there can be composite states such as "current INIT, past motion" state, "current INIT, past peak found" state, etc.

[0058] Sometimes two periodic movements cause traces to cross, touch, or get close together. There can be a "cross" state where two traces cross, an "interference" state where two traces are close enough to interfere with each other (e.g., two traces may or may not cross, may cross intermittently, or may cross regularly / in a predictable pattern / in a statistically stationary pattern / in a periodic stationary pattern), a "multi-trace" state for the crossing of multiple traces (e.g., 2 or more), or a "lost" state where a long-term trace suddenly disappears.

[0059] When two traces cross each other and then separate, the association of the two peaks with the two traces after the crossing may be based on peak prediction. For example, a trace that shows an increasing trend after the crossing can be associated with a past trace that showed an increasing trend before the crossing, and vice versa. A highly variable trace after the crossing (e.g., with high variance and / or high total variation) may be associated with a past trace that has similarly variable characteristics. An initial association is made immediately after the crossing. However, the association can be revisited (one, two, or more times) in the near future as more observations are obtained and short-term, medium-term, and long-term trace behavior is monitored and used to adjust any erroneous initial associations.

[0060] For each trace, features can be defined, calculated, stored, displayed, and provided to the user, such as duration, past history, frequency of occurrence, intermittency measure, current run length, previous run length, start time of run, end time of run, confidence, trust, reliability, peak power, interference background motion power, variance, total variation, antenna ID, Type 1 device ID, Type 2 device ID, multiple Type 1 devices, multiple Type 2 devices, etc.

[0061] Pattern recognition and / or machine learning can be applied to traces to recognize short-, medium-, and long-term trends. For example, trends can be long-term increases, short-term increases, vibrations, Doppler effects, etc. Approaching objects can have increasing frequencies. Moving / receding objects can have decreasing frequencies. A fan sweeping from side to side can have a repeating increase-decrease pattern. Gait can have a "signature" in the time trend of associated peaks. Objects attached to multiple Type 1 devices (e.g., a vehicle may be large, with four Type 1 devices installed at each corner) can correlate corresponding states with each other. If a vehicle is rotating, traces from multiple Type 1 devices (and / or tracked motion in indoor motion tracking) can be correlated. Therefore, multiple traces from related Type 1 devices can be jointly analyzed to make smarter, more accurate / precise decisions to avoid incorrect decisions due to impulsive interference.

[0062] When multiple periodic movements (e.g., breathing of multiple people) are monitored, different movements may be associated with different FSMs. One FSM may be correlated with another. For example, a husband and wife may sleep in the same bed. An FSM may be set for the husband; another FSM may be for the wife. When the husband gets up (e.g., to get a drink of water or use the toilet), the wife may be disturbed. Each person's state may be related to that person's movements, which may depend on the movements (and states) of the others. Thus, the behavior and state transitions of multiple FSMs may be determined together. For example, if one person undergoes a state change, the other person is likely to undergo some change. Therefore, state / peak predictions may be less aggressive (i.e., assuming the predictions are very accurate / reliable). A more conservative approach may also be used; for example, using a larger search radius in the local search, a more global search, a more conservative threshold, or a larger / smaller threshold.

[0063] Systems / Interconnect In one embodiment, the present teachings disclose 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 (channel) can be obtained using a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory. The time series of CI (TSCI) can be extracted from wireless signals (signals) transmitted between a Type 1 (Type 1) heterogeneous wireless device and a Type 2 (Type 2) heterogeneous wireless device through the channel within a location. The channel may be affected by the movement of objects within the location. Characteristics and / or spatiotemporal information (e.g., motion information) of the objects and / or their motion can be monitored based on the TSCI. A task can be performed based on the characteristics and / or spatiotemporal information (e.g., motion information). A presentation related to the task can be generated in a user interface (UI) on a user's device. The TSCI can be preprocessed.

[0064] A Type 1 device may include at least one heterogeneous wireless transmitter. A Type 2 device may include at least one heterogeneous wireless receiver. The Type 1 device and the Type 2 device may be the same device. Any device may have a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory for execution by the processor. Some processors, memories, and instruction sets may cooperate. There may be multiple Type 1 devices interacting with the same Type 2 device (or multiple Type 2 devices), and / or there may be multiple Type 2 devices interacting with the same Type 1 device. Multiple Type 1 / Type 2 devices may be synchronous and / or asynchronous with the same / different window width / size and / or time shift. Wireless signals sent by multiple Type 1 devices may be synchronous and / or coexistent. Multiple Type 1 / Type 2 devices may operate independently and / or cooperatively. Type 1 and / or Type 2 devices may have / include / be heterogeneous hardware circuitry (e.g., heterogeneous chips or ICs capable of generating / receiving wireless signals, extracting CI from received signals, or making CI available). They may be communicatively coupled to the same or different servers (e.g., cloud servers, edge servers, local servers). The operation of a device may be based on the operation, state, internal state, storage, processor, memory output, physical location, computational resources, or network of another device. Various devices may communicate directly and / or through another device / server / cloud server. Devices may be associated with one or more users with associated settings. Settings may be selected once, pre-programmed, and / or changed over time. When one or more Type 1 devices interact 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, co-processor, memory, battery life, available power, antenna, antenna type, antenna directionality / unidirectionality, power settings, and / or other parameters / characteristics of the device.

[0065] The wireless receiver (e.g., a Type 2 device) can receive a signal and / or another signal from the wireless transmitter (e.g., a Type 1 device). The wireless receiver can receive another signal from another wireless transmitter (e.g., a second Type 1 device). The wireless transmitter can transmit a signal and / or another signal to another wireless receiver (e.g., a second Type 2 device). The wireless transmitter, the wireless receiver, another wireless receiver, and / or another wireless transmitter can move with the object and / or another object. The other object can be tracked.

[0066] A Type 1 and / or Type 2 device may be capable of wirelessly coupling with at least two Type 2 and / or Type 1 devices. The Type 1 device may switch / establish a wireless coupling from a Type 2 device to a second Type 2 device at another location within the location. Similarly, the Type 2 device may switch / establish a wireless coupling from a Type 1 device to a second Type 1 device at yet another location within the location. The switching may be controlled by a server, a processor, a Type 1 device, a Type 2 device, and / or another device. The radio used before and after the switching may be different. A second wireless signal (second signal) may be transmitted between the Type 1 device and the second Type 2 device (or between the Type 2 device and the second Type 1 device) over the channel. A second TSCI of the channel may be extracted from the second signal. The second signal may be the first signal. Object features, spatiotemporal information, and / or another quantity may be monitored based on the second TSCI. The Type 1 device and the Type 2 device may be identical.

[0067] The signal and / or another signal may have data embedded therein. The signal may be a series of probe signals. The probe signal may have data embedded therein. The probe signal may be replaced by a data signal. The wireless receiver, the wireless transmitter, the another wireless receiver, and / or the another wireless transmitter may be associated with at least one processor, a memory communicatively coupled to the respective processor, and / or a respective instruction set stored in the memory, which, when executed, causes the processor to perform any and / or all steps necessary to determine spatiotemporal information (e.g., motion information), initial spatiotemporal information, initial time, direction, instantaneous location, instantaneous angle, and / or speed of the object. The processor, memory, and / or instruction set may be associated with the Type 1 heterogeneous wireless transceiver, one of the at least one Type 2 heterogeneous wireless transceiver, the object, a device associated with the object, another device associated with the location, a cloud server, and / or another server.

[0068] A Type 1 device can transmit a signal in a broadcast manner to at least one Type 2 device(s) over a channel within a location. The signal is transmitted without the Type 1 device establishing a wireless connection with any Type 2 device. A Type 1 device can transmit to a specific media access control (MAC) address that is common to two or more Type 2 devices. Each Type 2 device can adjust its MAC address to the specific MAC address.

[0069] A particular MAC address may be associated with a location. The association may be recorded in an association table of an association server. A location may be identified by a Type 1 device, a Type 2 device, and / or another device based on a particular MAC address, a series of probe signals, and / or at least one TSCI extracted from the probe signals. For example, a Type 2 device may be moved to a new location within a location (e.g., from another location). A Type 1 device may be newly set up within a location such that the Type 1 and Type 2 devices are unaware of each other. During setup, the Type 1 device may be instructed / induced / caused / controlled (e.g., using a dummy receiver, using a hardware pin configuration / connection, using a stored configuration, using a local configuration, using a remote configuration, using a downloaded configuration, or using a server) to send a series of probe signals to a particular MAC address. Upon powering on, the Type 2 device may scan for probe signals according to a table of MAC addresses (e.g., various MAC addresses used in various locations such as homes, offices, enclosures, floors, skyscrapers, stores, airports, malls, stadiums, halls, stations, subways, lots, areas, zones, regions, districts, cities, countries, continents, etc.) that may be used to broadcast at various locations (e.g., stored in a designated source, server, cloud server). If the Type 2 device detects a probe signal transmitted to a particular MAC address, the Type 2 device can use the table to identify a location based on the MAC address. The location of the Type 2 device within the location can be calculated based on the particular MAC address, the series of probe signals, and / or at least one TSCI obtained by the Type 2 device from the probe signals. The calculation can be performed by the Type 2 device.

[0070] The specific MAC address may change over time. The change may be in accordance with a timetable, a rule, a policy, a mode, a condition, a situation, and / or a change. The specific MAC address may be selected based on MAC address availability, a preselection list, a collision pattern, a traffic pattern, data traffic between the Type 1 device and another device, available bandwidth, a random selection, and / or a MAC address switching plan. The specific MAC address may be the MAC address of a second wireless device (e.g., a dummy receiver or a receiver acting as a dummy receiver). The Type 1 device may transmit a probe signal on a channel selected from a set of channels. At least one CI for the selected channel may be obtained by each Type 2 device from the probe signal transmitted on the selected channel. The selected channel may change over time. The change may be in accordance with a timetable, a rule, a policy, a mode, a condition, a situation, and / or a change. The selected channel may be selected based on channel availability, random selection, a pre-selection list, co-channel interference, inter-channel interference, channel traffic patterns, data traffic between the Type 1 device and another device, the available bandwidth associated with the channel, security criteria, channel switching plans, criteria, and / or considerations. Information about the specific MAC address and / or selected channel may be communicated between the Type 1 device and a server over a network. Information about the specific MAC address and / or selected channel may also be communicated between the Type 2 device and a server over another network. A Type 2 device may communicate information about the specific MAC address and / or selected channel to another Type 2 device (e.g., via a mesh network, Bluetooth, WiFi, etc.). The specific MAC address and / or selected channel may be selected by a server. The specific MAC address and / or selected channel may be signaled by the Type 1 device, the Type 2 device, and / or the server over an announcement channel. Any information may be pre-processed before communication.

[0071] A wireless connection between a Type 1 device and another wireless device may be established (e.g., using a signal handshake). The Type 1 device may send a first handshake signal (e.g., a sounding frame, a probe signal, a request to send RTS) to the other device. The other device may reply by sending a second handshake signal (e.g., an instruction or a clear to send CTS) to the Type 1 device and triggering the Type 1 device to send a signal (e.g., a series of probe signals) to multiple Type 2 devices in a broadcast manner without establishing a connection with any Type 2 devices. The second handshake signal may be a response or acknowledgment (e.g., an ACK) to the first handshake signal. The second handshake signal may include data having location information and / or information of the Type 1 device.

[0072] The other device may be a dummy device with the purpose (e.g., primary purpose, secondary purpose) of establishing a wireless connection with the Type-1 device, receiving the first signal, and / or transmitting the second signal. The other device may be physically attached to the Type-1 device. In another example, the other device may send a third handshake signal 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 devices. The Type-1 device may reply to the third special signal by sending a fourth handshake signal to the other device. The other device may be used to trigger and broadcast to two or more Type-1 devices. Triggering may be sequential, partially sequential, partially parallel, or fully parallel. The other device may have two or more radio circuits for triggering multiple transmitters in parallel. Parallel triggering may also be achieved by using at least one additional device to perform triggering in parallel with the other device (as the other device does). The other device may not communicate (or may suspend communication) with the Type-1 device after establishing a connection with the Type-1 device. Suspended communication may be resumed. The other device may enter an inactive mode, a hibernation mode, a sleep mode, a standby mode, a low power mode, an OFF mode, and / or a power down mode after establishing a connection with the Type 1 device. The other device may have a specific MAC address, which causes the Type 1 device to send a signal to the 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 the designated source, and / or a fourth processor associated with the other device. The first and second processors may be coordinated with each other.

[0073] A first series of probe signals may be transmitted by a first antenna of the Type 1 device to at least one first Type 2 device via a first channel in a first location. A second series of probe signals may be transmitted by a second antenna of the Type 1 device to at least one second Type 2 device via a second channel in a second location. The first series and the second series may be different or not different. The at least one first Type 2 device may be different or not different from the at least one second Type 2 device. The first and / or second series of probe signals may be broadcast without an established connection between the Type 1 device and any Type 2 device. The first and second antennas may be the same or not.

[0074] The two locations may have different sizes, shapes, and multipath characteristics. The first and second locations may overlap. The respective proximity areas around 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 2.4 GHz WiFi and the second may be 5 GHz WiFi. Or, both may be 2.4 GHz WiFi but have different channel numbers, SSID names, and / or WiFi settings. Each Type 2 device may obtain at least one TSCI from each series of probe signals, and the CI may be for each channel between the Type 2 device and the Type 1 device. Some first Type 2 device(s) and some second Type 2 device(s) may be identical. The first and second series of probe signals may be synchronous / asynchronous. The probe signals may be transmitted along with data or may be replaced by data signals. The first and second antennas may be the same or different.

[0075] The first series of probe signals may be transmitted at a first rate (e.g., 30 Hz). The second series of probe signals may be transmitted at a second rate (e.g., 200 Hz). The first and second rates may be the same or different. The first and / or second rates may change over time. The change may be according to a timetable, rule, policy, mode, condition, situation, and / or change. Any rate may change over time. The first and / or second series of probe signals may be transmitted to a first MAC address and / or a second MAC address, respectively. The two MAC addresses may be the same or different. The first series of probe signals may be transmitted on a first channel. The second series of probe signals may be transmitted on a 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 timetables, rules, policies, modes, conditions, circumstances, and / or changes.

[0076] A Type 1 device and another device may be controlled and / or coordinated, may be physically attached, or may be / be within a common device. They may be controlled / connected by a common data processor or connected to a common bus interconnect / network / LAN / Bluetooth network / BLE network / wired network / wireless network / mesh network / mobile network / cloud. They may share common memory or be associated with a common user, user device, profile, account, identity (ID), household, house, physical address, location, geographic coordinates, IP subnet, SSID, home device, office device, and / or manufacturing device. Each Type 1 device may be a signal source for a respective set of Type 2 devices (i.e., transmit a respective signal (e.g., a respective series of probe signals) to a respective set of Type 2 devices). Each respective Type 2 device selects a Type 1 device as a signal source from among all Type 1 devices. Each Type 2 device may select a Type 1 device as a signal source asynchronously. At least one TSCI can be obtained by each respective Type 2 device from a respective series of probe signals from the Type 1 device, the CI being of a channel between the Type 2 device and the Type 1 device.

[0077] Each Type 2 device selects a Type 1 device as a signal source from among all Type 1 devices based on the Type 1 / Type 2 device's identification (ID), the task to be performed, past signal sources, history (e.g., of past signal sources, the Type 1 device, another Type 1 device, each Type 2 receiver, and / or another Type 2 receiver), a threshold for switching signal sources, and / or user, account, access information, parameters, characteristics, and / or signal strength information (e.g., associated with the Type 1 device and / or each Type 2 receiver). Initially, a Type 1 device may be a signal source for the initial set of respective Type 2 devices (i.e., the Type 1 device transmits a respective signal (series of probe signals) to the initial set of respective Type 2 devices). Each initial respective Type 2 device selects a Type 1 device as a signal source from among all Type 1 devices.

[0078] A signal source of a particular Type 2 device (Type 1 device) may be changed if (1) the time interval between two adjacent probe signals (e.g., between the current probe signal and the immediately preceding probe signal, or between the next probe signal and the current probe signal) received from the current signal source of the Type 2 device exceeds a first threshold, (2) the signal strength associated with the current signal source of the Type 2 device is less than a second threshold, (3) the processed signal strength associated with the current signal source of the Type 2 device is less than a third threshold, and the signal strength has been processed with a low-pass filter, a band-pass filter, a median filter, a moving average filter, a weighted average filter, a linear filter, and / or a linear filter, and / or (4) the signal strength (or the processed signal strength) associated with the current signal source of the Type 2 device is less than a fourth threshold for a significant percentage (e.g., 70%, 80%, 90%, etc.) 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. Condition (1) may occur when the Type 1 device and the Type 2 device gradually move away from each other, causing some probe signals from the Type 1 device to become too weak and not be received by the Type 2 device. Conditions (2)-(4) may occur when the two devices move away from each other, causing the signal strength to become very weak. The signal source of a Type 2 device may not change if other Type 1 devices have signal strengths weaker than the current signal source by a factor (e.g., 1, 1.1, 1.2, or 1.5). When the signal source changes, a new signal source may become effective in the near future (e.g., each next time). The new signal source may be the Type 1 device with the strongest signal strength and / or processed signal strength. The current signal source and the new signal source may be the same or different. 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.

[0079] The Type 2 device may select between a first series of probe signals from a first Type 1 device and a second series of probe signals from a second Type 1 device based on their respective probe signal rates, MAC addresses, channels, characteristics / properties / states, tasks to be performed by the Type 2 device, signal strengths of the first and second series, and / or other considerations. The series of probe signals may be transmitted at a regular rate (e.g., 200 Hz). The series of probe signals may be scheduled at regular intervals (e.g., 0.01 seconds at 100 Hz), although each probe signal may experience brief time perturbations, perhaps due to timing requirements, timing control, network control, handshaking, message passing, collision avoidance, carrier sensing, congestion, resource availability, and / or other considerations. The rate may be changed. The change may be according to a timetable (e.g., once per hour), a rule, a policy, a mode, a condition, and / or a change (e.g., whenever some event occurs). For example, the rate may be normally 100Hz, but may be changed to 1000Hz in situations where necessary, or to 1Hz in low power / standby situations. The probe signal may be transmitted in bursts.

[0080] The probe signal rate may vary based on the task performed by the Type 1 or Type 2 device (e.g., a task may require 100 Hz normally, 1000 Hz for 20 seconds instantaneously). In one embodiment, the transmitter (Type 1 device), receiver (Type 2 device), and associated task may be adaptively associated with a class (e.g., low priority, high priority, emergency, critical, normal, privileged, non-subscription, subscription, payment, and / or non-payment classes). The (transmitter's) rate may be adjusted for some classes (e.g., high priority class). If the needs of a class change, the rate can be changed. If the receiver's power is very low, the rate can be reduced to reduce the receiver's power consumption to respond to the probe signal. In one embodiment, the probe signal can be used to wirelessly transmit power to the receiver (Type 2 device), and the rate can be adjusted to control the amount of power transmitted to the receiver. The rate can be changed by (or based on) the server, the Type 1 device, and / or the Type 2 device. Control signals can be communicated between them. The server can monitor, track, forecast, and / or anticipate the needs of and / or tasks performed by Type 2 devices and can control Type 1 devices to change rates. The server can schedule rate changes according to a timetable. The server can detect emergencies and change rates immediately. The server can detect evolving conditions and gradually adjust rates.

[0081] The features and / or spatiotemporal information (e.g., movement information) may be monitored individually based on TSCIs associated with a particular Type 1 device and a particular Type 2 device, and / or jointly based on any TSCIs associated with a particular Type 1 device and any Type 2 device, and / or jointly based on any TSCIs associated with a particular Type 2 device and any Type 1 device, and / or collectively based on any TSCIs associated with any Type 1 device and any Type 2 device. Any joint monitoring may be associated with a user, user account, profile, household, location map, and / or user history, etc.

[0082] A first channel between a Type 1 device and a Type 2 device may be different from a second channel between another Type 1 device and another Type 2 device. The two channels may be associated with different frequency bands, bandwidths, carrier frequencies, modulation, wireless standards, coding, encryption, payload characteristics, networks, network IDs, SSIDs, network characteristics, network settings, and / or network parameters, etc. The two channels may be associated with different types of wireless systems (e.g., two of WiFi, LTE, LTE-A, 2.5G, 3G, 3.5G, 4G, greater than 4G, 5G, 6G, 7G, 802.11 systems, 802.15 systems, 802.16 systems, mesh networks, Zigbee, WiMax, Bluetooth, BLE, RFID, UWB, microwave systems, radar-like systems, etc.). For example, one channel is WiFi and the other is LTE. The two channels may be associated with the same type of wireless system but in different networks. For example, a first channel may be associated with a WiFi network named "Pizza and Pizza" in the 2.4 GHz band with a bandwidth of 20 MHz, while a second channel may be associated with a WiFi network in the 5 GHz band with a bandwidth of 40 MHz and an SSID of "StarBud hotspot." The two channels may be different channels within the same network (e.g., the "StarBud hotspot" network).

[0083] In one embodiment, the wireless monitoring system may include training a classifier for multiple events within a location based on training TSCIs associated with the multiple events. For each of multiple known events occurring within the location during a respective training period associated with the known events, a respective training wireless signal (e.g., a respective series of training probe signals) may be transmitted by an antenna of the first Type-1 heterogeneous wireless device to at least one first Type-2 heterogeneous wireless device via a wireless multipath channel within the location during the respective training period using a processor, memory, and instruction set of the first Type-1 device. At least one respective time series of training CIs (training TSCIs) may be asynchronously obtained by each of the at least one first Type-2 device from the (respective) training signal. The CIs may be CIs of channels between the first Type-2 device and the first Type-1 device during the training period associated with the known events. The at least one training TSCI may be preprocessed.

[0084] For a current event occurring within a current time period within the location, a current wireless signal (e.g., a series of current probe signals) may be transmitted by an antenna of the second type-1 heterogeneous wireless device to at least one second type-2 heterogeneous wireless device via a channel within a current time period associated with the current event within the location using a processor, memory, and instruction set of the second type-1 device. At least one time series of current CI (current TSCI) may be asynchronously derived by each of the at least one second type-2 device from the current signal (e.g., the series of current probe signals). The CI may be a CI of a channel between the second type-2 device and the second type-1 device within a current time period associated with the current event. The at least one current TSCI may be preprocessed.

[0085] The classifier may be applied to classify at least one current TSCI obtained from a series of current probe signals by at least one second Type 2 device, 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 with another portion of another TSCI. The classifier may also be applied to associate a current event with a known event, an unknown event, and / or another event. Each TSCI may include at least one CI, each associated with a respective timestamp. Two TSCIs associated with two Type 2 devices may differ in start time, duration, stop time, amount of CIs, sampling frequency, and sampling frequency. The CIs may have different characteristics. The first and second Type 1 devices may be the same location within a location. They may be the same device. At least one second Type 2 device (or these locations) may be a permutation of at least one first Type 2 device (or these locations). A specific second Type 2 device and a specific first Type 2 device may be the same device.

[0086] The subset of first Type 2 devices and the subset of second Type 2 devices may be identical. At least one second Type 2 device and / or the subset of at least one 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 the subset of at least one first Type 2 device may be a permutation of a subset of at least one second Type 2 device. At least one second Type 2 device and / or the subset of at least one second Type 2 device may be a permutation of a subset of at least one first Type 2 device. At least one second Type 2 device and / or the subset of at least one second Type 2 device may be in the same respective location as a subset of at least one first Type 2 device. At least one first Type 2 device and / or the subset of at least one first Type 2 device may be in the same respective location as a subset of at least one second Type 2 device.

[0087] The antenna of the Type 1 device and the antenna of the second Type 1 device may be at the same location within the location. The antenna(s) of the at least one second Type 2 device and / or the antenna(s) of a subset of the at least one second Type 2 device may be at the same respective location as the respective antenna(s) of the subset of the at least one first Type 2 device. The antenna(s) of the at least one first Type 2 device and / or the antenna(s) of the subset of the at least one first Type 2 device may be at the same respective location(s) as the respective antenna(s) of the subset of the at least one second Type 2 device.

[0088] A first section of a first duration of a first TSCI and a second section of a second duration of a second TSCI may be aligned. A map between the items of the first section and the items of the second section may be calculated. The first section may include a first segment of the first TSCI having a first start time / end time and / or another segment 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 of the second TSCI having a second start time and a second end time, as well as another segment of the processed second TSCI. The processed second TSCI may be the second TSCI processed by the second operation.

[0089] The first operation and / or the second operation may include subsampling, resampling, interpolation, filtering, transformation, feature extraction, preprocessing, and / or another operation. A first item in the first section may be mapped to a second item in the second section. A 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 a second item in the second section. The mapping may be one-to-one, one-to-many, many-to-one, or many-to-many. At least one function of at least one of: a first item of a first section of a first TSCI; another item of the first TSCI; a timestamp of the first item; a time difference of the first item; a time derivative of the first item; an adjacent timestamp of the first item; another timestamp associated with the first item; a second item of a second section of a second TSCI; another item of the second TSCI; a timestamp of the second item; a time difference of the second item; a time derivative of the second item; an adjacent timestamp of the second item; and another timestamp associated with the second item may satisfy at least one constraint.

[0090] One constraint may be that the difference between the timestamp of the first item and the timestamp of the second item may be upper bounded by an adaptive upper threshold and lower bounded by an adaptive lower threshold. The first section may be the entire first TSCI. The second section may be the entire second TSCI. The first duration may be equal to the second duration. The section duration of the TSCI may be adaptively determined. A provisional section of the TSCI may be calculated. A start time and an end time of a section (e.g., a provisional section) may be determined. The section may be determined by removing the beginning and end of the provisional section. The beginning of the provisional section may be determined as follows: Again, the item in the provisional section with increasing timestamp may be considered the current item, one item at a time. At each iteration, at least one activity measure may be calculated and / or considered. At least one activity measure may be associated with at least one of a current item associated with a current timestamp, a past item in the interim section having a timestamp not greater than the current timestamp, and / or a future item in the interim section having a timestamp not less than the current timestamp. If at least one criterion associated with the at least one activity measure is met, the current item may be added to the beginning of the interim section.

[0091] At least one criterion associated with the activity measure is (a) the activity measure is less than an adaptive upper threshold; (b) the activity measure is greater than an adaptive lower threshold; (c) for at least a predetermined amount of consecutive timestamps, the activity measure is continuously less than the adaptive upper threshold; (d) for at least another predetermined amount of consecutive timestamps, the activity measure is continuously greater than the adaptive lower threshold; (e) for at least a predetermined percentage of at least a predetermined amount of consecutive timestamps, the activity measure is continuously less than the adaptive upper threshold; or (f) for at least another predetermined percentage of another predetermined amount of consecutive timestamps, the activity measure is continuously greater than the adaptive lower threshold. (g) another activity measure associated with another timestamp associated with the current timestamp is less than another adaptive upper threshold and greater than another adaptive lower threshold; (h) at least one activity measure associated with at least one respective timestamp associated with the current timestamp is less than a respective upper threshold and greater than a respective lower threshold; (i) a percentage of timestamps in the set of timestamps associated with the current timestamp that are less than a respective upper threshold and greater than a respective lower threshold exceed a threshold; and (j) another criterion.

[0092] The activity measure associated with an item at time T1 may include at least one of: (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 fixed 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; T2 may be updated periodically; T2 may be the beginning of a period and T1 may be a sliding time within the period); and (4) a fourth function of the item at time T1 and another item.

[0093] At least one of the first function, the second function, the third function, and / or the fourth function may be a function (e.g., F(X,Y,...)) having at least two arguments X and Y. The two arguments may be scalars. The function (e.g., F) may be at least one of 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 quantities. For example, the function may be simply abs(XY), or (XY)^2, (XY)^4. The function may be a robust function. For example, the function can be (XY)^2 when abs(XY) is less than a threshold T, and (XY)+a when abs(XY) is greater than a threshold T. Alternatively, the function can be constant when abs(XY) is greater than T. The function can also be bounded by slowly increasing the function when abs(Xy) is greater than T so that outliers do not seriously affect the results. Another example of a function can be (abs(X / Y)-a), where a=1. In this way, when X=Y (i.e., no change or no activity), the function yields 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. And, when X is less than Y, (X / Y) is less than 1, and the function is negative. In another embodiment, both arguments X and Y can be n-tuples, such that X=(x_1, x_2,..., x_n) and Y=(y_1, y_2,..., y_n). The function may be at least one of x_i, y_i, (x_i-y_i), (y_i-x_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-tuple of X and Y (1≦i≦n), e.g., the component index of x_1 is i=1 and the component index of x_2 is i=2.The function may include a component-wise sum of at least one further function of the following: x_i, y_i, (x_i-y_i), (y_i-x_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 X and Y in the n-tuple. For example, the function may be of the form sum_{i=1}^n(abs(x_i / y_i)-1) / n, or sum_{i=1}^n w_i×(abs(x_i / y_i)-1), where w_i is the weight of component i.

[0094] The map may be calculated using dynamic time warping (DTW). The DTW may include constraints on at least one of the map, the first TSCI item, the second TSCI item, the first duration, the second duration, the first section, and / or the second section. Assume that in the map, the i-th domain item is mapped to the j-th range item. The constraint may be on the allowable combinations of i and j (constraints on the relationship between i and j). A mismatch cost between a first section of a first duration of the first TSCI and a second section of a second duration of the second TSCI may be calculated. The first and second sections may be aligned, thereby establishing a map including two or more links between the first item of the first TSCI and the second item of the second TSCI. For each link, one of the first items having a first timestamp may be associated with one of the second items having a second timestamp. A mismatch cost between the matched first section and the matched second section may be calculated, and the mismatch cost may include a function of an item-wise cost between the first item and the second item associated by a particular link of the map and a link-wise cost associated with the particular link of the map.

[0095] The matched first section and the matched second section may be represented as a first vector and a second vector, respectively, of the same vector length. The mismatch cost may include at least one of an inner product, an inner-product-like quantity, a correlation-based quantity, a covariance-based quantity, a discriminant score, a distance, a Euclidean distance, an absolute distance, an Lk distance (e.g., L1, L2, ...), a weighted distance, a distance-like quantity, and / or another similarity value between the first vector and the second vector. The mismatch cost may be normalized by the respective vector lengths.

[0096] A parameter 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 with a statistical distribution. At least one of a scale parameter, a location parameter, and / or another parameter of the statistical distribution can be estimated. The first section of the first duration of the first TSCI can be a sliding section of the first TSCI. The second section of the second duration of the second TSCI can be a sliding section of the second TSCI. A first sliding window can be applied to the first TSCI, and a corresponding second sliding window can be applied to the second TSCI. The first sliding window of the first TSCI and the corresponding second sliding window of the second TSCI can be aligned. A mismatch cost between the aligned first sliding window of the first TSCI and the aligned second sliding window of the second TSCI can be calculated. The current event may be associated with at least one of a known event, an unknown event, and / or another event based on a mismatch cost.

[0097] The classifier may be applied to at least one of each first section of the first duration of the first TSCI and / or each second section of the second duration of the second TSCI to obtain at least one provisional classification result. Each provisional classification result may be associated with the respective first section and the respective second section. The current event may be associated with at least one of a known event, an unknown event, and / or another event based on mismatch costs. The current event may be associated with at least one of a known event, an unknown event, and / or another event based on the maximum number of corresponding provisional classification results in two or more sections of the first TSCI and in more sessions of the second TSCI. For example, if the mismatch costs point to a specific known event N consecutive times (e.g., N=10), the current event may be associated with the specific known event. In another example, if the percentage of mismatch costs within the N consecutive N immediately preceding the specific known event that point to the specific known event exceeds a certain threshold (e.g., >80%), the current event may be associated with the specific known event.

[0098] In another example, the current event may be associated with a known event that minimizes the mismatch cost most of the time within the time period. The current event may be associated with a known event that minimizes the overall mismatch cost, where the overall mismatch cost 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 particular known event that achieves the minimum of another overall cost. If no known event achieves a mismatch cost less than a first threshold T1 for a sufficient percentage of at least one first section, the current event may be associated with an "unknown event." If no event achieves an overall mismatch cost less than a second threshold T2, the current event may also be associated with an "unknown event." The current event may be associated with at least one of a known event, an unknown event, and / or another event 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. The known events may include at least one of a door closed event, a door open event, a window closed event, a window open event, a multi-state event, an on-state event, an off-state event, an intermediate state event, a continuous state event, a discrete state event, a human presence event, a human absence event, a presence of people event, and / or a presence of no people event. A projection for each CI may be trained using a dimension reduction method based on the training TSCI.The dimensionality reduction method may include at least one of principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher linear discriminant analysis, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or another method. The projection may be applied to at least one of the training TSCI associated with the at least one event and / or the current TSCI for the classifier. The classifier for the at least one event may be trained based on the projection and the training TSCI associated with the at least one event. The at least one current TSCI may be classified based on the projection and the current TSCI. The projection may be retrained using at least one of the dimensionality reduction method and another dimensionality reduction method based on at least one of the training TSCI, the at least one current TSCI before retraining the projection, and / or additional training TSCI. Other dimensionality reduction methods may include at least one of principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher linear discriminant analysis, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or other methods. A classifier for at least one event may be retrained based on at least one of the retrained projections, the training TSCI associated with the at least one event, and / or the at least one current TSCI. The at least one current TSCI may be classified based on the retrained projections, the retrained classifier, and / or the current TSCI. Each CI may include a vector of complex values. Each complex value may be preprocessed to provide a magnitude of the complex value. Each CI may be preprocessed to provide a vector of non-negative real values ​​including the magnitude of the corresponding complex value.Each training TSCI may be weighted in the training of a projection. The projection may include two or more projected components. The projection may include at least one most significant projected component. The projection may include at least one projected component that may be useful to the classifier.

[0099] Channel / Channel information / Location / Spatial-temporal information / Motion / Object Channel information (CI) may be associated with / include signal strength, signal amplitude, signal phase, received signal strength indicator (RSSI), channel state information (CSI), channel impulse response (CIR), channel frequency response (CFR), channel characteristics, channel filter response, timestamp, aiding information, data, metadata, user data, account data, access data, security data, session data, status data, monitor data, household data, identification information (ID), 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. CI may be associated with information associated with frequency bands, frequency signatures, frequency phases, frequency amplitudes, frequency trends, frequency features, frequency-like characteristics, time-domain elements, frequency-domain elements, time-frequency domain elements, orthogonal decomposition features, and / or non-orthogonal decomposition features of signals over the channel.

[0100] A CI may also be associated with information associated with a signal's duration, time signature, time stamp, time amplitude, time phase, time trend, and / or time characteristics. A CI may be associated with information associated with a signal's time-frequency division, signature, amplitude, phase, trend, and / or characteristics. A CI may be associated with a signal's decomposition. A CI may be associated with information associated with a signal's direction, angle of arrival (AoA), directional antenna angle, and / or phase through a channel. A CI may be associated with a signal's attenuation pattern through a channel. Each CI may be associated with a Type 1 device and a Type 2 device. Each CI may be associated with a Type 1 device's antenna and a Type 2 device's antenna. A CI may be obtained from communication hardware capable of providing a CI. The communications hardware may be a WiFi-enabled chip / IC (integrated circuit), a chip compliant with 802.11 or 802.16 or another wireless / wireless standard, a next-generation WiFi-enabled chip, an LTE-enabled chip, a 5G-enabled chip, a 6G / 7G / 8G-enabled chip, a Bluetooth-enabled chip, a Bluetooth low power (BLE)-enabled chip, a UWB chip, another communications chip (e.g., Zigbee, WiMax, mesh network), etc. The communications hardware calculates the CI and stores the CI in a buffer memory to enable extraction of the CI. The CI may include data related to channel state information (CSI) and / or at least one matrix. The at least one matrix may be used for channel equalization, beamforming, etc.

[0101] A channel may be associated with a location. Attenuation may be due to signal propagation within the location, signal propagation / reflection / refraction / diffraction through / in / in the air (e.g., the air of the location), and refractive media / reflective surfaces (e.g., walls, doors, furniture, obstacles, and / or barriers). Attenuation may be due to reflections off surfaces and obstacles (e.g., reflective surfaces, obstacles), such as floors, ceilings, furniture, built-ins, objects, people, and pets. Each CI may be associated with a timestamp. Each CI may include N components (e.g., N frequency-domain components of the CFR, N time-domain components of the CIR, or N decomposed components). Each component may be associated with a component index. Each component may be a real, imaginary, or complex number, magnitude, phase, flag, and / or set. Each CI may include a vector or matrix of complex numbers, a set of mixing quantities, and / or a multi-dimensional collection of at least one complex number.

[0102] Components of the TSCI associated with a particular component index may form respective component time series associated with the respective index. The TSCI may be divided into N component time series. Each respective component time series is associated with a respective component index. Object motion characteristics / spatiotemporal information may be monitored based on the component time series.

[0103] A feature for each component of the TSCI of the component-feature time series may be calculated. The feature for each component may be a scalar (e.g., energy) or a function with a domain and range (e.g., autocorrelation function, transform, inverse transform). The feature / spatiotemporal information of the object's motion may be monitored based on the feature for each component. The overall feature of the TSCI may be calculated based on the feature for each component of each component time series of the TSCI. The overall feature may be a weighted average of the feature for each component. The feature / spatiotemporal information of the object's motion may be monitored based on the overall feature.

[0104] The Type 1 and Type 2 devices may support WiFi, WiMax, 3G / exceeding 3G, 4G / exceeding 4G, LTE, 5G, 6G, 7G, Bluetooth, BLE, Zigbee, UWB, mesh networks, proprietary wireless systems, IEEE 802.11 standards, 802.15 standards, 802.16 standards, 3GPP standards, and / or another wireless system. A common wireless system and / or a common wireless 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 their respective signals simultaneously using the common wireless system and / or common wireless channel. A Type 1 transceiver may transmit signals to at least one Type 2 transceiver using the common wireless system and / or common wireless channel. A Type 1 device may temporarily function as a Type 2 device, and vice versa. A device may simultaneously function as both a Type 1 device (wireless transmitter) and a Type 2 device (wireless receiver). 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.

[0105] At least one TSCI may correspond to various antenna pairs between the Type 1 device and the Type 2 device. The Type 1 device may have at least one antenna. The Type 2 device may also have at least one antenna. Each TSCI may be associated with an antenna of the Type 1 device and an antenna of the Type 2 device. Averaging or weighted averaging may be performed over the antenna links. The averaging or weighted averaging may be over at least one TSCI. The averaging may optionally be performed over a subset of the at least one TSCI corresponding to a subset of the antenna pairs. The timestamps of some CIs of a TSCI may be irregular and may be corrected, such that the corrected timestamps of the time-corrected CIs may be uniformly spaced in time. In the case of multiple Type 1 devices and / or multiple Type 2 devices, the corrected timestamps may relate to the same or different clocks. An original timestamp associated with each of the CIs may be determined. The original timestamps may not be uniformly spaced in time. The original timestamps of all CIs in a particular portion of a particular TSCI in the current sliding time window may be modified such that the modified timestamps of the time-modified CIs are uniformly spaced in time.

[0106] The features and / or spatiotemporal information (e.g., movement information) may include location, location coordinates, change of location, location (e.g., initial location, new location), location on map, height, horizontal location, vertical location, distance, displacement, speed, acceleration, rotational speed, rotational acceleration, angle of movement, orientation, direction of movement, rotation, path, deformation, translation, shrinkage, expansion, gait, gait cycle, head movement, repetitive movement, cyclic movement, pseudo-cyclic movement, impulsive movement, sudden movement, fall movement, transient movement, behavior, transient behavior, movement period, movement frequency, time trend, temporal profile, temporal features, occurrence, change, Change in frequency, change in timing, change in gait cycle, timing, start time, end time, duration, movement history, movement type, movement classification, frequency, frequency spectrum, frequency features, presence, absence, proximity, approach, retreat, object identity, object configuration, head movement rate, head movement direction, mouth-related rate, eye-related rate, respiratory rate, heart rate, hand movement rate, hand movement direction, leg movement, body movement, gait rate, hand movement rate, position features, features associated with object movement, tool movement, machine movement, complex movement, and / or combination of multiple motions, events, motion statistics, motion magnitude, motion phase, similarity score, distance score, Euclidean distance, weighted distance, L_1 norm for k>2, L_2 norm, L_k norm, statistical distance, correlation, autocorrelation, covariance, autocovariance, cross-covariance, dot product, cross product, motion signal transformation, motion features, motion presence, motion absence, motion localization, motion identification, motion recognition, object presence, object absence, object entry, object exit, object change, motion cycle, motion count, gait cycle, motion rhythm, Deformation movements, gestures, handwriting, head movements, mouth movements, heart movements, internal organ movements, movement trends, size, length, area, volume, capacity, shape, form, tag, start location, end location, start amount, end amount, event, fall event, security event, accident event, home event, office event, factory event, warehouse event, manufacturing event, assembly line event, maintenance event, automobile-related event, navigation event, tracking event, door event, door open event, door close event, window event, window open event,The information may include a window close event, a repeatable event, a one-time event, a consumption amount, an unconsumption amount, a state, a physical state, a health state, a happiness state, an emotional state, a mental state, another event, and / or another information. The processor shares the computational workload with the Type 1 heterogeneous wireless device and the Type 2 heterogeneous wireless device.

[0107] The Type 1 device and / or the Type 2 device may be a local device, which may be a smartphone, a smart device, a TV, a soundbar, a set-top box, an access point, a router, a repeater, a remote control, a speaker, a fan, a refrigerator, a microwave, an oven, a coffee maker, a kettle, a kitchen appliance, a table, a chair, a light, a lamp, a door lock, a camera, a microphone, a motion sensor, a security device, a fire hydrant, a garage door, a switch, a power adapter, a computer, a dongle, a computer peripheral, an electronic pad, a sofa, a tile, an accessory, a smart home device, a smart vehicle device, a smart office device, a smart building device, a smart manufacturing device, a smart watch, a smart glasses, a smart clock, a smart TV, a smart oven, a smart refrigerator, a smart air conditioner, a smart chair, a smart table, a smart accessory, a smart utility, a smart appliance, a smart machine, a smart vehicle, an Internet of Things (IoT) device, an Internet-enabled device, a computer, a portable computer, a tablet, a smart house, a smart office, a smart building, a smart parking lot, a smart system, and / or another device. Each Type 1 device may be associated with a respective identity (ID). Each Type 2 device may also be associated with a respective identification (ID). The ID may include numbers, a combination of text and numbers, a name, a password, an account, an account ID, a web link, a web address, an index of some information, and / or another ID. The ID may be assigned. The ID may be assigned by hardware (e.g., hardwired, via a dongle and / or other hardware), software, and / or firmware. The ID may be stored (e.g., in a database, in memory, on a server, in the cloud, locally, remotely, permanently, temporarily) and retrieved.The ID may be associated with at least one record, account, user, household, address, phone number, social security number, customer number, another ID, timestamp, and / or collection of data. The ID and / or a portion of the ID of the Type 1 device may be made available to the Type 2 device. The ID may be used by the Type 1 device and / or the Type 2 device for registration, initialization, communication, identification, verification, detection, recognition, authentication, access control, cloud access, networking, social networking, logging, recording, cataloging components, tagging, association, pairing, transactions, electronic transactions, and / or intellectual property control.

[0108] Objects include people, passengers, children, elderly people, babies, sleeping babies, babies in vehicles, patients, workers, high value workers, experts, specialists, waiters, mall customers, travelers at airports / train stations / bus terminals / ship terminals, factory / mall / supermarket / office / workplace staff / laborers / customer service representatives, sewer / ventilation system / lift shaft service personnel, lifts in lift shafts, elevators, inmates, people to be tracked / monitored, animals, plants, creatures, pets, dogs, cats, smartphones, phone accessories, computers, tablets, portable computers, dongles, computing accessories, network devices, WiFi devices, IoT devices, smart watches, smart glasses, smart devices, speakers, keys, smart keys, wallets, purses, handbags, backpacks, goods, cargo, luggage, equipment, motors, machinery, air conditioners, fans, air conditioning equipment, built-in lights, movable lights The object may be a light, a television, a camera, audio and / or video equipment, stationary, monitoring equipment, a part, a sign, a tool, a cart, a ticket, a parking pass, a local exchange pass, an airline ticket, a credit card, a plastic card, an access card, food packaging, kitchen utensils, a table, a chair, cleaning equipment / tools, a vehicle, an automobile, an automobile in a parking facility, goods in a warehouse / store / supermarket / distribution center, a boat, a bicycle, an airplane, a drone, a remote controlled automobile / plane / boat, a robot, a manufacturing device, an assembly line, materials / unfinished parts / robots / wagons / transports in a factory, an object being tracked in an airport / shopping mart / supermarket, a non-object, the absence of an object, the presence of an object, an object with form, an object that changes form, an object without form, a fluid mass, a liquid mass, a gas / smoke mass, a fire, a flame, an electromagnetic (EM) source, an EM medium, and / or another object.

[0109] The object itself may be communicatively coupled to several networks, such as WiFi, MiFi, 3G / 4G / LTE / 5G, Bluetooth, BLE, WiMax, Zigbee, mesh networks, ad-hoc networks, and / or other networks. The object itself may be bulky and equipped with AC power, but may be moved during installation, cleaning, maintenance, repair, etc. It may also be mounted on a movable platform, such as a lift, pad, mobile platform, elevator, conveyor belt, robot, drone, forklift, automobile, boat, vehicle, etc. The object may have multiple parts, each of which performs a different motion. For example, the object may be a person walking forward. While walking, the left and right hands may move in different directions with different instantaneous speeds, accelerations, motions, etc.

[0110] The wireless transmitter (e.g., a Type 1 device), the wireless receiver (e.g., a Type 2 device), another wireless transmitter, and / or another wireless receiver can move with the object and / or another object (e.g., in a previous motion, a current motion, and / or a future motion). They can be communicatively coupled to one or more nearby devices. They can transmit TSCIs and / or information associated with the TSCIs to nearby devices and / or to each other. They can have nearby devices. The wireless transmitter and / or wireless receiver can be part of a small (e.g., coin-sized, cigarette pack-sized, or smaller), lightweight, portable device. The portable device can be wirelessly coupled to the nearby devices.

[0111] The nearby devices may be smartphones, iPhones, Android phones, smart devices, smart appliances, smart vehicles, smart gadgets, smart TVs, smart refrigerators, smart speakers, smart watches, smart glasses, smart pads, iPads, computers, wearable computers, notebook computers, gateways, etc. The nearby devices may be connected to cloud servers, local servers, and / or other servers via the Internet, a wired Internet connection, and / or a wireless Internet connection. The nearby devices may be portable. The portable device, nearby devices, local servers, and / or cloud servers may share computation and / or storage for tasks (e.g., obtaining TSCI, determining object features / spatiotemporal information associated with object motion, calculating time series power information, determining / calculating specific functions, searching for local extremums, classification, identifying specific values ​​of time offsets, noise removal, processing, simplification, cleaning, wireless smart sensing tasks, CI extraction from signals, switching, segmentation, trajectory estimation, map processing, correction, correction adjustment, adjustment, map correction, error detection, boundary hitting check, thresholding, etc.) and information (e.g., TSCI).

[0112] The nearby device may or may not move with the object. The nearby device may be portable / non-portable / mobile / non-mobile. The nearby device may use battery power, solar power, AC power, and / or other power sources. The nearby device may have replaceable / non-replaceable and / or rechargeable / non-rechargeable batteries. The nearby device may resemble the object. The nearby device may have the same (and / or similar) hardware and / or software as the object. The nearby device may be a smart device, a network-enabled device, a device with a connection to WiFi / 3G / 4G / 5G / 6G / Zigbee / Bluetooth / ad-hoc network / other network, a smart speaker, a smart watch, a smart clock, a smart appliance, a smart machine, a smart piece of equipment, a smart tool, a smart vehicle, an internet-of-thing (IoT) device, an internet-enabled device, a computer, a portable computer, a tablet, and another device.

[0113] A nearby device and / or at least one processor, wireless transmitter, another wireless receiver, another wireless transmitter, and / or a cloud server (in the cloud) associated with the wireless receiver may determine initial spatiotemporal information of the object. Two or more of them may jointly determine the initial spatiotemporal information. Two or more of them may share intermediate information in determining the initial spatiotemporal information (e.g., initial location). In one embodiment, the wireless transmitter (e.g., a Type 1 device or a Tracker Bot) may move with the object. The wireless transmitter may transmit a signal to the wireless receiver (e.g., a Type 2 device or an Origin Register) or determine the object's initial spatiotemporal information (e.g., initial location). The wireless transmitter may also transmit a signal and / or another signal to another wireless receiver (e.g., another Type 2 device or another Origin Register) to monitor the object's movement (spatiotemporal information). The wireless receiver may also receive signals and / or other signals from the wireless transmitter and / or another wireless transmitter to monitor the movement of the object. The location of the wireless receiver and / or another wireless receiver may be known. In another example, the wireless receiver (e.g., a Type 2 device or a tracker bot) can move with the object. The wireless receiver may receive signals transmitted from the wireless transmitter (e.g., a Type 1 device or an origin registrar) to determine initial spatiotemporal information (e.g., an initial position) of the object. The wireless transmitter may also receive signals and / or other signals from another wireless transmitter (e.g., another Type 1 device or another origin registrar) to monitor the current movement (e.g., spatiotemporal information) of the object. 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) to monitor the movement of the object. The location of the wireless transmitter and / or another wireless transmitter may be known.

[0114] A venue is a room, a house, an office, a workplace, a corridor, a walkway, a lift, a lift shaft, an escalator, an elevator, a sewer system, a ventilation system, a staircase, a gathering area, a duct, an air duct, a pipe, a tube, an enclosed structure, a semi-enclosed structure, an enclosed area, an area with at least one wall, a plant, a machine, an engine, a structure made of wood, a structure made of glass, a structure made of metal, a structure with walls, a structure with doors, a structure with gaps, a structure with reflective surfaces, a structure with fluid, a building, a rooftop, a store, a factory, an assembly line, a hotel room, a museum, a classroom, a school, a university, a government building, a warehouse, a garage, a mall, an airport, a train station, a bus terminal, a hub, a transportation hub, a shipping terminal, a government facility, a public facility, a school, a university, an entertainment facility, a recreational facility, a hospital, a nursing home, an assisted living facility, a community center, a stadium, a playground, a park, a field, a sports facility, a swimming facility, a track and / or field, a basketball court, a tennis court, a soccer stadium, a baseball stadium, a gymnasium , hall, garage, shopping mart, mall, supermarket, manufacturing facility, parking facility, construction site, mining facility, transportation facility, highway, road, valley, forest, tree, land, terrain, cubicle, patio, ground, path, amusement park, city, countryside, suburban area, metropolitan area, garden, square, plaza, music hall, downtown facility, over-air facility, semi-open facility, enclosed area, railroad platform, train station, distribution center, warehouse, store, distribution center, storage facility, underground facility, space (e.g., above ground, (space) facilities, floating facilities, caves, tunnel facilities, indoor facilities, outdoor facilities, outdoor facilities with some walls / doors / reflective barriers, open facilities, semi-open facilities, cars, trucks, buses, vans, containers, ships / boats, submersibles, trains, trams, airplanes, vehicles, mobile homes, caverns, tunnels, pipes, channels, metropolitan areas, downtown areas with relatively tall buildings, valleys, wells, ducts, pathways, gas lines, oil lines, water pipes, pathways / alleys / roads / tubes / cavities / caves / pipe-like structures / airspaces / fluid areas,The location may be a network interconnecting a human body, an animal body, a body cavity, an organ, a bone, a tooth, a soft tissue, a hard tissue, a non-hard tissue, a blood / fluid duct, a trachea, an air duct, a cubicle, or other space. The location may be indoors or outdoors. The location may include both inside and outside of a space. For example, the location may include both inside and outside of a building. For example, the location may be a one-story or multi-story building, and part of the building may be underground. The shape of the building may be, for example, circular, square, rectangular, triangular, or irregular. These are merely examples. Events within other types of locations or spaces can be detected using the present disclosure.

[0115] The wireless transmitter (e.g., Type 1 device) and / or wireless receiver (e.g., Type 2 device) may be embedded within a portable device (e.g., a module or a device with a module) that can move with the object (e.g., in a previous motion and / or a current motion). The portable device may have a wired connection (e.g., via USB, micro USB, Firewire, HDMI, serial port, parallel port, and other connectors) and / or a wireless connection (e.g., Bluetooth, Bluetooth Low Energy, etc.).The portable device may be communicatively coupled to the object using Bluetooth Low Energy (BLE), WiFi, LTE, ZigBee, etc. The portable device may be a lightweight device. The portable may be powered by a battery, a rechargeable battery, and / or an AC power source. The portable device may be ultra-small (e.g., sub-millimeter scale 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 bulky (e.g., heavy equipment installed). 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, smart watches, smart glasses, smart mirrors, smart antennas, smart batteries, smart lighting, smart pens, smart rings, smart doors, smart windows, smart clocks, small batteries, smart wallets, smart belts, smart handbags, smart clothing / apparel, smart ornaments, smart packaging, smart paper / books / magazines / posters / printed materials / signs / displays / illuminated systems / lighting systems, smart keys / tools, smart bracelets / chains / necklaces / wearables / accessories, smart pads / cushions, smart tiles / blocks / bricks / building materials / other materials, smart trash cans / waste containers, smart food carriages / storage, smart balls / racquets, smart chairs / sofas / beds, smart shoes / footwear / carpets / mats / shoe racks, smart gloves / mittens / rings / handwearware), smart hats / headwear / makeup / stickers / tattoos, smart mirrors, smart toys, smart pills, smart kitchen appliances, smart bottles / food containers, smart tools, smart devices, IoT devices, WiFi-enabled devices, network-enabled devices, 3G / 4G / 5G / 6G-enabled devices, embedded devices, implantable devices, air conditioners, refrigerators, heaters, fireplaces, furniture, ovens, cooking devices, televisions / set-top boxes The portable device may be a TV, DVD player, audio player, video player, remote control, hi-fi, audio device, speaker, lamp / lighting, wall, door, window, roof, roof tile / shingle / structure / attic structure / device / feature / equipment / built-in furniture, lawn mower / garden tool / garage tool, trash can / container, 20-foot / 40-foot container, storage container, factory / manufacturing / production device, repair tool, fluid container, machine, installed machine, vehicle, cart, wagon, warehouse vehicle, automobile, bicycle, motorcycle, boat, large ship, airplane, basket / box / bag / bucket / container, smart plate / cup / bowl / pot / mat / kitchen utensil / kitchen tool / kitchen device / kitchen accessory / cabinet / table / chair / tile / lighting / water pipe / faucet / gas range / oven / dishwasher, etc. The portable device may have a battery that may be replaceable, non-replaceable, rechargeable, and / or non-rechargeable. The portable device may be wirelessly charged. 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 locations / facilities where payment is required. The portable device may have an identification (ID), as described above.

[0116] Events can be monitored based on the TSCI. The events can be object-related events, such as an object (e.g., a person and / or a sick person) falling, rolling, hesitating, pausing, or impact (e.g., a person hitting a punching bag, door, window, bed, chair, table, desk, cabinet, box, another person, animal, bird, fly, table, chair, ball, bowling ball, tennis ball, football, soccer ball, baseball, basketball, volleyball, etc.), two-body actions (e.g., a person releasing a balloon, catching a fish, molding clay, writing a document, typing on a computer, etc.), a car moving in a garage, a person walking around an airport / mall / government building / office / etc. with a smartphone, or an autonomous mobile object / machine moving around (e.g., a vacuum cleaner, utility vehicle, automobile, drone, self-driving car, etc.).

[0117] Tasks or wireless smart sensing tasks include object detection, presence detection, object recognition, object verification, tool detection, tool recognition, tool verification, machine detection, machine recognition, machine verification, human detection, human recognition, human verification, infant detection, infant recognition, infant verification, human breathing detection, motion detection, motion estimation, motion verification, periodic motion detection, periodic motion estimation, periodic motion verification, steady motion detection, steady motion estimation, steady motion verification, periodic steady motion detection, periodic steady motion estimation, periodic steady motion verification, transient motion detection, transient motion estimation, transient motion verification, trend detection, trend estimation, trend verification, breathing detection, breathing estimation, breathing estimation, human biometric detection, human biometric estimation, human biometric verification, environmental information detection, environmental information estimation, environmental information verification, gait detection, gait estimation, gait verification, gesture detection, gesture estimation, gesture verification, machine learning, supervised learning, unsupervised learning, semi-supervised learning, clustering, feature extraction, The tasks may include signature training, principal component analysis, eigenvalue decomposition, frequency decomposition, time decomposition, time-frequency decomposition, function decomposition, other decompositions, training, discriminative training, supervised training, unsupervised training, semi-supervised training, neural networks, sudden motion detection, fall detection, hazard detection, life threat detection, regular motion detection, steady motion detection, periodic steady motion detection, intrusion detection, suspicious motion detection, security, safety monitoring, navigation, guidance, map processing, map correction, irregularity detection, location identification, tracking, multiple object tracking, indoor tracking, indoor location, indoor navigation, power transmission, wireless power transmission, object counting, vehicle tracking in a parking garage, patient detection, patient monitoring, patient verification, wireless communication, data communication, signal broadcasting, networking, coordination, management, encryption, protection, cloud computing, other processing, and / or other tasks. The tasks may be performed by a Type 1 device, a Type 2 device, another Type 1 device, another Type 2 device, a nearby device, a local server, an edge server, a cloud server, and / or another device.

[0118] The first portion of tasks may include at least one of pre-processing, signal conditioning, signal processing, post-processing, noise removal, 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, breathing rate detection, breathing rate estimation, breathing pattern detection, breathing pattern estimation, breathing pattern recognition, heart rate detection, heart rate estimation, cardiac pattern detection, cardiac pattern estimation, cardiac pattern recognition, gesture detection, gesture estimation, gesture recognition, speed detection, speed estimation, object localization, object tracking, navigation, acceleration estimation, acceleration detection, fall detection, change detection, intruder detection, infant detection, infant monitor, patient monitor, object recognition, wireless power transfer, and / or wireless charging.

[0119] The second portion of the tasks may include a smart home task, a smart office task, a smart building task, a smart factory task (e.g., manufacturing using machines or an assembly line), a smart Internet of Things (IoT) task, a smart system task, a smart home action, a smart office action, a smart building action, a smart manufacturing action (e.g., moving materials / components / raw materials to a machine / assembly line), an IoT action, a smart system action, turning on lights, turning off lights, controlling lights, playing a sound clip, playing a sound clip in at least one of the rooms, areas, and / or locations, playing at least one sound clip of a welcome, greeting, farewell, first message, and / or second message associated with the first portion of the task, turning on appliances, turning off appliances, controlling appliances in at least one of the rooms, areas, and / or locations, The control may include at least one of: turning on an electrical system, turning off an electrical system, controlling an electrical system in at least one of the locations; turning on a security system, turning off a security system, controlling a security system in at least one of the rooms, areas, and / or locations; turning on a mechanical system, turning off a mechanical system, controlling a mechanical system in at least one of the rooms, areas, and / or locations; and / or controlling at least one of an air conditioning system, a heating system, a ventilation system, a lighting system, a heating device, a stove, an entertainment system, a door, a fence, a window, a garage, a computer system, a network device, a network system, a home appliance, an office equipment, a lighting device, a robot (e.g., a robotic arm), a smart vehicle, a smart machine, an assembly line, a smart device, an Internet of Things (IoT) device, a smart home device, and / or a smart office device.

[0120] Tasks include: detect when the user comes home, detect when the user leaves, detect when the user moves from room to room, detect / control / lock / unlock / open / close / partially open windows / doors / garage doors / blinds / curtains / panels / solar panels / shades, detect pets, detect / monitor when the user is doing 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 upstairs / downstairs, going out / coming home, in the toilet, etc.), monitor / detect user / pet location, automatically do something when detected, automatically do something for the user when detected, turn lights on / off / dim, turn music / radio / home entertainment system on / off, TV / HiFi / Examples of such actions include turning on / off / adjusting / controlling a set-top box (STB) / home entertainment system / smart speaker / smart device, turning on / off / adjusting an air conditioning system, turning on / off / adjusting a ventilation system, turning on / off / adjusting a heating system, adjusting / controlling curtains / light shades, turning on / off / starting a computer, turning on / off / preheating / controlling a coffee maker / kettle, turning on / off / controlling / preheating a cooker / oven / microwave / other cooking device, checking / adjusting a temperature, checking the weather forecast, checking a phone message box, checking email, performing a system check, controlling / adjusting a system, checking / controlling / preparing / disarming a security system / baby monitor, checking / controlling a refrigerator, making reports (e.g., through a speaker such as Google Home, Amazon Echo, on a display / screen, via a web page / email / messaging system / notification system, etc.).

[0121] For example, when a user arrives home in their car, the task may be to automatically detect that the user or their car is approaching, open the garage door as soon as it is detected, turn on the driveway / garage lights as the user approaches the garage, turn on the air conditioner / heater / fan, etc.When a user enters the home, tasks may automatically include turning on entrance lights, turning off driveway / garage lights, playing a greeting message to welcome the user, turning on music, turning on the radio to the user's preferred radio news channel, opening curtains / blinds, monitoring the user's mood, adjusting the lighting and sound environment depending on the user's mood or current / impending events in the user's daily calendar (e.g., the user is having dinner with his girlfriend in an hour, so turn on romantic lighting and music), warming up food in the microwave that the user prepared that morning, running diagnostic checks on all systems in the home, checking the weather forecast for tomorrow's work, checking news of the user's interest, checking the user's calendar and to-do list for reminders, checking the phone answering system / messaging system / email and reporting verbally using a dialogue system / speech synthesis (e.g., For example, a task may be to remind a user's mother about her birthday and call her, prepare a report (e.g., using a tool for reminding as described above) and perform the report (e.g., using audible tools such as speakers / HiFi / speech synthesis / voice / voice / music / song / sound field / background sound field / dialogue system, etc.; using visual tools such as TV / entertainment system / computer / notebook / smartpad / display / lighting / color / brightness / pattern / symbol, using haptic tools / virtual reality tools / gestures / tools, using smart devices / appliances / utensils / furniture / built-in furniture, using web tools / servers / cloud servers / fog servers / edge servers / home networks / mesh networks, using messaging tools / notification tools / communication tools / scheduling tools / email, using a user interface / GUI, using scents / smells / aromas / tastes, using neural tools / neurological tools, using a combination, etc.). A task may be to turn on an air conditioner / heater / ventilation system in advance or adjust the temperature setting of a smart thermostat in advance.When the user moves from the entrance to the living room, the tasks may be to turn on the living room lights, open the living room curtains, open the windows, turn off the entrance lights behind the user, turn on the TV and set-top box, set the TV to the user's favorite channel, adjust the appliances according to the user's preferences and conditions / states (e.g., adjust the lights and select / play music to create a romantic atmosphere), etc.

[0122] Another example could be: When a user wakes up in the morning, the tasks could be to detect that the user is moving in the bedroom, open the blinds / curtains, open the windows, turn off the alarm clock, adjust the indoor temperature from a night temperature profile to a day temperature profile, turn on the bedroom lights, turn on the restroom lights as the user approaches the restroom, check a wireless or streaming channel to play the morning news, turn on the coffee maker and preheat water, turn off the security system, etc. When a user is walking from the bedroom to the kitchen, the tasks could be to turn on the kitchen and hallway lights, turn off the bedroom and restroom 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 and open the kitchen window to let in fresh air, unlock the back door so the user can check out the backyard, adjust the kitchen temperature setting, etc. Another example could be: When a user leaves home for work, the tasks may be to detect the user's departure, play farewell and / or goodbye messages, open / close the garage door, turn on / off garage and driveway lights, turn off / dim lights to save energy (in case the user forgets), close / lock all windows / doors (in case the user forgets), turn off appliances (especially stoves, ovens, microwaves), turn on / arm the home security system to protect the home against intruders, adjust the air conditioning / heating / ventilation system to an "away" profile to save energy, send alerts / reports / updates to the user's smartphone, etc.

[0123] Motion includes no motion, static motion, motionless motion, deterministic motion, transient motion, tipping motion, repetitive motion, periodic motion, pseudo-periodic motion, periodic motion associated with breathing, periodic motion associated with heartbeat, periodic motion associated with living things, periodic motion associated with machines, periodic motion associated with man-made objects, periodic motion associated with nature, complex motion with transient and periodic elements, repetitive motion, non-deterministic motion, stochastic motion, chaotic motion, random motion, complex motion with non-deterministic and deterministic elements, stationary random motion, pseudo-stationary random motion, periodic stationary random motion, non-stationary random motion, periodic autocorrelation function Stationary random motion with an ACF, random motion with a periodic ACF over a period of time, random motion that is pseudo-stationary over a period of time, random motion with a pseudo-periodic component of the instantaneous ACF over a period of time, machine motion, 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 electromagnetic signatures, subsurface motion, earthquake motion, plant motion, animal motion, human motion, normal motion, abnormal motion, dangerous motion, warning motion, suspicious motion, rain, fire, flood, tsunami, explosion, collision, imminent collision, human body motion, head motion, face motion, eye motion, mouth motion, tongue motion, neck motion, The motions may include at least one of finger movements, hand movements, arm movements, shoulder movements, body movements, chest movements, abdominal movements, hip movements, leg movements, foot movements, body joint movements, knee movements, elbow movements, upper body movements, lower body movements, skin movements, subcutaneous movements, subcutaneous tissue movements, blood vessel movements, intravenous movements, organ movements, heart movements, lung movements, stomach movements, intestinal movements, bowel movements, eating movements, breathing movements, facial expressions, eye expressions, mouth expressions, speaking movements, singing movements, eating movements, gestures, hand gestures, arm gestures, keystrokes, typing strokes, user interface gestures, human-machine interaction, walking, dancing movements, coordinated movements, and / or coordinated body movements.The heterogeneous ICs of the Type 1 device and / or any Type 2 receiver may include a low noise amplifier (LNA), a power amplifier, a transmit / receive switch, a medium access controller, a baseband radio, a 2.4 GHz radio, a 3.65 GHz radio, a 4.9 GHz radio, a 5 GHz radio, a 5.9 GHz radio, a sub-6 GHz radio, a sub-60 GHz radio, and / or another radio.

[0124] A heterogeneous IC may include a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory for execution by the processor. The IC and / or any processor may include at least one of a general-purpose processor, a special-purpose processor, a microprocessor, a multiprocessor, a multicore processor, a parallel processor, a CISC processor, a RISC processor, a microcontroller, a central processing unit (CPU), a graphical processor unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an embedded processor (e.g., ARM), a logic circuit, other programmable logic device, discrete logic, and / or a combination. The heterogeneous ICs are used in broadband networks, wireless networks, mobile networks, mesh networks, cellular networks, wireless local area networks (WLANs), wide area networks (WANs), and metropolitan area networks (MANs), WLAN standards, WiFi, LTE, 802.11 standards, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11ad, 802.11af, 802.11ah, 802.11ax, 802.11ay, mesh network standards, 802.15 standards, 802.16 standards, cellular network standards, 3G, 3.5G, 4G, beyond 4G, 4.5G, 5G, 6G, 7G, 8G, 9G, Bluetooth, It may support Low-Energy (BLE), Zigbee, WiMax, and / or other wireless network protocols.The processor may include a general-purpose processor, a special-purpose processor, a microprocessor, a microcontroller, an embedded processor, a digital signal processor, a central processing unit (CPU), a graphical processing unit (GPU), a multiprocessor, a multi-core processor, and / or a processor with graphics capabilities, and / or a combination thereof. The memory may be volatile, non-volatile, random access memory (RAM), read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), a hard disk, flash memory, CD-ROM, DVD-ROM, magnetic storage, optical storage, organic storage, a storage system, a storage network, network storage, cloud storage, edge storage, local storage, external storage, internal storage, or any other form of non-transitory storage media known in the art. The instruction sets (machine-executable code) corresponding to the method steps may be directly embedded in hardware, software, firmware, or any combination thereof. The instruction set may be embedded, pre-loaded, loaded at startup, loaded on-the-fly, loaded on demand, pre-installed, installed, and / or downloaded. The presentation may be audiovisual, graphical (e.g., using a GUI), textual, symbolic, or mechanical.

[0125] basic calculations The computational workload associated with the method is shared among the processor, the Type 1 heterogeneous wireless device, the Type 2 heterogeneous wireless device, a local server, a cloud server, and another processor. Operations, pre-processing, processing, and / or post-processing can be applied to the data (e.g., TSCI, autocorrelation). The operations can be pre-processing, processing, and / or post-processing. The pre-processing, processing, and / or post-processing can be operations. The operations can include pre-processing, processing, post-processing, computation of operand functions, filtering, linear filtering, nonlinear filtering, folding, grouping, energy consumption, low-pass filtering, bandpass filtering, high-pass filtering, median filtering, rank filtering, quartile filtering, percentile filtering, mode filtering, finite impulse response (FIR) filtering, infinite impulse response (IIR) filtering, moving average (MA) filtering, autoregressive (AR) filtering, autoregressive moving average (AR) filtering, and autoregressive moving average (AM) filtering. averaging (ARMA) filtering, selective filtering, adaptive filtering, interpolation, decimation, subsampling, upsampling, resampling, time correction, time base correction, phase correction, magnitude correction, phase cleaning, magnitude cleaning, matched filtering, enhancement, restoration, noise removal, smoothing, signal conditioning, enhancement, restoration, spectral analysis, linear transforms, nonlinear transforms, frequency transforms, inverse frequency transforms, Fourier transforms, wavelet transforms, Laplace transforms, Hilbert transforms, Hadamard transforms, trigonometric transforms, sine transforms, cosine transforms, power of two transforms, sparse transforms, graph-based transforms, graph signal processing, fast transforms, transforms combined with zero padding, cyclic padding, padding, zero padding, feature extraction, decomposition, projection, orthogonal projection, non-orthogonal projection, over-complete projection, eigenvalue decomposition, singular value decompositionvalue decomposition (SVD), principal component analysis (PCA), independent component analysis (ICA), grouping, sorting, thresholding, soft thresholding, hard thresholding, clipping, soft clipping, first derivative, second derivative, higher derivative, convolution, multiplication, division, addition, subtraction, integration, maximization, minimization, local maximization, local minimization, cost function optimization, neural networks, recognition, labeling, training, clustering, machine learning, supervised learning, unsupervised learning, semi-supervised learning, comparison with another TSCI, similarity score calculation, quantization, vector quantization, matching pursuit, compression, encryption, coding, storage, transmission, normalization, time normalization, frequency domain normalization, classification, clustering, labeling, tagging, learning, detection, estimation, learning networks, mapping, remapping, dilation, storage, retrieval, transmission The operations may include: receiving, representing, merging, combining, splitting, tracking, monitoring, matched filtering, Kalman filtering, particle filters, interpolation, extrapolation, importance sampling, Monte Carlo sampling, compressed sensing, representing, merging, combining, splitting, scrambling, error protection, forward error correction, do nothing, time-varying processing, adjusted average, weighted average, arithmetic average, geometric average, harmonic average, average over selected frequencies, average over antenna links, logical operations, permutation, combination, sorting, AND, OR, XOR, union, logical and, vector addition, vector subtraction, vector multiplication, vector division, inverse, norm, distance, and / or another operation. The operations may be pre-processing, processing, and / or post-processing. The operations may be applied jointly to multiple time series or functions.

[0126] Functions (e.g., functions of operands) include scalar functions, vector functions, discrete functions, continuous functions, polynomial functions, feature functions, magnitude, phase, exponential functions, logarithmic functions, trigonometric functions, transcendental functions, logic functions, linear functions, algebraic functions, nonlinear functions, piecewise linear functions, real functions, complex functions, vector-valued functions, inverse functions, derivatives of a function, integrals of a function, circular functions, functions of another function, one-to-one functions, one-to-many functions, many-to-one functions, many-to-many functions, zero crossings (zero crossing), absolute value function, indicator function, mean, mode, median, range, statistics, variance, arithmetic mean, geometric mean, harmonic mean, trimmed mean, percentile, square, cube, root, power, sine, cosine, tangent, cotangent, secant, cosecant, elliptic function, parabolic function, hyperbolic function, game function, zeta function, absolute value, threshold method, limit function, floor function, rounding function, sign function, quantization, piecewise constant function, composition function, function of functions, operations (e.g., filtering) time functions processed by (processing), stochastic functions, probability functions, random functions, ergodic functions, stationary functions, deterministic functions, periodic functions, transformations, frequency transformations, inverse frequency transformations, discrete time transformations, Laplace transforms, Hilbert transforms, sine transforms, cosine transforms, trigonometric transforms, wavelet transforms, integer transforms, power of two transforms, sparse transforms, projections, decompositions, principal component analysis (PCA), independent component analysis (ICA), neural networks, feature extraction, moving functions number, moving window function for adjacent items in a time series, filtering function, convolution, mean function, variance function, statistical function, short-time transform, discrete transform, discrete 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, sparse approximation, arbitrary decomposition, graph-based processing, graph-based transform, graph signal processing, classification, labeling, learning, machine learning, detection, estimation, feature extraction, learning networks, feature extraction, denoising, signal enhancement, coding, encryption, mapping, remapping, vector quantization, low-pass filtering, high-pass filtering, bandpass filtering, matched filtering, Kalman filtering, pre-processing, post-processing, particle filter, FIR filtering, IIR filtering, autoregressive (AR) filtering, adaptive filtering, first derivative, higher-order derivative, integral, zero crossing, smoothingThese may include median filtering, mode filtering, sampling, random sampling, resampling functions, downsampling, upsampling, interpolation, extrapolation, importance sampling, Monte Carlo sampling, compressed sensing, statistics, short-term statistics, long-term statistics, mean, variance, autocorrelation function, cross-correlation, moment generating function, time averaging, weighted average, special functions, Bessel functions, error functions, complementary error functions, beta functions, gamma functions, entire functions, Gaussian functions, Poisson functions, etc.

[0127] Machine learning, training, discriminative training, deep learning, neural networks, continuous-time processing, distributed computing, distributed storage, and acceleration using GPUs / DSPs / coprocessors / multicores / multiprocessing may be applied to the steps (or each step) of the present disclosure. Frequency transforms 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 combinations of transforms, Fourier transforms with zero padding, and / or other transforms. Fast and / or approximate versions of transforms may be implemented. Transforms may be implemented using floating-point and / or fixed-point arithmetic. The inverse frequency transform may include an inverse Fourier transform, an inverse Laplace transform, an inverse Hadamard transform, an inverse Hilbert transform, an inverse sine transform, an inverse cosine transform, an inverse trigonometric transform, an inverse wavelet transform, an inverse integer transform, an inverse power of two transform, zero-padding and a combination of transforms, an inverse Fourier transform with zero padding, and / or another transform. Fast and / or approximate versions of the transforms may be implemented. The transforms may be implemented using floating-point and / or fixed-point arithmetic.

[0128] Quantities from the TSCI can be calculated, including motion, location, map coordinates, height, speed, acceleration, angle of motion, rotation, size, volume, time trend, time profile, periodic motion, frequency, transient, breathing, walking, action, event, suspicious event, dangerous event, alarm event, warning, belief, proximity, collision, power, signal, signal power, signal strength, received signal strength indicator. The statistics may include at least one of: RSSI (Radio Signal Indicator), signal amplitude, signal phase, signal frequency component, signal frequency band component, channel state information (CSI), map, time, frequency, time frequency, decomposition, orthogonal decomposition, non-orthogonal decomposition, tracking, respiration, heart rate, biometrics, infant, patient, machine, device, temperature, vehicle, parking lot, location, lift, elevator, space, road, fluid flow, home, room, office, house, building, warehouse, storage, system, ventilation, fan, pipe, duct, people, human, car, boat, truck, plane, drone, downtown, crowd, impact event, cyclostationary, environment, vibration, material, surface, 3D, 2D, local, global, presence, and / or another.

[0129] Sliding Window Algorithm The sliding time window may have a time-varying window width. It may be initially small to allow for rapid acquisition and may increase over time to a steady-state size. The steady-state size may relate to the frequency, repetitive motion, transient motion, and / or spatiotemporal information being monitored. Even in the steady state, the window size may be adaptively changed based on battery life, power consumption, available computational power, changes in the amount of target, the nature of the motion being monitored, etc. The time shift between two sliding time windows at adjacent time instances may be constant / variable / locally adaptive over time. If a shorter time shift is used, updates of any monitor may become more frequent, which may be used for fast-changing conditions, object motion, and / or objects. A longer time shift may be used for slower conditions, object motion, and / or objects. The window width / size and / or time shift may be changed according to user request / selection. The time shift may be changed automatically (e.g., controlled by a processor / computer / server / cloud server) and / or adaptively.

[0130] At least one characteristic of the function (e.g., an autocorrelation function, an autocovariance function, a cross-correlation function, a cross-covariance function, a power spectral density, a time function, a frequency domain function, a frequency transform) may be determined (e.g., by the object tracking server, a processor, a type 1 heterogeneous device, a type 2 heterogeneous device, and / or another device). The at least one feature of the function can include a local maximum, a local minimum, a local extremum, a local extremum with a positive time offset, a first local extremum with a positive time offset, an nth local extremum with a positive time offset, a local extremum with a negative time offset, a first local extremum with a negative time offset, an nth local extremum with a negative time offset, a constrained (with argument in constraint) maximum, a minimum, a constrained maximum, a constrained minimum, a constrained extremum, a slope, a derivative, a higher order derivative, a maximum slope, a minimum slope, a local maximum slope, a local maximum slope with a positive time offset, a local minimum slope, a constrained maximum slope, a constrained minimum slope, a maximum higher order derivative, a minimum higher order derivative, a constrained higher order derivative, a zero crossing, a zero crossing with a positive time offset, an nth zero crossing with a positive time offset, a zero crossing with a negative time offset, an nth zero crossing with a negative time offset, a constrained zero crossing, a zero crossing of a slope, a zero crossing of a higher order derivative, and / or another feature. At least one argument of the function can be identified that is associated with at least one characteristic of the function, and some quantity (e.g., spatiotemporal information of the object) can be determined based on the at least one argument of the function.

[0131] A characteristic (e.g., a characteristic of an object's movement within a location) may include at least one of an instantaneous characteristic, a short-term characteristic, a repeating characteristic, a recurring characteristic, a history, an incremental characteristic, a fluctuation characteristic, a deviation characteristic, a phase, a magnitude, a time characteristic, a frequency characteristic, a time-frequency characteristic, a decomposition characteristic, an orthogonal decomposition characteristic, a non-orthogonal decomposition characteristic, a deterministic characteristic, a stochastic characteristic, a probabilistic characteristic, an autocorrelation function (ACF), a mean, a variance, a statistic, a duration, a timing, a trend, a periodic characteristic, a long-term characteristic, a history characteristic, an average characteristic, a present characteristic, a past characteristic, a future characteristic, a predicted characteristic, a location, a distance, a height, a speed, a direction, a velocity, an acceleration, a change in acceleration, an angle, an angular velocity, an angular velocity of an object, an angular acceleration of an object, a change in angular acceleration of an object, an orientation of an object, an angle of rotation, a deformation of an object, a shape of an object, a change in shape of an object, a change in size of an object, a change in structure of an object, and / or a change in a characteristic of an object.

[0132] 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-like (SNR-like) parameter can be calculated for each pair of adjacent local maximums and minima. The SNR-like parameter can be a function (e.g., linear, logarithmic, exponential, monotonic) of the fraction of the quantity (e.g., power, magnitude, etc.) of the local maximum that is the same quantity as the local minimum. It can also be a function of the difference between the quantity of the local maximum and the quantity of the local minimum that is the same quantity.

[0133] Significant local peaks can be identified or selected. Each significant local peak can be a local maximum of an SNR-like parameter greater than a threshold T1 and / or a local maximum of an amplitude greater than a threshold T2. A persistence-based approach can be used to identify / calculate at least one local minimum and at least one local minimum in the frequency domain. A set of selected significant local peaks can be selected from the set of identified significant local peaks based on a selection criterion. Object features / spatiotemporal information can be calculated based on the set of selected significant local peaks and frequency values ​​associated with the set of selected significant local peaks.

[0134] In one embodiment, the selection criteria may always correspond to selecting the strongest peak within a range. While the strongest peak may be selected, peaks that are not selected may still be significant (fairly strong). Significant peaks that are not selected may be stored and / or monitored as "saved" peaks for use in future selection within a future sliding time window. As an example, a particular peak (at a particular frequency) may appear uniformly over time. Initially, it may be significant but not selected (because other peaks may be strong). However, later on, the peak may become stronger and more dominant and may be selected. Once "selected," it may regress in time to become "selected" earlier if it was significant but not selected. In such a case, the regressed peak may replace a peak that was previously selected earlier. The replaced peak may be relatively weak or a peak that appears isolated in time (i.e., appears for only a short time in time). In another embodiment, the selection criteria may not correspond to selecting the strongest peak within a range. Instead, one may consider not only the "strength" of a peak but also the "signature" of the peak, which may be a peak that has occurred in the past, especially a peak that has been identified for a long time. For example, if a finite state machine (FSM) is used, the peak(s) may be selected based on the state of the FSM. The decision threshold may be adaptively calculated based on the state of the FSM.

[0135] Similarity scores and / or component similarity scores can be calculated (e.g., by a server, a processor, a Type 1 device, a Type 2 device, a local server, a cloud server, and / or another device) based on pairs of temporally adjacent CIs of a TSCI. The pairs can be from the same sliding window or two different sliding windows. Similarity scores can also be based on pairs of temporally adjacent or non-adjacent CIs from two different TSCIs. The similarity scores and / or component similarity scores can be / can include time reversal resonating strength (TRRS), correlation, cross-correlation, autocorrelation, covariance, cross-covariance, autocovariance, dot product of two vectors, distance score, norm, metric, statistical feature, discriminant score, metric, neural network, deep learning network, machine learning, training, discrimination, weighted averaging, preprocessing, denoising, signal conditioning, filtering, time correction, timing compensation, phase offset compensation, transform, component-wise operation, feature extraction, finite state machine, and / or another score. The features and / or spatiotemporal information can be determined / calculated based on the similarity score. Any threshold can be predetermined, adaptively determined, and / or determined by a finite state machine. The adaptive determination can be based on time, space, location, antenna, path, link, condition, battery life, remaining battery life, available power, available computational resources, available network bandwidth, etc. A threshold can be determined to apply to a test statistic to distinguish between two events (or two conditions, or two situations, or two states) A ​​and B. Data (e.g., CI, channel state information (CSI)) can be collected under A and / or B in a training situation. The test statistic can be calculated based on the data. The distribution of the test statistic under A can be compared to the distribution of the test statistic under B, and the threshold can be selected based on some criteria.Criteria may include maximum likelihood (ML), maximum aposterior probability (MAP), discriminative training, minimum Type 1 error for a given Type 2 error, minimum Type 2 error for a given Type 1 error, and / or other criteria. Thresholds may be adjusted to obtain different sensitivities to A, B, and / or other events / conditions / situations / states. Threshold adjustments may be automatic, semi-automatic, and / or manual. Threshold adjustments may be applied once, occasionally, frequently, periodically, infrequently, occasionally, and / or on demand. Threshold adjustments may be adaptive. Threshold adjustments may depend on the object, object's movement / location / direction / action within / at / at the location, object characteristics / spatiotemporal information / size / properties / habits / habits / behavior, location, features / fitted furniture / furniture / barriers / materials / machines / creatures / things / objects / boundaries / surfaces / mediums, map, map constraints, event / state / situation / condition, time, timing, duration, current state, past history, user, and / or personal preference, etc.

[0136] A stopping criterion for the iterative algorithm may be that the change in the current parameter (e.g., offset value) during the update during the iteration is less than a threshold value. The threshold value may be 0.5, 1, 1.5, 2, or another number. The threshold adjustment may be adaptive and may change as the iterations progress. For the offset value, the adaptive threshold may be determined based on the task, a specific value of the first time, 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 iteration rate. A local extremum may be determined as a corresponding extremum of the regression function in the regression window. The local extremum may be determined based on a set of time offset values ​​and a set of related regression function values ​​within the regression window. Each of the sets of related regression function values ​​associated with a set of time offset values ​​may be within a range from a corresponding extremum of the regression function within the regression window.

[0137] Searching for local extrema includes robust search, minimization, maximization, 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, calculus of variations, optimal control, dynamic programming, mathematical programming, multiobjective optimization, multimodal optimization, disjunctive programming, space mapping, infinite dimensional optimization, heuristics, metaheuristics, convex programming, semidefinite programming, cone linear programming, cone programming, integer programming, The methods may include quadratic programming, fractional programming, numerical analysis, simplex algorithms, iterative methods, gradient descent, subgradient methods, coordinate descent, conjugate gradient methods, Newton's algorithm, sequential quadratic programming, interior point methods, ellipsoid methods, reduced gradient methods, quasi-Newton methods, simultaneous perturbation stochastic approximation, interpolation methods, pattern search methods, line search, non-differentiable optimization, genetic algorithms, evolutionary algorithms, dynamic relaxation, hill climbing, particle swarm optimization, gravitational search algorithms, simulated annealing, mimetic algorithms, differential evolution, dynamic relaxation, stochastic tunneling, tabu search, reactive search optimization, curve fitting, least squares methods, simulation-based optimization, calculus of variations, and / or deformation. The search for local extrema may be associated with an objective function, a loss function, a cost function, a utility function, a fitness function, an energy function, and / or an energy function. The regression may be performed using a regression function to fit the sampled data (e.g., CI, features of CI, components of CI) or another function (e.g., an autocorrelation function) within a regression window. In at least one iteration, the length of the regression window and / or the location of the regression window may be varied. The regression function may be a linear function, a quadratic function, a cubic function, a polynomial function, and / or another function.

[0138] The regression analysis may minimize absolute error, squared error, higher-order error (e.g., third order, fourth order, etc.), robust error (e.g., squared error if the error magnitude is small, absolute error if the error magnitude is large, or Type I error if the error magnitude is small, or Type II error if the error magnitude is large), another error, a weighted sum of absolute errors (e.g., for a wireless transmitter with multiple antennas and a wireless receiver with multiple antennas, each pair of transmitter antenna and receiver antenna forms a link. Errors associated with different links may have different weights. One possibility is that some links and / or some components with large noise may have smaller or larger weights.), weighted sum-of-squares error, a weighted sum of higher-order errors, a weighted sum of robust errors, a weighted sum of another error, an absolute cost, a squared cost, a higher-order cost, a robust cost, another cost, a weighted sum of absolute costs, a weighted sum-of-squares cost, a weighted sum of higher-order costs, a weighted sum of robust costs, and / or a weighted sum of another cost.

[0139] The determined regression error may be an absolute error, a squared error, a higher-order error, a robust error, a further 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 further errors. The time offset associated with the maximum regression error (or minimum regression error) of the regression function for a particular function within the regression window may be the current time offset updated during the iteration. Local extrema may be searched for based on a quantity including 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 be an absolute error, a squared error, a higher-order error, a robust error, a further 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 further errors. The quantity may be compared to an F-distribution, a central F-distribution, another statistical distribution, a threshold, a threshold associated with a probability, a threshold associated with a probability of finding a false peak, a threshold associated with an F-distribution, a threshold associated with a central F-distribution, and / or a threshold associated with another statistical distribution.

[0140] The regression window may be determined based on at least one of the following conditions: the motion of the object, a quantity associated with the object, at least one feature and / or spatiotemporal information of the object associated with the motion of the object, an estimated location of a local extremum, a noise feature, an estimated noise feature, an F-distribution, a central F-distribution, another statistical distribution, a threshold, a preset threshold, a threshold associated with a probability, a threshold associated with a desired probability, a threshold associated with a probability of finding a false peak, a threshold associated with an F-distribution, a threshold associated with a central F-distribution, a threshold associated with another statistical distribution, a condition that the quantity at the window center is a maximum within the regression window, a condition that the quantity at the window center is a maximum within the regression window, a condition that for a particular value of a first time within the regression window, another regression window, there is only one of the local extrema of a particular function, and / or another condition. The width of the regression window may be determined based on the particular local extremum to be searched for. The local extrema are a first maximum, a second maximum, a higher-order maximum, a first maximum with a positive time offset, a second maximum with a positive time offset, a higher maximum with a positive time offset, a first maximum with a negative time offset, a second maximum with a negative time offset, a higher maximum with a negative time offset, a first minimum, a second minimum, a higher minimum, a first minimum with a positive time offset, a second minimum with a positive time offset, a higher minimum with a positive time offset, a negative The local extrema may include a first local minimum with a time offset value of 0, a second local minimum with a negative time offset value, a higher local minimum with a negative time offset value, a first local extrema, a second local extrema, a higher local extrema, a first local extrema with a positive time offset value, a second local extrema with a positive time offset value, a higher local extrema with a positive time offset value, a first local extrema with a negative time offset value, a second local extrema with a negative time offset value, and / or a higher local extrema with a negative time offset value.

[0141] The current parameters (e.g., time offset value) may be initialized based on a target value, a target profile, a trend, a past trend, a current trend, a target speed, a speed profile, a target speed profile, a past speed trend, an object motion, at least one feature and / or spatiotemporal information of the object associated with the object motion, a position quantity of the object, an initial velocity of the object associated with the object motion, a predefined value, an initial width, a duration of the regression window, a value based on the carrier frequency of the signal, a bandwidth of the signal, an antenna quantity associated with the channel, a noise characteristic, and / or an adaptive value. The current time offset may be the center, left, right, and / or another fixed relative location of the regression window.

[0142] In the presentation, the information may be displayed on a map of the location. Information may include location, zone, region, area, modified location, approximate location, location relative to a map of locations, location relative to a segmentation of locations, direction, path, path relative to a map and / or segmentation, trace (e.g., location within a time window such as the past 5 seconds, or the past 10 seconds, where the time window duration may be adaptively adjusted, where the time window duration may be adaptively adjusted with respect to speed, acceleration, etc.), path history, approximate area / zone along the path, history / summary of past locations, history of past locations of interest, frequently visited areas, customer traffic, crowd distribution, crowd behavior, crowd control information, speed, acceleration, movement statistics, breathing rate, heart rate, presence / absence of movement, presence / absence of people, presence / absence of vital signs, gestures, gesture control (control of devices using gestures), location-based gesture control, location-based action information, identification (ID) of objects involved (e.g., pet, person, autonomous machine / device, vehicle, drone, car, boat, bicycle, autonomous vehicle, etc.), machine, fan-equipped machine, air conditioner, TV, machine with moving parts), user (e.g., person) identification, user information, user location / speed / acceleration / direction / movement / gesture / gesture control / movement tracking, user ID, user activity, user status, user sleep / rest characteristics, user emotional state, user vital signs, location environment information, location weather information, earthquake, explosion, storm, rain, fire, temperature, collision, impact, vibration, event, door open event, door close event, window open event, window close These may include rose events, fall events, burning events, freezing events, water-related events, wind-related events, air-motion events, accident events, pseudo-cyclic events (e.g., running on a treadmill, jumping up and down, skipping rope, somersaults, etc.), crowd events, vehicle events, and user gestures (e.g., hand gestures, arm gestures, foot gestures, leg gestures, body gestures, head gestures, face gestures, mouth gestures, eye gestures, etc.).

[0143] Location may be two-dimensional (e.g., by two-dimensional coordinates), three-dimensional (e.g., by three-dimensional coordinates). Location may be relative (e.g., with respect to a map), or relational (e.g., midpoint between point A and point B, around a corner, on the stairs, on a table, on the ceiling, on the floor, on a sofa, near point A, distance R from point A, within radius R from point A, etc.). Location may be displayed in Cartesian coordinates, polar coordinates, and / or another representation. Information (e.g., location) may be marked with at least one symbol. Symbols may change over time. Symbols may flash and / or vibrate, with or without changing color / intensity. Size may change over time. Orientation of symbols may change over time. The symbols may be numbers reflecting momentary quantities (e.g., user's vital signs / respiratory rate / heart rate / gesture / condition / status / action / movement, temperature, network traffic, network connectivity, device / machine status, remaining device power, device status, etc.). The rate of change, size, orientation, color, intensity, and / or symbol may reflect the respective movement. Information may be displayed visually and / or explained verbally (e.g., using pre-recorded audio or speech synthesis). Information may be written in text. Information may also be displayed in mechanical ways (e.g., animated gadgets, moving parts).

[0144] The user interface (UI) device may be a smartphone (e.g., iPhone, Android phone), a tablet (e.g., iPad), a laptop (e.g., notebook computer), a personal computer (PC), a device with a graphical user interface (GUI), a smart speaker, a device with voice / audio / speaker capabilities, a virtual reality (VR) device, an augmented reality (AR) device, a smart car, an in-car display, a voice assistant, an in-car voice assistant, etc. The map may be two-dimensional, three-dimensional, and / or higher dimensional (e.g., a time-varying 2D / 3D map). Walls, windows, doors, entrances, exits, and restricted areas may be marked on the map. The map may include a floor plan of the facility. The map may have one or more layers (overlays). The map may be a maintenance map including water pipes, gas pipes, wiring, cabling, air ducts, crawl spaces, ceiling layouts, and / or basement layouts. The location may be divided into multiple zones, e.g., bedroom, living room, storage room, hallway, kitchen, dining room, lobby, garage, first floor, second floor, restroom, office, conference room, reception area, various office areas, various warehouse areas, various facility areas, etc. The segments / zones / areas may be displayed in a map. Different regions may be color-coded. Different regions may be presented with characteristics (e.g., color, brightness, color depth, texture, animation, flashing, flash rate, etc.). The logical segmentation of the location may be performed using at least one heterogeneous Type 2 device, or server, or cloud server, etc.

[0145] This is an example of the disclosed system, apparatus, and method. Stephan and his family want to install the disclosed wireless motion detection system to detect motion in their 2,000-square-foot, two-story townhouse in Seattle, Washington. Because his house has two floors, Stephan has decided to use one Type 2 device (labeled A) and two Type 1 devices (labeled B and C) on the first floor. The first floor of his house has three main rooms: the kitchen, dining room, and living room, which are aligned in a straight line, with the dining room in the middle. The kitchen and living room are at opposite ends of the house. He has placed a Type 2 device (A) in the dining room, one Type 1 device (B) in the kitchen, and another Type 1 device (C) in the living room. By arranging the devices in this way, he is using the motion detection system to practically divide the first floor into three zones: the dining room, the living room, and the kitchen. When motion is detected by the AB and AC pairs, the system will analyze the motion information and associate the motion with one of three zones.

[0146] When Stephan and his family go away for the weekend (e.g., to go camping during a long weekend vacation), Stephan turns on the 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 Stephan (e.g., an SMS text message, email, push message to the mobile phone app, etc.). If Stephan pays a monthly fee (e.g., $10 / month), a 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 implements security procedures for Stephan (e.g., calling him to confirm the problem, sending someone to check the house, contacting the police on Stephan's behalf, etc.). Stephan loves his elderly mother and is concerned about her well-being when she is home alone. When his mother is alone and the rest of the family is out (e.g., going to work or shopping, or going on vacation), Stephan turns on the motion detection system using his mobile phone app to make sure his mother is okay. He then uses the mobile app to monitor his mother around the house. When Stephan uses the mobile app to see his mother moving between three areas of the house following her daily routine, Stephan knows his mother is doing well. Stephan is grateful that the motion detection system can help him monitor his mother's well-being while he is out.

[0147] On a typical day, his mother would wake up around 7:00 AM. She would spend about 20 minutes making breakfast in the kitchen. Then, she would eat breakfast in the dining room for about 30 minutes. Then, she would do her daily exercise in the living room and then sit on the living room couch and watch her favorite TV show. The motion detection system allows Stephan to see the timing of movement in each of the three areas of the house. If the movement matches the daily routine, Stephan knows that his mother must be doing well. However, if the movement pattern becomes abnormal (e.g., no movement until 10:00 AM, or she spends too long in the kitchen, or she remains motionless for too long), Stephan would suspect something is wrong and would call his mother to check on her. Stephan could also have someone (e.g., family, neighbors, paid personnel, friends, social workers, service providers) check on his mother.

[0148] One day, Stephan wants to relocate his Type 2 device. He simply unplugs it from the original AC power plug and plugs it into another AC power plug. He's pleased that our wireless motion detection system is plug-and-play, and relocating it doesn't affect the system's operation. It works immediately after powering it on. Later, Stephan is convinced that our wireless motion detection system can truly detect motion with high accuracy and very low alarms, and he can truly monitor motion on the first floor using the mobile app. He decides to install a similar setup (i.e., one Type 2 device and two Type 1 devices) on the second floor to monitor the bedrooms on the second floor. Once again, he finds that system setup is extremely simple; he simply plugs the Type 2 and Type 1 devices into the AC power plugs on the second floor. No special installation is required. He can then use the same mobile app to monitor motion on both the first and second floors. Each Type 2 device on the first / second floor can interact with all Type 1 devices on both the first and second floors. Stephen will be pleased to learn that doubling his investment in Type 1 and Type 2 devices will more than double the capacity of the combined system.

[0149] According to various embodiments, each CI (CI) may include at least one of channel state information (CSI), frequency domain CSI, frequency domain CSI associated with at least one subband, time domain CSI, CSI in a domain, channel impulse response (CIR), channel frequency response (CFR), channel characteristics, channel filter response, CSI of a wireless multipath channel, information of the wireless multipath channel, timestamp, aiding information, data, metadata, user data, account data, access data, security data, session data, status data, monitor data, household data, identification information (ID), device data, network data, neighborhood 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 wireless transmitter / receiver with an antenna, analog circuitry, a power supply, a processor, a memory, etc.) and corresponding software components. According to various embodiments of the present teachings, the disclosed system includes a bot (referred to as a Type 1 device) and an origin (referred to as a Type 2 device) for detecting and monitoring vital signs, each device comprising a transceiver, a processor, and a memory.

[0150] The disclosed system is applicable in many cases. In one embodiment, the Type 1 device (transmitter) can be a small WiFi-enabled device placed on a table. It can 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. In one embodiment, the Type 2 device (receiver) can be a WiFi-enabled device placed on a table. It can 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 can be placed in or near a conference room to count people. Type 1 and Type 2 devices can be part of a health monitoring system for elderly people, monitoring their daily activities and any signs of symptoms (e.g., dementia, Alzheimer's disease). Type 1 and Type 2 devices can be used in baby monitors to monitor the vital signs (breathing) of live infants. Type 1 and Type 2 devices can be placed in bedrooms to monitor sleep quality and sleep apnea. Type 1 and Type 2 devices can be placed in cars to monitor the health of passengers and drivers, detect driver sleep, and detect infants left inside the car. Type 1 and Type 2 devices can be used in logistics to prevent human trafficking by monitoring any humans hiding in trucks and containers. Type 1 and Type 2 devices can be deployed by emergency responders in disaster areas to search for victims trapped in rubble. Type 1 and Type 2 devices can be deployed in an area to detect the breathing of any intruders. There are many applications for non-wearable wireless respiratory monitors.

[0151] The hardware modules can be configured to include either a Type 1 transceiver or a Type 2 transceiver. The hardware modules may be sold to or used by various brands to design, build, and sell end products. Products using the disclosed systems and / or methods may be home / office security products, sleep monitor products, WiFi products, mesh products, TVs, STBs, entertainment systems, HiFi, speakers, home appliances, lamps, stoves, ovens, microwaves, tables, chairs, beds, shelves, tools, kitchen utensils, torches, vacuum cleaners, smoke detectors, sofas, pianos, fans, doors, windows, door / window handles, locks, smoke detectors, car accessories, computing devices, office devices, air conditioners, heaters, pipes, connectors, monitor cameras, access points, computing devices, mobile devices, LTE devices, 3G / 4G / 5G / 6G devices, gaming devices, glasses, glass panels, VR goggles, necklaces, watches, waistbands, belts, wallets, pens, hats, wearables, implantable devices, tags, parking tickets, smartphones, etc.

[0152] The summary may include at least one of an analysis, a selected time window, subsampling, a transformation, a projection, etc. The display may include at least one of a monthly chart, a weekly chart, a daily chart, a simplified chart, a detailed chart, a cross-sectional chart, a small form factor chart, a large form factor chart, a color-coded chart, a comparison chart, a summary chart, an animation, a web chart, an audio announcement, and another depiction of cyclical features of recurring motion.

[0153] A Type 1 device and / or a Type 2 device may be an antenna, a device having an antenna, a device having an interface that attaches / connects / links to an antenna, a device having a wireless transceiver, a device having a wireless transmitter, a device having a wireless receiver, an Internet of Things (IoT) device, a device having a wireless network, a device having both wired and wireless networking capabilities, a device having a wireless integrated circuit (IC), a Wi-Fi device, a device having a Wi-Fi chip (e.g., 802.11a / b / g / n / ac / ax compliant, etc.), a Wi-Fi access point (AP), a Wi-Fi client, a Wi-Fi router, a Wi-Fi repeater, a Wi-Fi hub, a Wi-Fi mesh network router / hub / AP, a wireless mesh network router, an ad-hoc network device, a wireless mesh network device, a mobile device (e.g., 2G / 2.5G / 3G / 3.5G / 4G / LTE / 5G / 6G / 7G, etc.), a cellular device, a mobile network base station, a mobile network hub, mobile network enabled device, LTE device, device having an LTE module, mobile module (e.g., circuit board having a mobile enabled chip (IC) such as a Wi-Fi chip, LTE chip, BLE chip), device having a Wi-Fi chip (IC), LTE chip, BLE chip, mobile module, smartphone, companion device for smartphone (e.g., dongle, attachment, plug-in), dedicated device, plug-in device, AC powered device, battery powered device, device having processor / memory / instruction set, smart clock, smart stationery, smart pen, smart user interface, smart paper, smart mat, smart camera, smart television (TV), set-top box, smart microphone, smart speaker, smart refrigerator, smart oven, smart machine, smartphone, smart wallet, smart furniture, smart door, smart window, smart ceiling, smart floor, smart wall, smart table, smart chair, smart bed, smart nightstand,The type 1 device and / or type 2 device may be a smart air conditioner, smart heater, smart pipe, smart duct, smart cable, smart carpet, smart decoration, smart gadget, smart USB device, smart plug, smart dongle, smart lamp / lighting, smart tile, smart ornament, smart bottle, vehicle, smart car, smart AGV, drone, smart robot, laptop, tablet, computer, hard disk, network card, smart instrument, smart racket, smart ball, smart shoe, smart wearable, smart clothing, smart glasses, smart hat, smart necklace, smart food, smart pill, small device that moves within a living organism (e.g., within blood vessels, lymphatic system, digestive system, etc.). The type 1 device and / or type 2 device may be communicatively coupled to the Internet, another device that accesses the Internet (e.g., a smartphone), a cloud server, an edge server, a local server, and / or storage.

[0154] An exemplary low-complexity respiratory monitoring scheme is presented below that utilizes a fast Fourier transform for spectral analysis using channel frequency responses (CFRs) obtained from off-the-shelf WiFi devices. The scheme also leverages a finite state machine (FSM) to counteract subject motion or motion from other nearby subjects. The integration of FFT and FSM results in a scalable, low-complexity, and robust scheme for respiratory monitoring.

[0155] An exemplary flow diagram for respiration estimation and detection is shown in FIG. 5, which consists of the following steps:

[0156] 1. CFR Acquisition: Our scheme acquires CFR from off-the-shelf WiFi chips. For example, it can acquire 114 subcarriers in a CFR frame on a 5 GHz WiFi channel with a maximum transmit power of 20 dBm.

[0157] 2. CFR Sanitization: Due to the inevitable phase shift between the WiFi transmitter and receiver, there exists serious phase distortion in the CFR. To solve this problem, the phase of the CFR can be cleaned by conventional linear regression. The cleaned CFR can be written as G[k] on subcarrier k.

[0158] 3. CFR Normalization: The received power of a CFR varies over time. Unfortunately, if the employed CFR chip does not have an automatic gain control (AGC) value, the received power is not measurable. Therefore, the CFR can be normalized across all subcarriers so that each CFR has unit power. Although this procedure loses information, it still significantly improves performance. Now, the CFRs captured at different subcarriers and different time instances can be written as a CFR matrix G with dimensions K × N, where N is the number of collected CFR frames. The matrix is ​​as follows:

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[0159] 4. Inverse Fourier Transform: In this step, the inverse fast Fourier transform (IFFT) can be used to convert the filtered CFR into a time-domain CIR for noise removal. The reason why IFFT improves performance is as follows: the useful signal contained in MPCs with large time delays is less significant than in MPCs with short time delays, mainly due to the significant attenuation associated with the propagation distance of EM waves. By using IFFT, we can focus on the first few channel taps and ignore the channel taps with large time delays. At time t i The CIR takes the following form:

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[0160] 5.Timestamp Correction: Ideally, the time instance t i can be written as (i-1)Ts, where Ts is the CFR sampling interval. Nevertheless, in practice, other wireless systems may coexist with the respiratory tracking system. Due to the carrier-sense multiple access with collision avoidance (CSMA / CA) mechanism, the transmission of WiFi packets is not uniformly scheduled over the air. More specifically, a WiFi device will probe the medium before transmitting. If some other WiFi device is transmitting a packet, other WiFi networks in the same area will have to silently back off for a while and probe the medium again to see if there is any WiFi traffic going on. i =(i-1)Ts can no longer be assumed. To solve this problem, we can turn to interpolation techniques. N-1 ]. Then we can define the exact time instances to be interpolated as the actual timestamps t0, t1, t2, ..., t N-1 Based on this, a linear interpolation can be performed to the exact time instance. This results in a time-corrected CIR that can be written as:

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[0161] 7. Spectral analysis by FFT: In this step, for each channel tap, a fast Fourier transform (FFT) can be performed at the CIR, which results in a K′ spectral estimate. The energy spectrum of the kth tap in the uth frequency bin is given by:

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[0162] 8. Channel Tap Averaging: We can take the average over channel taps with indices in the range [0,S-1], where S is called the stripe width. The averaged energy spectrum is then given by:

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[0163] 10. Peak detection: location of peaks in the energy spectrum;

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[0164] 11. Respiratory rate estimation: Assuming the total number of remaining peaks U' after the previous step and the detected respiratory rate P, select the top P out of the U' peaks, and use the corresponding peak locations measured in Hz as the respiratory rate estimation for P people

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[0165] 12. Real-time Update: The proposed tracking system keeps updating the respiration rate estimates regularly. Here, we consider two other parameters: the window size C window and shift size C shift and both are measured in sample numbers. The first set of respiration rate estimates is window A second set of respiratory rate estimates is generated by performing spectral analysis on the CFR using the sample index [C shift ,C window +C shift -1], and the overlap between two adjacent windows is window -C shift It is given as C shift The smaller the value, the faster the real-time update rate.

[0166] In a realistic environment setting, motion from other people and / or objects is always present, which introduces motion interference into the respiratory tracking system. At the same time, the subject's movement under the monitor also injects interference. Both types of interference significantly degrade performance.

[0167] Considering this issue, we can add a proposed system with a finite state machine (FSM) consisting of several states to the proposed respiratory tracking system, which allows the proposed system to automatically adjust parameters such as peak detection threshold under different conditions. In this work, the FSM has four different states, as shown below. For convenience, the transitions are shown below for the single-person respiratory tracking case. The peak search range can be expressed as [u1,u2].

[0168] Initial State (INIT): This is the default state when the system is powered on. In this state, the system uses the default peak detection thresholds.

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[0169] Verification: To avoid detecting spurious peaks, an internal counter can be used to record the number of times a peak can be detected in succession. If a peak can be detected in consecutive γ time windows, the system switches its state to state "peak found". If the peak becomes weaker, the FSM switches to state "initial state".

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[0170] In this state, the system should zoom in on the adjacent region of estimated respiration rate. For example,

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[0171] PeakFound: Now the system determines that a genuine peak has been detected and can be used as a respiration rate estimate. To prevent potential error propagation, the system will no longer update the two thresholds shown in the "Verify" state. If the peak becomes weaker, the system will switch back to the "Initialize" state. Similarly, the system will

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[0172] Motion: In this state, the system determines that strong motion is present and therefore a reliable respiration rate estimate cannot be produced.

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[0173] The idea of ​​FSM to overcome the motion interference problem under the single-person case can be extended to the multi-person case by running multiple FSMs in parallel, each capturing the status of a particular person. The FSMs associated with different people interact with each other, i.e., they are not independent. To make the FSMs more predictable, the following rules can be defined:

[0174] 1. The first FSM to operate always selects the strongest peak in its frequency range. For example, the ith FSM, denoted as FSM(i), selects the strongest peak in its search range [u' 1,i ,u' 2,i ].

[0175] 2. To avoid overlapping of respiratory rate estimation, two FSMs cannot select the same peak in each time window. For example, if FSM(i) is at location

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[0176] 3. To avoid the situation of selecting peaks that encapsulate respiratory harmonics, FSM(i) is first

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[0177] Remark 1: Rule #2 cannot handle the problem of two people breathing at different rates. One person's breathing rate increases, the other decreases. Sooner or later, these two breathing rates will match each other and the proposed FSM will not be able to capture the second person's breathing rate (rendezvous problem).

[0178] Figure 10 visualizes an example of two FSMs sequentially selecting two peaks. Assume both peaks meet the threshold criteria. At time t1, FSM1 searches the energy spectrum and selects the strongest peak, represented by Peak 1. Then, FSM(2) comes along and tries to select Peak 1. However, since the first peak has already been taken by FSM(1), FSM(2) gives up on its selection and moves on to Peak 2.

[0179] The experiments are presented below and are carried out in an office environment. The experimental setup is shown in Figure 1. The adopted parameters are shown in Table 1. [Table 1]

[0180] Single-Person NLOS: We evaluate the performance of the proposed scheme for single-person NLOS respiration tracking. A total of five experiments were conducted, each lasting 2 minutes. The experimental setup is shown in Figure 1(a). The ground truth is given as [12.5, 15, 17.5, 20, 22.5] BPM in 2.5 BPM increments. The results are shown in Figure 12. As can be seen, when the ground truth is [12.5, 15, 17.5] BPM, the difference between the schemes with and without FSM is negligible. However, when the respiration rate increases to 20 and 22.5 BPM, the FSM-based scheme outperforms the scheme without FSM. This is mainly due to the fact that when a person breathes faster, their respiration rate is not as stable as when they breathe slowly, which leads to fluctuations in the respiration signal strength, which can be addressed using FSM. The exact performances by FSM are given as [97.65%, 94.53%, 92.86%, 94.53%, 93.88%], respectively.

[0181] Single-person NLOS with fan operation: In this experiment, an electronic fan in the same room as the subject can be turned on. Because the fan is an electronic device with a metal cover, it reflects some EM waves, which interferes with the CFR and reduces tracking performance. The setup is shown in Figure 1(b). Figure 13 compares the schemes with and without FSM. A respiration rate estimate of 0 indicates that the system cannot obtain a reliable respiration rate. Although using FSM does not fully recover the ground truth respiration rate (15 BPM), utilizing FSM still improves overall performance. The accuracy of FSM is 94.48%. Figure 14 compares the CDFs of the respiration rate, defined in (9). This also demonstrates the advantage of using FSM, implying that the CDF with FSM is steeper than the CDF without FSM.

[0182] Figure 2 shows the state transitions of the FSM-based scheme in this experiment. The various states shown in Figure 11 are encoded as follows: initial state → 0, verification → 1, peak finding → 2, and motion → 3. Figure 2 shows that the FSM responds correctly to the fan movement by occasionally switching to the motion state.

[0183] Another experiment can be performed using the same setup except that the breathing rate is 20 BPM, and the results are shown in Figure 15. Also, using FSM improves performance as indicated by a more intensive breathing rate estimation. The accuracy is given as 94.53%. For performance evaluation of multi-person breathing tracking, the setup shown in Figure 1(c) and Figure 1(b) can be used.

[0184] First, we can study the breathing performance of two people. The ground truth is given in [15,16.5] BPM. C with FSM window =300, C window = 450, and C windowThe respiration rate estimation for ρ = 600 is shown in Figure 16(a), Figure 16(b), and Figure 16(c), respectively. As can be seen, the time window size of 300 samples is insufficient to resolve the two different respiration rates. For durations of 45 and 60 seconds, the resolution improves when the window size is increased from 450 samples to 600 samples. The accuracy of respiration rate estimation under the three window sizes is [75.52%, 96.54%], [90.33%, 96.70%], and [99.61%, 99.43%], respectively. C without FSM window =300, C window = 450, and C window The results of respiratory rate estimation for =600 are shown in Figure 17(a), Figure 17(b), and Figure 17(c), respectively. Compared to the results using FSM, the performance degradation is very severe. In particular, window If = 600, the second person's respiratory rate cannot recover.

[0185] Figure 3 shows the performance of the proposed respiration tracking scheme when the subject stood and sat for 5 seconds during the measurement. Clearly, the proposed respiration tracking scheme overcomes the effects of such large movements and produces stable respiration rate estimates over time. This disclosure provides a robust vital sign tracking scheme that analyzes small time variations in CFR. For each antenna link, the proposed scheme converts CFR to CIR and performs spectral analysis on the CIR using an FFT. The spectra of different antenna links are then merged into one averaged spectrum. Persistence-based peak detection is then performed on the spectrum, which ultimately yields the respiration rate estimate. Furthermore, an FSM is introduced into the proposed system to allow the vital sign tracking system to adopt different thresholds in different states, which significantly improves the robustness of vital sign tracking. Extensive experimental results demonstrate that the proposed scheme can be successfully implemented in single-person as well as multi-person respiration tracking.

[0186] An exemplary description of the speed estimation method is as follows: The three receiving antennas are assumed to be aligned in a line parallel to the direction of movement. Furthermore, the speed of the receiving antennas can be assumed to vary slowly in time, i.e., within a short time, the speed can be approximated to a constant value v. The receiving antennas Rx2 and Rx3 can be grouped and called the leading antennas, and the receiving antennas Rx1 and Rx2 can be grouped and called the following antennas. This is because the following antennas follow the same trajectory as the leading antennas, but with an unknown delay τ. The speed is determined by the relationship

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[0187] Coin-sized Bot The present disclosure also relates to object tracking using a small (e.g., coin-sized, cigarette-pack-sized, or smaller) bot / origin. In the present disclosure, the wireless transmitter and / or wireless receiver may be part of a small, lightweight, first portable device. The first portable device may be wirelessly coupled to a second device. The second device may be a smartphone, iPhone, Android phone, smart device, smart appliance, smart vehicle, smart gadget, smart TV, smart refrigerator, smart speaker, smart watch, smart glasses, smart pad, iPad, computer, wearable computer, notebook computer, or gateway. The second device may be connected to a cloud server, a local server, and / or other servers via the Internet, a wired Internet connection, and / or a wireless Internet connection. The second device may be portable. The first portable device, the second device, the local server, and / or the cloud server may share computation and / or storage for any of the tasks (e.g., obtaining TSCI, determining object features associated with object motion, calculating time series power information, determining / calculating specific functions, searching for local extrema, classification, identifying specific values ​​of time offset, noise removal, processing, simplification, cleaning, wireless smart sensing tasks, CI extraction from wireless signals, switching, segmentation, trajectory estimation, map processing, correction, correction adjustment, adjustment, map correction, error detection, boundary hit check, thresholding, etc.) and information associated with the present disclosure (e.g., TSCI).

[0188] CSI-compatible chip The present disclosure also relates to communications hardware capable of providing CI. The communications hardware may be a WiFi-enabled chip (e.g., an integrated circuit or IC), a next-generation WiFi-enabled chip, an LTE-enabled chip, a 5G-enabled chip, a 6G / 7G / 8G-enabled chip, etc. The communications hardware calculates the CI and stores the CI in a buffer memory to make the CI available for extraction. The CI may include data related to channel state information (e.g., CSI) and / or at least one matrix. The at least one matrix can be used for channel equalization, beamforming, etc.

[0189] Passive speed estimation (walk cycle estimation, fall detection) Another aspect of the present disclosure relates to estimating one or more characteristics and / or spatiotemporal information of an object, such as speed, acceleration, walk cycle, gait, object motion, and events. More specifically, the present teachings relate to determining at least one characteristic and / or spatiotemporal information of an object based on time reversal techniques in a rich scattering environment. The characteristic and / or spatiotemporal information may relate to an event. The event may be monitored based on the characteristic and / or spatiotemporal information.

[0190] In one embodiment, a system, apparatus, and / or method for determining at least one feature and / or spatiotemporal information of an object (e.g., an object) associated with a motion (e.g., a current motion and / or a previous motion) of the object includes obtaining, using a processor, a memory communicatively coupled to the processor, and an instruction set (e.g., an instruction set) stored in the memory, one or more TSCIs (e.g., CIs and / or channel state information) of a wireless multipath channel (e.g., the wireless multipath channel). The at least one TSCI (e.g., CIs, or channel state information) is obtained from a wireless signal (e.g., a radio signal) transmitted over the wireless multipath channel. The wireless multipath channel is affected by the motion of the object. The system, apparatus, and / or method further includes determining the one or more feature and / or spatiotemporal information of the object associated with the motion of the object based on the one or more TSCIs.

[0191] One or more time-series power information may be calculated based on one or more TSCIs of the wireless multipath channel. Each power information may be associated with a CI of the wireless multipath channel. Both the power information and the CI may be associated with time. The CI vector and the power information vector may have the same number of elements. Each real element of the power information vector may be calculated based on the magnitude, phase, real part, imaginary part, and / or another function of the corresponding complex element of the CI. In particular, the real element of the power information vector may be based on the square of the magnitude of the corresponding complex element of the channel information.

[0192] The specific function may be determined based on a first time, a time offset, a first window of power information, and a second window of power information. Although power information is referred to herein, the specific function may be applied to CI and / or other information. The first window of power information may be a first subset of a specific time series of power information associated with the first time. The second window of power information may be a second subset of a specific time series of power information associated with a second time at a time offset from the first time. The first window and the second window may be the same size. The second window is offset in time from the first window. The first window and the second window may both be windows of the same specific time series of power information (and / or CI and / or other information). The cardinality of the first subset may be equal to the cardinality of the second subset. The specific function may be calculated for at least one value of the first time and for at least one value of the time offset for each of the first times. One or more features and / or spatiotemporal information of the object associated with the object's motion may be determined based on an articular function that calculates at least one value of a time offset. A specific value of the first time may be associated with the object's motion and at least one feature and / or spatiotemporal information of the object. The specific function may be a second function of at least one of a covariance function, a covariance-like function, an autocovariance function, an autocovariance-like function, a correlation function, a correlation-like function, an autocorrelation function, an autocorrelation-like function, a dot product, another function, a combination of any of these functions with a pre-processing function, a combination of any of these functions with a post-processing function, and a combination of any of these functions with at least one of frequency decomposition, time decomposition, time-frequency decomposition, and another decomposition.The second function is noise removal, smoothing, adjustment, enhancement, restoration, feature extraction, weighted averaging, low-pass filtering, bandpass filtering, high-pass filtering, median filtering, rank filtering, quartile filtering, percentile filtering, mode filtering, linear filtering, nonlinear filtering, finite impulse response (FIR) filtering, infinite impulse response (IIR) filtering, moving average (MA) filtering, autoregressive (AR) filtering, and autoregressive moving average (ARMA) filtering. The function may be at least one of: filtering, thresholding, soft thresholding, hard thresholding, soft clipping, maximization, minimization, cost function optimization, neural network, machine learning, supervised learning, unsupervised learning, semi-supervised learning, transform, Fourier transform, Laplace, Hadamard transform, transform, decomposition, selective filtering, adaptive filtering, derivative, first derivative, second derivative, higher order derivative, integral, zero crossing, indicator function, absolute transform, convolution, multiplication, division, pre-processing, post-processing, another transform, another processing, another filter, and a third function. The particular function may be an estimate of a weighted sum of squares of the autocorrelation function (ACF) of the electric field component of the received electromagnetic (EM) wave of the wireless signal.

[0193] The power information may include a frequency decomposition, a time decomposition, a time-frequency decomposition, and / or another decomposition. The other function may be determined based on a frequency of the frequency decomposition (or a variable / index of the other decomposition), a first time, a time offset, a first window of the power information, and a second window of the power information. The other function may be calculated for at least one value of the frequency of the frequency decomposition. The particular function may be calculated by averaging another function that calculates at least one value of the frequency of the frequency decomposition.

[0194] In another example, the power information may include a frequency subband decomposition. The other function may be determined based on a frequency subband of the frequency decomposition, a first time, a time offset, a first window of power information, and a second window of power information. The other function may be calculated for at least one instance of a frequency subband of the frequency decomposition. The particular function may be calculated by averaging the other function calculated for at least one instance of a frequency subband of the frequency decomposition. In another example, the power information may include a time-frequency decomposition. The other function may be determined based on a time-frequency division of the time-frequency decomposition, a first time, a time offset, a first window of power information, and a second window of power information. The other function may be calculated for at least one instance of a time-frequency division of the time-frequency decomposition. The particular function may be calculated by averaging the other function calculated for at least one instance of a time-frequency division of the time-frequency decomposition.

[0195] Calculating the specific function may include determining a preliminary function based on the first time, the third time, the time offset, the third window of power information, and the fourth window of power information. The third window of power information may be a third subset of the specific time series of power information associated with the third time. The fourth window of power information may be a fourth subset of the specific time series of power information associated with a fourth time offset from the third time. The preliminary function may be calculated for a specific value of the first time and a specific value of the time offset for at least one value of the third time close to the first time. The specific function for a specific value of the first time and a specific value of the time offset may be calculated by averaging the preliminary functions for the specific values ​​of the time offset calculated for the specific value of the first time and at least one value of the third time close to the first time.

[0196] At least one special feature and / or special spatiotemporal information (e.g., local maxima, minima, zero crossings, etc.) may be determined / calculated for a particular value of the first time. At least one particular value of the time offset may be identified, each associated with a respective special feature and / or spatiotemporal information (e.g., local maxima, minima, zero crossings, etc.) of the particular function for a particular value of the first time. At least one special feature and / or special spatiotemporal information of the object associated with motion of the object may be determined based on the at least one particular value of the time offset.

[0197] A search may be performed for a local extremum of a particular function for a particular value of a first time. The search may include the following steps: (1) a current time offset value may be initialized, followed by a recursion; (2) at each iteration of the recursion, a regression window around the current time offset value may be determined; (3) the particular function within the regression window around the current time offset value may be approximated with / by a regression function using regression analysis; (4) a regression error of the regression function with respect to the particular function may be determined; (5) the current time offset value may be updated based on the convexity of the regression function, other characteristics and / or spatiotemporal information of the regression function, and / or the regression error of the regression function with respect to the particular function within the regression window; and (6) performing the recursion iterations until at least one stopping criterion is met.

[0198] A local extremum of the particular function may be determined for a particular value of the first time based on a corresponding extremum of the regression function within the regression window. A particular value of the time offset associated with the local extremum of the particular function may be identified within the regression window. At least one feature of the object and / or spatiotemporal information associated with the object's motion may be calculated based on the particular value of the time offset.

[0199] In one example, if the regression function is locally convex within the regression window, the time offset associated with the maximum regression error of the regression function for a particular function within the regression window can be the updated current time offset. In another example, if the regression function is locally concave within the regression window, the time offset associated with the maximum regression error of the regression function for a particular function within the regression window can be the updated current time offset.

[0200] If the regression function is locally convex within the regression window, at least one of the time offsets associated with both ends of the regression window can be the updated current time offset.If the regression function is locally concave within the regression window, at least one of the time offsets associated with both ends of the regression window can be the updated current time offset.

[0201] At least one of the time offsets associated with both ends of the regression window may be an updated current time offset based on the local convexity of the regression function within the regression window. If the regression is locally convex in one part of the window and locally concave in another part of the window, the window size may be reduced.

[0202] The time offset closest to the time offset associated with the maximum regression error of the regression function for a particular function within the regression window may become the current time offset updated based on the condition.

[0203] One of the at least one characteristic and / or spatiotemporal information of the object associated with the object's motion may be the object's speed. The object's speed may be calculated based on a specific value of a time offset associated with a local extremum of a specific function for a specific value of a first time within a regression window. The object's speed at a certain time may be calculated based on a specific value of a time offset associated with a local extremum of a specific function for a specific value of a first time within a regression window. The specific value of the first time may be equal to the time. The object's acceleration at a specific time may be determined based on at least one value of the object's speed at each time close to the specific time. The object's acceleration at a specific time may be one of the at least one characteristic and / or spatiotemporal information of the object associated with the object's motion. The object's acceleration at a specific time may be determined based on at least one value of the object's speed at one or more times close to the specific time. Determining the acceleration of the object at a particular time may include approximating the speed of the object at one or more times with a time function, determining one or more slopes of the time function near the particular time, and calculating the acceleration based on the one or more slopes of the time function. The acceleration may be calculated as a function of the one or more slopes of the time function. The function may be an average, a weighted average, a median, a mode, a quartile, a percentile, a robust function, a linear function, a nonlinear function, and / or another function.

[0204] One of the one or more slopes of the time function may be determined as the difference between two values ​​of the object's speed at two adjacent times divided by the difference between two adjacent time values. The time function may be a piecewise linear function. The acceleration may be calculated as the slope of the piecewise linear function at a particular time. The one or more times may be within a time window around the particular time. The one or more times may include a time window around the particular time. The acceleration of the object may be an acceleration of the object associated with the object's motion at the particular time. The acceleration of the object at the particular time may be determined based on the object's speed calculated at multiple values ​​of time proximate the particular time. The object's speed may be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on at least one TSCI.

[0205] The object's speed may be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on a specific function calculated for a specific value of the first time (the specific value of the first time is one of multiple values ​​of time close to the specific time) and at least one value of the time offset. The object's speed may also be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on a time offset associated with a local extremum of the specific function for a specific value of the first time within a regression window (the specific value of the first time is equal to time). The object's speed at a specific value of the first time may be calculated as a quantity divided by a specific value of the time offset associated with a local extremum of the specific function within the regression window.

[0206] In another embodiment, one of the at least one feature and / or spatiotemporal information may be the speed of the object. The speed of the object at a first time may be calculated based on a specific value of a time offset associated with a local extremum of a specific function within the regression window. The local extremum may be a first maximum with a positive time offset and / or a first maximum with a negative time offset. The specific function may be a derivative of a specific autocorrelation function with respect to the time offset. The CI may be a CSI associated with a frequency decomposition. Each CSI may be a vector of complex components, each corresponding to a frequency in the frequency decomposition. An autocorrelation function for each component may be calculated for each frequency in the frequency decomposition based on a specific time sequence of the CSI. The feature autocorrelation may be calculated as an average of the autocorrelation functions for each component across the frequencies in the frequency decomposition.

[0207] The iterations may be applied at least once to obtain at least one extremum. Each extremum may be associated with a respective current time offset value, a respective regression window, a respective regression function, and / or a respective specific time offset value in each iteration. At least one feature and / or spatiotemporal information of the object associated with the object's motion may be calculated based on at least one of the specific time offset values ​​of the specific function within the regression window. The iterations may be applied twice to obtain a first local extremum and a second local extremum. The first local extremum may be associated with a first current time offset value, a first regression window, a first regression function, and / or a first specific time offset value. The second local extremum may be associated with a second current time offset value, a second regression window, a second regression function, and / or a second specific time offset value. At least one feature and / or spatiotemporal information of the object associated with the object's motion may be calculated based on the first specific time offset value and / or a second specific time offset value of the specific function within the regression window. The iteration may be applied twice to obtain a first local extremum, which is a first local maximum with a positive time offset, and a second local extremum, which is a first local maximum with a negative time offset. The first local extremum may be associated with a first current time offset value, a first regression window, a first regression function, and / or a first specific time offset value. The second local extremum may be associated with a second current time offset value, a second regression window, a second regression function, and / or a second specific time offset value. At least one feature of the object and / or spatiotemporal information associated with the object's motion may be calculated based on the first specific time offset value and / or the second specific time offset value of the specific function within the regression window.

[0208] The object's walk cycle may be determined based on at least one value of the object's speed and / or at least one value of the object's acceleration. The object's walk cycle may also be determined based on at least one value of the object's speed and / or at least one value of the object's acceleration. The object's walk cycle may be determined by detecting a sequence of periods of positive acceleration and negative acceleration. The object's walk cycle may be determined by detecting a sequence of periods of increasing speed and periods of decreasing speed. The object's walk cycle may be determined by detecting a sequence of periods of positive speed and negative speed. The object's walk cycle may be determined by detecting an underlying cyclical behavior of the object's speed, a change in the object's speed, and / or an acceleration of the object during a related period. The object's walk cycle may be determined by detecting an underlying cyclical behavior of the object's maximum speed, a minimum object speed, a minimum object acceleration, a minimum object acceleration, a maximum change in the object's speed, and / or a minimum change in the object's speed during a related period. At least one walk cycle may be determined to be associated with the time period. A function of the object speed and / or the object acceleration may exhibit local maxima at locations associated with the time period. At least one walk cycle may be determined to be associated with the time period. A function of the object speed and / or the object acceleration may be determined to exhibit peak localizations having multiple pairs of local maxima and minima at associated multiple pairs of peak locations associated with the time period.

[0209] The period may be determined based on the distance between at least one of two adjacent maxima, two adjacent minima, a pair of adjacent maxima and minima, two maxima separated by a predetermined number of maxima, two minima separated by a predetermined number of minima, and a pair of maxima and minima separated by a predetermined number of extrema of the function. The function may include an autocorrelation function, an auto-correlation like function, an autocovariance function, an auto-covariance like function, a product of velocity and a shifted version of acceleration, a product of velocity and a shifted version of a periodic function of known period, a product of acceleration and a shifted version of a periodic function of known period, an optimization of a cost function, a constrained optimization of a cost function, an unconstrained optimization of a cost function, a Fourier transform, a Laplace transform, another transform, and / or another function.

[0210] At least one gait cycle of the object may be determined based on at least one value of the object's speed and at least one value of the object's acceleration. The object's speed may be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on the at least one TSCI. The object's acceleration may be determined based on the object's speed. At least one gait cycle of the object may be determined based on at least one value of the object's speed and at least one value of the object's acceleration. The object's speed may be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on a specific function calculated for a specific value of a first time (the specific value of the first time is one of multiple values ​​of time close to the specific time) and at least one value of a time offset. The object's acceleration may be determined based on the object's speed. At least one gait cycle of the object may be determined based on at least one value of the object's speed and at least one value of the object's acceleration. The object speed may be one of at least one feature of the object and / or spatiotemporal information associated with the object's motion, determined based on a time offset associated with a local extremum of a specific function for a specific value of a first time within a regression window (the specific value of the first time equals time). The object acceleration may be determined based on the object speed. The object's gait and / or events may be detected based on at least one of at least one segment of the TSCI, at least one segment of the time-series channel state information (CSI), at least one value of the specific function, at least one value of the object's speed, and / or at least one value of the object's acceleration, each based on at least one value of the object's speed.The object gait and / or event may be detected based on a time trend of at least one of the TSCI, the channel state information (CSI), the specific function, the object speed, and / or the object acceleration. Detecting the object gait and / or event may include determining the object gait and / or event using an engine based on a time trend of the object speed. Input to the engine may include at least one of at least one segment of the TSCI, at least one segment of the time series of the channel state information (CSI), at least one value of the specific function, at least one value of the object speed, and at least one value of the object acceleration. The engine may be a neural network and / or a classification engine.

[0211] The engine may be trained with training inputs in a training phase to generate at least one reference temporal trend associated with the object's known gait and known events. The engine's training input may be associated with the object's known gait and known events. The training may be at least one of discriminative training, decision-feedback training, machine learning, supervised learning, unsupervised learning, shallow learning, deep learning, and / or another training. Determining the object's gait and / or events using the engine may include obtaining a time trend, comparing the time trend with each of the reference temporal trends, and aligning the time trend of a first duration with each of at least one reference temporal trend of a different duration. Calculating at least one similarity score between the time trend and each of the aligned reference temporal trends and / or determining the object's gait and / or event as one of the object's known gaits and / or one of the known events based on the at least one similarity score.

[0212] The object speed may be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on at least one TSCI. The object speed may be one of at least one feature and / or spatiotemporal information of the object associated with the object's motion determined based on a specific function calculated for a specific value of a first time (the specific value of the first time is one of multiple values ​​of time close to the specific time) and at least one value of a time offset.

[0213] At least one threshold may be determined. At least one feature and / or spatiotemporal information of the object may be determined based on the at least one threshold. The at least one threshold may be determined based on at least one time series of training CI (CI) of the wireless multipath channel using a second processor, a second memory communicatively coupled to the second processor, and a second instruction set stored in the second memory. The second processor may be the first processor, a portion of the first processor, and / or connected to the first processor. Some resources of the second processor (hardware, network, communication, user interface, and / or software) may be shared with the first processor. The second memory may be the first memory, a portion of the first memory, connected to the first memory, and / or shared with the first memory. The second instruction set may be the first instruction set and / or a portion of the first instruction set. The second instruction set and the first instruction set may share some software libraries, some software routines, some hardware resources, some communication resources, and / or other resources. The second instruction set and the first instruction set may operate independently, cooperatively, jointly, sequentially, in parallel, alternatingly, and / or interactively.

[0214] At least one time series of training CI (CI) of the wireless multipath channel can be obtained from a second wireless signal (e.g., 140) transmitted through the wireless multipath channel during a training phase. The wireless multipath channel can be affected by training motion of a second object during the training phase. The training phase can be a training session, which can be performed once, occasionally, periodically, and / or on demand.

[0215] A first training CI may be obtained for at least one time series of the wireless multipath channel associated with target positive training motion of the second object during the training phase. The positive training motion may be target motion that is to be recognized, monitored, measured, studied, processed, detected, estimated, verified, and / or captured. A second training CI may be obtained for at least one time series of the wireless multipath channel associated with target negative training motion of the second object during the training phase. The negative training motion may be target motion that is to be ignored, missed, not monitored, not detected, estimated, not recognized, not verified, not captured, not measured, and / or not studied. At least one threshold may be determined based on at least one first quantity and / or at least one second quantity. The at least one threshold may be determined such that a first percentage of the first quantity is greater than, equal to, and / or less than the first threshold (not the at least one threshold). At least one threshold may be determined such that a second percentage of the second amount is greater than, equal to, and / or less than the second threshold (but not the at least one threshold). The first threshold may be greater than, equal to, and / or less than the second threshold. The first threshold may be the second threshold. The first percentage may be greater than, equal to, and / or less than the second percentage.

[0216] The first training CI of the at least one time series of the wireless multipath channel may be associated with a training movement of a second object within the monitored area during a training phase. The target positive training movement of the second object may be a training movement of the second object within the monitored area. The second training CI of the at least one time series of the wireless multipath channel may be associated with a training movement of a second object outside the monitored area during the training phase. The target negative training movement of the second object may be a training movement of the second object outside the monitored area.

[0217] The second object may be the first object. The second object may be a copy, replacement, backup, and / or replica of the first object. The second object may be another object similar to the first object. The second object may be similar to the first object in terms of structure, size, shape, functionality, periodicity, deformation characteristics, movement characteristics, speed, acceleration, gait, tendencies, habits, radio characteristics, and other characteristics.

[0218] When at least one Type 1 device interacts with at least one Type 2 device, processing, pre-processing, post-processing, and / or other processing may vary from device to device. The processing / pre-processing / post-processing / other processing may be based on location, orientation, direction, role, user-related characteristics, settings, configuration, available resources, available bandwidth, network connection, hardware, software, processor, co-processor, memory, battery life, available power, antenna, antenna type, antenna directional / unidirectional characteristics, power settings, and / or other parameters / characteristics of the device.

[0219] ACF-based breathing Another embodiment is a method / apparatus / device / system / software for a wireless monitoring system that includes transmitting a series of probe signals by an antenna of a first wireless device to at least one disparate target wireless receiver over a wireless multipath channel within a location.

[0220] The transmitting may use a processor, a memory, and a set of instructions. The first wireless device may have a heterogeneous integrated circuit (IC). Each of the at least one heterogeneous target wireless receiver may have a heterogeneous IC.

[0221] It further includes asynchronously acquiring at least one TSCI (TSCI) from the series of probe signals by each of the at least one heterogeneous target wireless receiver. The TSCI (TSCI) is a CI of a wireless multipath channel between the heterogeneous target wireless receiver and the first wireless device. It further includes asynchronously monitoring, based on each of the at least one TSCI (obtained from the series of probe signals by each of the heterogeneous target wireless receivers), respective motions associated with the location and / or respective objects within the location. The respective motions may be associated with respective events associated with at least one of the location and the respective objects within the location. Since more than one of the respective objects may be the same object, more than one heterogeneous target wireless receiver may monitor the same object. Two or more of the respective motions may be the same motion. Multiple heterogeneous target wireless receivers may be monitoring the same motion. The wireless monitoring system method / apparatus / device / system / software may include jointly monitoring the same motion based on the TSCIs of the multiple heterogeneous target wireless receivers. The respective motions monitored by each of the multiple heterogeneous target wireless receivers may be the same motion. The method / apparatus / device / system / software of the wireless monitoring system may include jointly monitoring the same object based on the TSCI of multiple heterogeneous target wireless receivers. Each object within a location monitored by each of the multiple heterogeneous target wireless receivers may be the same object. The method / apparatus / device / system / software of the wireless monitoring system may include monitoring each characteristic of each movement of each of the objects within the location. The method / apparatus / device / system / software of the wireless monitoring system may include monitoring a periodic characteristic of each movement within the location based on at least one TSCI.

[0222] Each TSCI (CI) may be a channel frequency response having two or more components. Each respective component may be associated with a respective frequency. The respective components (of each TSCI) associated with each frequency may form a respective component time series. The component time series may be associated with a respective frequency. Each TSCI may be divided into multiple component time series. Each respective component time series may be associated with a respective frequency.

[0223] The method / apparatus / device / system / software of the wireless monitoring system may include monitoring a respective movement based on a respective plurality of component time series of each of the at least one TSCI. Each component time series may be associated with a respective frequency. The method / apparatus / device / system / software of the wireless monitoring system may include monitoring a cyclical characteristic of each movement based on a respective plurality of component time series of each of the at least one TSCI. The cyclical characteristic of each movement may be monitored based on the cyclical characteristic of each component of two or more component time series of each of the at least one TSCI.

[0224] A periodic feature for each component of each of the two or more component time series of each of the at least one TSCI may be calculated. The periodic feature for each component may be associated with a respective component index. The periodic feature for each component may be monitored based on the periodic feature for each of the two or more component time series of each of the at least one TSCI.

[0225] A total periodic feature of each of the at least one TSCI can be calculated based on the periodic features of each component of the component time series of the TSCI. A periodic feature of each movement can be monitored based on the periodic features of each component of two or more component time series of each of the at least one TSCI. The total periodic feature of the TSCI can be a weighted sum of the periodic features of each component of the two or more component time series of the TSCI. In the total periodic feature, weights for the periodic features of each component of the component time series can be adaptively determined based on the component time series.

[0226] A total periodic feature of all of each of the at least one TSCIs can be calculated based on the respective total periodic features of each of the at least one TSCIs. The periodic feature of each movement can be monitored based on the total periodic feature. The total periodic feature can be a weighted sum of the respective total component time series of each of the at least one TSCIs. In the total periodic feature, weights for the total periodic features of the TSCIs can be adaptively determined based on the TSCIs.

[0227] At least one extremum may be calculated. The at least one local extremum may be at least one local extremum of a periodic feature for each component of the plurality of component time series of a particular one of the at least one TSCIs, a total periodic feature for a particular one of the at least one TSCIs, and / or a total summed periodic feature for all of the at least one TSCIs. The periodic feature of each movement may be monitored based on the at least one local extremum. The local extremum may be a local maximum. The local extremum may be a local minimum. The local extremum may be positive. The time (or time shift or time lag) associated with the local extremum may be positive. Before calculating the at least one extremum of each periodic feature, noise and / or unwanted disturbances of each periodic feature may be suppressed and / or removed.

[0228] The period of each periodic feature of the movement may be monitored based on a selected one of the at least one extremum. The selected extremum may be a first significant positive maximum of each periodic feature after noise and / or undesired disturbances of each periodic feature have been suppressed and / or removed. The period of each periodic feature of the movement may be associated with a time lag associated with the selected extremum.

[0229] Each item in a component feature time series can be at least one of the following: a corresponding item in a component time series, a magnitude of the corresponding item, a phase of the corresponding item, a first function of the corresponding item, a magnitude of the corresponding item, a phase of the corresponding item, and / or another item, a second function of the component time series and / or another function of the component time series, a first function of a second function of the component time series, a second function of a first function of the component time series, and / or a combination of the first and second features. There can be an injective (one-to-one) mapping from component-feature time series (domain) to component time series (range). Each item in a component-feature time series (domain) may be mapped to an item in a component time series (range). Each item in a component time series (range) may be mapped to at most one item in a component-feature time series (domain).

[0230] The mapping can be bijective (one-to-one and upward). Each item in the component-feature time series (domain) may be mapped to an item in the component time series (range). Each item in the component time series (range) may be mapped to exactly one item in the component-feature time series (domain). There may be no mapping. At least one item in the component time series (range) cannot be mapped to any item in the component-feature time series (domain). The component-feature time series may be a subsampled version of the component time series with an associated subsampling factor. The associated subsampling factor may be an integer. The items in the component time series may be periodically sampled with a period associated with the integer subsampling factor. Each sampled item in the component time series (range) can be mapped to exactly one item in the component-feature time series (domain). An item in the unsampled component time series (range) must not be mapped to any item in the component-feature time series (domain). The component-feature time series may correspond to a resampling of the component time series with an associated re-subsampling factor. The resampling can be integer or non-integer. Each item in the component-feature time series (domain) can be mapped to at least one of the items in the component time series (range), interpolated items obtained by applying an interpolation filter to the component time series (range), and / or other items.

[0231] Resampling of the component time series may include at least one of the following: anti-aliasing filtering for resampling, subsampling, local subsampling, subsampling with an adaptive subsampling factor, subsampling with a time-varying subsampling factor, upsampling, local upsampling, upsampling with an adaptive upsampling factor, upsampling with a time-varying upsampling factor, adaptive resampling, adaptive subsampling, adaptive upsampling, combined subsampling and upsampling, alternating subsampling and upsampling, and / or different sampling. Different resampling may be applied to different component time series. Each item of the component-feature time series (domain) may be mapped to at least one of an item of the component time series (range), an interpolated item obtained by applying an interpolation filter to the component time series (range), and / or a feature item obtained by applying a feature filter to the component time series (range).

[0232] An item of a first component-feature time series may be mapped to a first feature item obtained by applying a first feature filter to the first component time series. An item of a second component-feature time series may be mapped to a second feature item obtained by applying a second feature filter to the second component time series. The first feature filter and the second feature filter may be different. The first function comprises at least one of a composite function, a function of functions, a complex function, a complex conjugate, a modulus, a phase, a continuous function, a discontinuous function, a real function, a linear function with an offset, a polynomial, a robust function, a robust function with outlier suppression, a function that increases relatively quickly for arguments of smaller absolute value and increases relatively slowly for arguments of larger absolute value, a nonlinear function, a logarithm, an exponential function, a hyperbolic function, a power function, a periodic function, a trigonometric function, a sine, a cosine, a tangent, a cotangent, a cosine, a cosecant, an inverse function, an inverse trigonometric function, an arcsine function, an arccosine function, an arctangent function, a special function, a Bessel function, an error function, a complementary error function, a gamma function, a beta function, an integral function, a logarithmic integral function, an exponential integral, a trigonometric integral, a Gaussian, a Poisson, an absolute value, a sign function, a limit function, a floor function, a rounding function, a quantization function, an index function, a step function, a sawtooth, an approximation function, a Taylor series, an asymptotic series, an analytic function, and / or other functions.

[0233] A respective autocorrelation function (ACF) of each feature of each component time series may be calculated. Each ACF may be associated with a respective component index. Respective movements may be monitored based on the respective ACF. A respective combined autocorrelation function (ACF) of each of the at least one TSCI may be calculated. Each combined ACF may be a linear combination of the ACFs of the multiple component time series of the TSCI. Respective movements may be monitored based on the respective combined ACF. Each of the multiple component time series of the TSCI may be weighted in the linear combination by a weight that is a function of the component time series. Each of the at least one TSCI obtained from the series of probe signals by each heterogeneous target wireless receiver may be preprocessed.

[0234] Another embodiment is a method / apparatus / device / system / software for a wireless monitoring system that includes transmitting a series of first probe signals by an antenna of a first wireless device to at least one heterogeneous first target wireless receiver through a wireless multipath channel within a location. The transmitting can be performed using a processor, memory, and a set of instructions. The first wireless device can include a heterogeneous integrated circuit (IC). Each of the at least one heterogeneous target wireless receiver can include a heterogeneous IC.

[0235] The wireless monitoring system method / apparatus / device / system / software further includes asynchronously acquiring, by each of at least one heterogeneous first target wireless receiver, at least one time-series first CI (first TSCI) from the series of first probe signals. The first CI (CI) is a CI of a first wireless multipath channel between the heterogeneous first target wireless receiver and the first wireless device. The wireless monitoring system method / apparatus / device / system / software further includes asynchronously monitoring a location and respective first movements associated with at least one of the respective first objects within the location based on each of the at least one first TSCI acquired by each of the heterogeneous first target wireless receivers from the series of first probe signals. The wireless monitoring system method / apparatus / device / system / software further includes asynchronously acquiring, by each of at least one heterogeneous second target wireless receiver, at least one time-series second CI (second TSCI) from the series of second probe signals. The second CI (CI) is a CI of a second wireless multipath channel between the disparate second target wireless receiver and the second wireless device. The method / apparatus / device / system / software of the wireless monitoring system further includes asynchronously monitoring respective second movements associated with at least one of the location and respective second objects within the location based on respective at least one second TSCI obtained by each disparate second target wireless receiver from the series of second probe signals.

[0236] A specific first motion associated with a specific heterogeneous first target wireless receiver and a specific second motion associated with a specific heterogeneous second target wireless receiver may be the same motion. The same motion may be jointly monitored based on at least one first TSCI obtained from a series of first probe signals by the specific heterogeneous first target wireless receiver and at least one second TSCI obtained from a series of second probe signals by the specific heterogeneous second target wireless receiver. A specific first object associated with the specific heterogeneous first target wireless receiver and a specific second object associated with the specific heterogeneous second target wireless receiver may be the same object.

[0237] The particular classifier may be configured to calculate, for a first section of a first time duration of the current TSCI and for each of at least one known event, a pairwise mismatch between the first section of the first time duration of the current TSCI and a second section of a second time duration of a trained representative TSCI associated with the known event. The particular classifier may associate the first section of the first time duration of the current TSCI with the known event whose second section has the smallest pairwise mismatch with the first section. If a previous first section of another first time duration of the current TSCI has the smallest pairwise mismatch with another second section of another second time duration of a trained TSCI associated with the known event, the particular classifier may associate the first section of the first time duration of the current TSCI with the known event whose second section has the smallest pairwise mismatch with the first section.

[0238] A trained representative TSCI associated with a known event can be obtained based on at least one trained TSCI associated with the known event. The trained representative TSCI associated with the known event can be one of the at least one trained TSCI associated with the known event. The trained representative TSCI associated with the known event can be a specific one of the at least one training TSCI associated with the known event, thereby having a minimum set mismatch compared to the at least one training TSCI.

[0239] The ensemble mismatch may be a function of at least one pairwise mismatch between a particular one and each of the remaining at least one training TSCI. The function may include at least one of the average, weighted average, mean, trimmed mean, median, mode, arithmetic mean, geometric mean, harmonic mean, truncated mean, generalized mean, power mean, f-mean, interquartile mean, and / or another mean. A particular trained representative TSCI associated with a particular known event may have a particular duration. A particular trained representative TSCI of a particular duration may be aligned with each of the at least one training TSCI associated with the known event. A respective pairwise mismatch may be calculated. The particular trained representative TSCI may minimize the ensemble mismatch compared to the at least one training TSCI. The ensemble mismatch may be a function of at least one pairwise mismatch between a particular one and each of the at least one training TSCI. The particular duration may minimize the ensemble mismatch between two or more candidate durations of the particular trained representative TSCI. The functions include at least one of the mean, weighted mean, average, trimmed mean, median, mode, arithmetic mean, geometric mean, harmonic mean, truncated mean, generalized mean, power mean, f-mean, interquartile mean, and / or another mean.

[0240] At least one of the set mismatch and / or each pairwise mismatch may be normalized to compare each set mismatch of at least one training TSCI with the one with the smallest set mismatch. A particular duration may minimize the set mismatch between two or more candidate time durations of a particular trained representative TSCI. For each of the at least one candidate duration, an optimal trained representative TSCI having the candidate duration may be calculated. A particular duration may be selected as the candidate duration that provides the smallest normalized set mismatch. The normalized set mismatch associated with a candidate duration may be the respective set mismatch normalized by the candidate duration. The particular duration may be obtained by performing a search among at least one candidate duration where the cost function is at least one of the following: normalized set mismatch, hybrid normalized set mismatch, combined normalized set match, simplified mismatch, and / or another cost function. The normalized set mismatch associated with a candidate duration may be the respective set mismatch normalized by the candidate duration. The search may include at least one of the following: a brute force search, a gradient descent, a steepest descent, a stochastic search, a genetic search, a predictive search, a local search, a multi-resolution search, a hierarchical search, a constrained search, an unrestricted search, a fast search, a simple search, and / or another search.

[0241] A specific duration and a specific trained representative TSCI having a specific duration may be iteratively calculated. The current duration may be initialized as one of the candidate durations. For the current duration, a current optimal trained representative TSCI at the current duration may be calculated. For the current optimal trained representative TSCI at the current duration, the current duration may be changed to provide a smaller normalized aggregate mismatch. The current duration may be initialized with a value based on a duration associated with at least one training TSCI associated with a known event. The current duration may be initialized with a value based on a duration associated with at least one training TSCI associated with a known event. At least one of the first wireless device and / or the first target wireless receiver may include at least two antennas. Each pair of the first wireless device antenna and the first target wireless receiver antenna may form a link between the first wireless device and the first target wireless receiver. At least two links may be formed between the first wireless device and the first target wireless receiver. Each link may correspond to a current TSCI. As an example, tracking of an object may be achieved by obtaining an initial location of the object at time T and then calculating an updated location of the object at time T+ΔT based on at least one TSCI.

[0242] The initial location of the object at time T can be obtained by placing several directional antennas at the entrance so that, as the object moves beneath them, it receives radio signals indicating that the object is at a location associated with the directional antenna. The updated location of the object can be calculated by calculating the distance traveled by the object in an incremental time ΔT and combining the distance with the object motion direction (e.g., obtained from a gyroscope associated with the object) to obtain a displacement vector. The displacement vector can be added to the location at time T to obtain the object's updated location at time T + ΔT.

[0243] A Type-1 heterogeneous wireless device is a first type of heterogeneous wireless device (e.g., a wireless transmitter that transmits a first probing signal (such as an impulse), a wireless receiver that receives a second probing signal, a wireless device that functions as a bot, or a wireless device that functions as an origin), and a Type-2 heterogeneous wireless device is a second type of heterogeneous wireless device (e.g., a wireless receiver that receives the first probing signal, a wireless transmitter that transmits a second probing signal, or a wireless transceiver, or a wireless device that functions as an origin, or a wireless device that functions as a bot). A Type-1 device may operate in cooperation with a Type-2 device so that one transmits a probing signal and the other receives it. The Type 1 device and / or Type 2 device may be a smart speaker, a smartphone, a phone accessory, a portable dongle for plugging into another device, a USB dongle, a computer, a WiFi router, an LTE base station, a WiFi repeater, an LTE repeater, a TV, a DVD player, a HiFi, audio equipment, lighting equipment, a clock, a lamp, a refrigerator, a microwave, an appliance, a kitchen utensil, a torch, a security camera, a dedicated security box, an ornament, etc. According to various embodiments of the present teachings, in a system for object tracking and navigation, an origin can be a transceiver and a bot can be another transceiver. Each of the origin and the bot can be a Type 1 or a Type 2 heterogeneous wireless device.

[0244] 18 illustrates an exemplary method for object tracking according to one embodiment of the present invention. At operation 1802, at least one TSCI f of a wireless multipath channel is obtained using a processor, a memory, and an instruction set stored in the memory. The at least one TSCI is extracted from a wireless signal transmitted between a type 1 heterogeneous wireless device at a first position within the location and a type 2 heterogeneous wireless device at a second position within the location over the wireless multipath channel. The wireless multipath channel is affected by a current motion of an object within the location. At operation 1804, spatiotemporal information of the object is determined based on at least one of the at least one TSCI, a time parameter associated with the current motion, and past spatiotemporal information of the object. At operation 1906, a task is performed based on the spatiotemporal information.

[0245] Figure 19 illustrates an example of spatiotemporal information 1902 according to one embodiment of the present invention. Figure 20 illustrates an example of a task performed based on the spatiotemporal information according to one embodiment of the present invention.

[0246] 21 illustrates another exemplary method for object tracking according to one embodiment of the present invention. As shown in FIG. 21, TSCI 111A / 111B can be obtained and the distance can be determined in operation 2102, thereby obtaining distance information 2104 of the current object's motion. In parallel, several mechanisms can be used to obtain an estimated direction 2106 of the current object's motion. In that way, spatiotemporal information can be determined based on the distance information 2104 and the estimated direction 2106, similar to operation 1804 shown in FIG. 18, to obtain spatiotemporal information 1902 as shown in FIG. 19.

[0247] Generally, tasks may be performed based on spatiotemporal information, as shown by operation 1806 in FIG. 18 . In one embodiment, the task may include depiction of spatiotemporal information. This may be displayed audiovisually, graphically, textually, symbolically, or mechanically. For example, the spatiotemporal information may be detection of the movement of objects in different rooms of the house. A graphical user interface (GUI) may be configured to indicate the location of objects in the house. For example, the object may be a person. The location or approximate location of the object may be indicated / marked. The GUI may then divide the house into a living room area, a family room area, a dining room area, a bedroom 1 area, a bedroom 2 area, etc. Each area may be assigned a color and / or shaded with a color. Each area may be animated (e.g., area size, area shape, area color, area color intensity, textual representation, symbolic representation, etc.). Alternatively, the GUI may have a different representation of each region with or without a map. The representation may be animated. The animation may be real-time or at a later time. Predicted object (user) behavior / activity may also be animated. The presentation can also be in the form of vibration, mechanical feedback, physical feedback, haptic feedback, lighting, shading, shape, etc. to reflect spatiotemporal information. The spatiotemporal information can include two or more analyses, such as number of people, presence of motion, intensity of motion, duration of motion, frequency of motion, "unusual" or "unexpected" motion, vital signs, life / death, immobility, sleep, suspicious event, and / or fall, etc. For example, if there is a lot of motion, the colors can be darker (more black / gray elements) or more vivid or brighter. If there is less motion, the colors can be brighter or less vivid or dimmer. When a person enters the home, the GUI can indicate that he is in the front lobby area, living room, bedroom 1, etc.The GUI can be software for a computer / tablet, an app on a smartphone (e.g., iPhone, Android phone, etc.), or an app in a smart device (e.g., smart glasses, smart watch, etc.).

[0248] 22 illustrates an exemplary presentation of spatiotemporal information at a location, according to one embodiment of the present invention. For example, as illustrated in FIG. 22, in a two-bedroom apartment 2200 (or 900), origin 2201 (or 901) can be located in living room area 2202 (or 902), bot 1 2210 (or 910) can be located in bedroom 1 area 2212 (or 912), and bot 2 2220 (or 920) can be located in dining room area 2222 (or 922). If both bot 1 2210 (or 910) and bot 2 2220 (or 920) detect movement / activity, the activity / movement / person / user may be in living room area 2202 (or 902). If only bot 1 2210 (or 910) detects movement / activity, the activity / movement / person / user may be in bedroom-1 area 2212 (or 912). If only Bot 2 2220 (or 920) detects movement / activity, the activity / movement / person / user may be in the dining room area 2222 (or 922). If neither of the two bots detects movement / activity, there may be no one in the apartment 2200 (or 900). The corresponding area where activity / movement / person / user was detected may be marked with a predetermined pattern or color. Many example implementations of the present disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the present disclosure.For example, the wireless transmitter, wireless receiver, antenna, directional antenna, wireless transmission, multipath channel, wireless signal, object, object, processor, memory, instruction set, CI (CI), channel state information (CSI), power information (PI), format / features / components / context of CI / CSI / PI, compression / representation / storage / transmission / encryption / processing / pre-processing / post-processing of CI / CSI / PI, time series format / order / sequence, extraction of CI from wireless signal, spatio-temporal information, past information, initial information, temporal quantity, spatial quantity, spatio-temporal quantity, estimated direction, similarity score, object, distance, object motion, feature points, local device, and / or computational workload share may be different from those described above. The spatio-temporal information may be speed. An exemplary description of a speed estimation method is as follows: Three receive antennas may be aligned on a line parallel to the direction of movement, as shown in FIG. 23. FIG. 23 is an exemplary diagram of speed estimation based on antenna matching, according to one embodiment of the present teachings. Furthermore, the speed of the receiving antennas can vary slowly in time, i.e., within a short time, the speed can approximate a constant value v. The receiving antennas Rx2 and Rx3 can be grouped and called the primary antennas, and the receiving antennas Rx1 and Rx2 can be grouped and called the trailing or trailing antennas. This is because the trailing antennas (or trailing antennas) can follow the same trajectory as the primary antenna, but with an unknown delay τ. The speed is related to 【nu...

Claims

1. 1. A system for monitoring movement of objects at a location, comprising: a receiver located at a second location at the location, receiving a second wireless signal through a wireless multipath channel affected by quasi-periodic motion of objects at the location; a transmitter located at a first location of the location transmitting a first wireless signal through the wireless multipath channel; receiving a second wireless signal, the second wireless signal being determined based on both the wireless multipath channel and the first wireless signal as affected by the quasi-periodic motion of the object at the location; extracting at least one time series of channel information (CI) of the wireless multipath channel from the second wireless signal, extracting channel state information (CSI), each CI including at least one of a channel state information (CSI), a frequency domain CSI, a frequency domain CSI associated with at least one subband, a time domain CSI, a channel impulse response (CIR), a channel frequency response (CFR), and a channel filter response, of the wireless multipath channel; 1. A vital signs estimator, comprising: determining that at least a portion of the at least one time series of CI (TSCI) within a current sliding time window is associated with the quasi-periodic motion of the object at the location; and calculating current features related to the quasi-periodic movement of the object in the current sliding time window based on at least one of the at least a portion of the at least one TSCI in the current sliding time window, at least a portion of the at least one TSCI in a past sliding time window, and past features related to the quasi-periodic movement of the object in the past sliding time window, and a vital signs estimator, wherein at least one of the current features and the past features comprises information related to at least one of a frequency of quasi-periodic motion, a frequency feature, a frequency spectrum, a period of quasi-periodic motion, a temporal feature, a temporal profile, a timing of quasi-periodic motion, a start time, an end time, a duration, a motion history, a motion type, a motion classification, a location of the object, a velocity, a displacement, an acceleration, a rotational velocity, a rotational feature, a gait cycle of the object, a transient behavior of the object, a transient motion, a change in quasi-periodic motion, a change in frequency of quasi-periodic motion, a change in gait cycle, an event associated with quasi-periodic motion, an event associated with transient motion, a sudden motion event, and a fall event, The vital signs estimator includes a channel information processor, and for each CI of a particular portion of the at least one TSCI within the current sliding time window, the channel information processor is configured to: Obtain N frequency domain components of the CI; determining a timestamp associated with the CI; configured to pre-process the N1 frequency domain components, the pre-processing comprising: cleaning the N1 frequency domain components; normalizing the N1 frequency domain components such that the N1 frequency domain components have a total power of unity; The system, wherein the current feature related to the quasi-periodic motion of the object is calculated based on the pre-processed N1 frequency domain components of the CI of the particular portion of the at least one TSCI within the current sliding time window.

2. The vital signs estimator includes a spectrum analyzer, the spectrum analyzer comprising: For each CI of the particular portion of the at least one TSCI within the current sliding time window, Transforming N1 frequency domain components of the channel information into N2 time domain coefficients of the channel information using an inverse frequency transform, where each channel information is associated with a time stamp, N2 is not less than N1, and each of the N2 coefficients is associated with a time delay, the inverse frequency transform comprising at least one of an inverse Fourier transform, an inverse Laplace transform, an inverse Hadamard transform, an inverse Hilbert transform, an inverse sine transform, an inverse cosine transform, an inverse trigonometric transform, an inverse wavelet transform, an inverse integer transform, an inverse power of two transform, a combination of zero padding and transform, and an inverse Fourier transform with zero padding; retaining a first C of the N2 time-domain coefficients, where C is not greater than N2; correcting timestamps of all channel information of the particular portion of the at least one TSCI within the current sliding time window such that corrected timestamps of time-corrected channel information are uniformly spaced in time, where correcting the timestamps includes: Identifying a particular CI having a particular timestamp to be replaced by a time-corrected CI having a corrected timestamp; calculating the time-corrected CI by calculating the C retained time-domain coefficients of the time-corrected CI at the corrected timestamp using an interpolation filter associated with the corrected timestamp; the current feature related to the quasi-periodic motion of the object is calculated based on the C retained time-domain coefficients of the CI of the particular portion of the at least one TSCI within the current sliding time window; and The system of claim 1 .

3. The spectrum analyzer further comprises: For each of the C retained time domain coefficients, identifying a corresponding retained time-domain coefficient of each CI of the particular portion of the at least one TSCI within the current sliding time window; applying bandpass filtering to all of the corresponding identified and retained time domain coefficients; applying a frequency transform to all the corresponding identified and retained time domain coefficients, the length of the frequency transform being equal to or greater than a length of the particular portion; the current features related to the quasi-periodic motion of the object are calculated based on an output of the frequency transform applied to the corresponding retained time domain coefficients of each of the CIs of the particular portion of the at least one TSCI within the current sliding time window. The system of claim 2.

4. The vital signs estimator further includes an energy spectrum normalizer, the spectrum analyzer and the energy spectrum normalizer being configured to: configured to process the output of the frequency transform applied to corresponding retained time-domain coefficients of each of the CIs of the particular portion of the at least one TSCI within the current sliding time window; The frequency transformation includes at least one of a Fourier transform, a Laplace transform, a Hadamard transform, a Hilbert transform, a sine transform, a cosine transform, a trigonometric transform, a wavelet transform, an integer transform, a power of two transform, a combination of zero padding and transforms, and a Fourier transform with zero padding; The processing includes at least one of pre-processing, processing, post-processing, filtering, linear filtering, non-linear filtering, folding, grouping, energy calculation, low pass filtering, band pass filtering, high pass filtering, matched filtering, enhancement, restoration, denoising, spectral analysis, inverse linear transform, non-linear transform, feature extraction, machine learning, recognition, labelling, training, clustering, grouping, sorting, thresholding, comparing different portions of time series channel information in different sliding time windows with time corrected channel information, calculating similarity scores, vector quantization, compression, encryption, coding, storing, transmitting, representing, merging, combining, splitting, restricting to selected frequency bands, spectral folding, averaging, averaging over selected frequencies, averaging over selected time domain coefficients, and averaging over antenna links; the average over the antenna links is a weighted average over the at least one TSCI; the average over the selected time domain coefficients is a weighted average over the C retained time domain coefficients. The system of claim 3.

5. The vital signs estimator further includes a peak detector, the peak detector comprising: identifying at least one maximum and at least one minimum in the frequency domain components; calculating at least one local signal-to-noise ratio (SNR) parameter for each pair of adjacent local maxima and minima; configured to identify each significant local peak as at least one of a local maximum having an SNR-like parameter greater than a first threshold T1 and a local maximum having an amplitude greater than a second threshold T2; A system according to any one of claims 1 to 4.

6. The vital signs estimator further includes a periodic motion velocity estimator, the periodic motion velocity estimator comprising: selecting a set of selected significant local peaks from the identified set of significant local peaks based on a selection criterion; selecting a set of significant local peaks, the current features related to the quasi-periodic motion of the object being calculated based on the selected set of significant local peaks and frequency values ​​associated with the selected set of significant local peaks; calculating information associated with the remaining spectrum from which the selected set of significant local peaks has been removed; and detecting an event related to the current feature related to the quasi-periodic motion of the object based on at least one of a residual spectral energy of the residual spectrum, an adaptive threshold, and a finite state machine, the event including at least one of presence, absence, appearance, disappearance, stationary behavior, and at least one of non-stationary behaviors among aperiodic motion, transient motion, strong motion, weak motion, strong background interference and other periodic motion. The system of claim 5.

7. The vital signs estimator further includes a system state controller, the system state controller comprising: Entering an initial (INIT) state of the finite state machine (FSM) of said system; In the INIT state of the FSM, Calculating several thresholds adaptively in a first method; detecting the events based on at least one of the number of thresholds and the selected set of significant local peaks and associated features; detecting excessive background interference motion based on the number of thresholds and the remaining spectral energy; if the event is concluded to be "not detected" and the excessive background interference motion is concluded to be "not detected", then remain in the INIT state; if it is concluded that the event has been provisionally "detected" and that the detected event needs to be verified, transitioning from the INIT state to a verify state of the FSM; transitioning from said INIT state to a FSM RUN state if it is concluded that said excessive background interference motion is "detected"; In the operational state of the FSM, adaptively calculating the number of thresholds in a fourth manner; and detecting the excessive background interference motion based on the number of thresholds and the remaining spectral energy; remaining in said operating state if it is concluded that said excessive background interference motion is "detected"; transitioning from the operational state to the INIT state if it is concluded that the excessive background interference motion is "not detected" for a number of time instances. The system of claim 6.

8. The system state controller further comprises: In the verify state of the FSM, adaptively calculating the number of thresholds in a second manner; and accumulating and calculating at least one statistic based on a set of selected significant local peaks and associated features within at least one adjacent sliding time window for validation of the tentatively detected detected event; remaining in the validation state while accumulating at least one statistic until sufficient statistics have been collected for the validation; validating the tentatively detected event based on the number of thresholds and the at least one statistic; if the tentatively detected event is concluded to be "verified", transitioning from the verify state to a peak found state of the FSM; if verification concludes "not verified", transitioning from said verification state to said INIT state; if the verification is inconclusive, remaining in the verification state; In the peak found state of the FSM, adaptively calculating the number of thresholds in a third method; and detecting the verified events based on at least one of the number of thresholds and features associated with the selected set of significant local peaks; detecting the excessive background interference motion based on the number of thresholds and the remaining spectral energy; if the verified event is concluded to be "detected", then remaining in the Peafound state; transitioning from the PEAK FIND state to the INIT state if it is concluded that the verified event was "not detected" for some time instance; transitioning from the peak found state to the operating state if it is concluded that the excessive background interference motion has been "detected" for some time instance. The system of claim 7.

9. The system further comprises: an additional transmitter located at a third location in the location and configured to transmit a third wireless signal through the wireless multipath channel affected by the quasi-periodic motion of the object in the location; an additional receiver located at a fourth location at the location, receiving a fourth wireless signal through the wireless multipath channel, the fourth wireless signal being determined based on both the wireless multipath channel and the third wireless signal; and obtaining an additional TSCI of the wireless multipath channel based on the fourth wireless signal; The vital signs estimator further comprises: determining that at least a portion of the additional TSCIs within the current sliding time window are associated with the quasi-periodic motion of the object at the location; and calculating the current features related to the quasi-periodic motion of the object within the current sliding time window based on at least one of the at least a portion of the additional TSCI within the current sliding time window, the at least a portion of the additional TSCI within an additional past sliding time window, and past features related to the quasi-periodic motion of the object within the additional past sliding time window. A system according to any preceding claim.

10. The vital signs estimator further comprises: determining that at least a portion of the at least one TSCI within the current sliding time window is associated with quasi-periodic motion of a plurality of objects at the location, the wireless multipath channel being further affected within the current sliding time window by the quasi-periodic motion of the plurality of objects; calculating current features related to the quasi-periodic motion of the plurality of objects within the current sliding time window based on at least one of the at least a portion of the at least one TSCI within the current sliding time window, the at least a portion of the at least one TSCI within the past sliding time window, and past features related to the quasi-periodic motion of the plurality of objects within the past sliding time window; and estimating a number of the plurality of objects based on the current features. A system according to any one of claims 1 to 9.

11. A method for monitoring movement of objects in a location, the method being performed by a receiver and a vital signs estimator, the method comprising: obtaining, by the receiver, at least one time series of channel information (CI) of a wireless multipath channel extracted from a wireless signal, the wireless signal is received as an output of the wireless multipath channel; the wireless multipath channel is affected by quasi-periodic motion of objects at the location; each CI includes at least one of a channel state information (CSI), a frequency domain CSI, a frequency domain CSI associated with at least one subband, a time domain CSI, a channel impulse response (CIR), a channel frequency response (CFR), and a channel filter response of the wireless multipath channel; determining, by the vital signs estimator, that at least a portion of the at least one time series of CI (TSCI) within a current sliding time window is associated with the quasi-periodic movement of the object at the location; calculating current features related to the quasi-periodic motion of the object within the current sliding time window based on at least one of the at least a portion of the at least one TSCI within the current sliding time window, the at least a portion of the at least one TSCI within a past sliding time window, and past features related to the quasi-periodic motion of the object within the past sliding time window, and calculating current features, wherein at least one of the current features and the past features comprises information related to at least one of a quasi-periodic motion frequency, a frequency feature, a frequency spectrum, a quasi-periodic motion duration, a time feature, a temporal profile, a quasi-periodic motion timing, a start time, an end time, a duration, a motion history, a motion type, a motion classification, a location of the object, a velocity, a displacement, an acceleration, a rotational velocity, a rotational feature, a gait cycle of the object, a transient behavior of the object, a transient motion, a quasi-periodic motion change, a quasi-periodic motion frequency change, a gait cycle change, a quasi-periodic motion associated event, a transient motion associated event, a sudden motion event, and a fall event; The vital signs estimator includes a channel information processor, and for each CI of the particular portion of the at least one TSCI within the current sliding time window, the method further comprises: obtaining N1 frequency domain components of the CI by the channel information processor; determining a timestamp associated with the CI; configured to pre-process the N1 frequency domain components, the pre-processing comprising: cleaning the N1 frequency domain components; 1. A method for monitoring movement of objects at a location, comprising: normalizing the N1 frequency domain components such that the N1 frequency domain components have a total power of unity, The method of claim 1, wherein the current feature related to the quasi-periodic motion of the object is calculated based on the pre-processed N1 frequency domain components of the CI of the particular portion of the at least one TSCI within the current sliding time window.

12. A method for monitoring movement of objects at the location, the vital signs estimator including a spectrum analyzer, the method further comprising: For each CI of a particular portion of the at least one TSCI within the current sliding time window, obtaining N1 frequency domain components of the CI by the spectrum analyzer; determining a timestamp associated with the CI; Pre-processing the N1 frequency domain components, The pre-processing includes a step of cleaning the N1 frequency domain components; pre-processing, including normalizing the N1 frequency domain components such that the N1 frequency domain components have a total power of unity; Transforming N1 frequency domain components of the channel information into N2 time domain coefficients of the channel information using an inverse frequency transform, where each channel information is associated with a time stamp, N2 is not less than N1, and the N2 coefficients are associated with a time delay, the inverse frequency transform comprising at least one of an inverse Fourier transform, an inverse Laplace transform, an inverse Hadamard transform, an inverse Hilbert transform, an inverse sine transform, an inverse cosine transform, an inverse trigonometric transform, an inverse wavelet transform, an inverse integer transform, an inverse power of two transform, a combination of zero padding and transform, and an inverse Fourier transform with zero padding; retaining a first C of the N2 time-domain coefficients, where C is not greater than N2; and correcting timestamps of all channel information of the particular portion of the at least one TSCI within the current sliding time window such that the corrected timestamps of the time-corrected channel information are uniformly spaced in time; Correcting the time stamp comprises: Identifying a particular CI having a particular timestamp to be replaced by a time-corrected CI having a corrected timestamp; calculating the time-corrected CI by calculating C retained time-domain coefficients of the time-corrected CI at the corrected timestamp using an interpolation filter associated with the corrected timestamp; the current feature related to the quasi-periodic motion of the object is calculated based on the C retained time domain coefficients of the CI of the particular portion of the at least one TSCI within the current sliding time window; The method of claim 11.

13. 13. A method for monitoring movement of objects at said location, further comprising: For each of the C retained time domain coefficients, identifying, by the spectrum analyzer, corresponding retained time-domain coefficients of each CI of the particular portion of the at least one TSCI within the current sliding time window; applying bandpass filtering to all of the identified corresponding retained time domain coefficients; applying a frequency transform to all of the identified corresponding retained time domain coefficients, the length of the frequency transform being equal to or greater than a length of the particular portion; the current features related to the quasi-periodic motion of the object are calculated based on an output of the frequency transform applied to the corresponding retained time domain coefficients of each of the CIs of the particular portion of the at least one TSCI within the current sliding time window. The method of claim 12.

14. A method for monitoring movement of objects at the location, the vital signs estimator further comprising an energy spectrum normalizer, the method further comprising: processing the output of the frequency transform applied to the corresponding retained time domain coefficients of each of the CIs of the particular portion of the at least one TSCI within the current sliding time window by the energy spectrum normalizer; The frequency transformation includes at least one of a Fourier transform, a Laplace transform, a Hadamard transform, a Hilbert transform, a sine transform, a cosine transform, a trigonometric transform, a wavelet transform, an integer transform, a power of two transform, a combination of zero padding and transforms, and a Fourier transform with zero padding; The processing includes at least one of pre-processing, processing, post-processing, filtering, linear filtering, non-linear filtering, folding, grouping, energy calculation, low pass filtering, band pass filtering, high pass filtering, matched filtering, enhancement, restoration, denoising, spectral analysis, inverse linear transform, non-linear transform, feature extraction, machine learning, recognition, labelling, training, clustering, grouping, sorting, thresholding, comparing different portions of time series channel information in different sliding time windows with time corrected channel information, calculating similarity scores, vector quantization, compression, encryption, coding, storing, transmitting, representing, merging, combining, splitting, restricting to selected frequency bands, spectral folding, averaging, averaging over selected frequencies, averaging over selected time domain coefficients, and averaging over antenna links; the average over the antenna links is a weighted average over the at least one TSCI; the average over the selected time domain coefficients is a weighted average over the C retained time domain coefficients. The method of claim 13.

15. A method of monitoring movement of objects at the location, the vital signs estimator further comprising a peak detector, the method further comprising: identifying at least one maximum and at least one minimum in a frequency domain with the peak detector; Calculating at least one local signal-to-noise ratio (SNR) parameter for each pair of adjacent local maxima and minima; and identifying each significant local peak as at least one of a local maximum having an SNR-like parameter greater than a first threshold T1 and an amplitude greater than a second threshold T2.

15. The method according to any one of claims 11 to 14.

16. A method for monitoring movement of objects at the location, the vital signs estimator further comprising a periodic movement rate estimator, the method further comprising: selecting, by the periodic motion velocity estimator, a set of selected significant local peaks from the set of identified significant local peaks based on a selection criterion; selecting local peaks, the current features related to the quasi-periodic motion of the object being calculated based on the selected set of significant local peaks and frequency values ​​associated with the selected set of significant local peaks; calculating information associated with the remaining spectrum from which the selected set of significant local peaks has been removed; Detecting an event related to the current feature related to the quasi-periodic motion of the object based on at least one of a residual energy of the residual spectrum, an adaptive threshold, and a finite state machine, the event including at least one of presence, absence, appearance, disappearance, stationary behavior, and at least one of non-stationary behaviors of aperiodic motion, transient motion, strong motion, weak motion, periodic motion other than strong background interference.

16. The method according to any one of claims 11 to 15.

17. A method for monitoring movement of objects at the location, the vital signs estimator further comprising a system state controller, the method further comprising: the system state controller entering an initial (INIT) state of a finite state machine (FSM) of the system; In the INIT state of the FSM, Calculating several thresholds adaptively in a first method; detecting the events based on at least one of the number of thresholds and the selected set of significant local peaks and associated features; detecting excessive background interference motion based on the number of thresholds and the remaining spectral energy; if the event is concluded to be "not detected" and the excessive background interference motion is concluded to be "not detected", then remain in the INIT state; if it is concluded that the event has been provisionally "detected" and that the detected event needs to be verified, transitioning from the INIT state to a verify state of the FSM; transitioning from the INIT state of the FSM to an OPERATION state of the FSM if it is concluded that the excessive background interference motion is "detected"; In the operational state of the FSM, adaptively calculating the number of thresholds in a fourth manner; and detecting the excessive background interference motion based on the number of thresholds and the remaining spectral energy; remaining in said operating state if it is concluded that said excessive background interference motion is "detected"; transitioning from the operational state to the INIT state if it is concluded that the excessive background interference motion is "not detected" for a number of time instances; In the verify state of the FSM, adaptively calculating the number of thresholds in a second manner; and accumulating and calculating at least one statistic based on a set of selected significant local peaks and associated features within at least one adjacent sliding time window for validation of the tentatively detected detected event; remaining in the validation state while accumulating at least one statistic until sufficient statistics have been collected for the validation; validating the tentatively detected event based on the number of thresholds and the at least one statistic; if the tentatively detected event is concluded to be "verified", transitioning from the verify state to a peak found state of the FSM; if verification concludes "not verified", transitioning from said verification state to said INIT state; and if the verification is inconclusive, remaining in the verification state; In the peak found state of the FSM, adaptively calculating the number of thresholds in a third method; and detecting the verified events based on the number of thresholds and at least one of the selected set of significant local peaks and associated features; detecting the excessive background interference motion based on the number of thresholds and the remaining spectral energy; if the verified event is concluded to be "detected", then remaining in the Peafound state; transitioning from the PEAK FOUND state to the INIT state if it is concluded that the verified event was "not detected" for some time instance; and transitioning from the peak found state to the operating state if it is concluded that the excessive background interference motion is "detected" for some time instance.

17. The method of claim 16.

18. 13. A method for monitoring movement of objects at said location, further comprising: an additional transmitter located at a third location in the location and configured to transmit a third wireless signal through the wireless multipath channel affected by quasi-periodic motion of the object in the location; an additional receiver located at a fourth location at the location, receiving a fourth wireless signal through the wireless multipath channel, the fourth wireless signal being determined based on both the wireless multipath channel and the third wireless signal; and obtaining an additional TSCI of the wireless multipath channel based on the fourth wireless signal; The method further comprises: determining, by the vital signs estimator, that at least a portion of the additional TSCI within the current sliding time window is associated with the quasi-periodic motion of the object at the location; calculating the current features related to the quasi-periodic motion of the object within the current sliding time window based on at least one of the at least a portion of the additional TSCI within the current sliding time window, the at least a portion of the additional TSCI within additional past sliding time windows, and past features related to the quasi-periodic motion of the object within the additional past sliding time windows.

18. The method according to any one of claims 11 to 17.

19. 13. A method for monitoring movement of objects at said location, further comprising: determining, by the vital signs estimator, that at least a portion of the at least one TSCI within the current sliding time window is associated with quasi-periodic motion of a plurality of objects at the location, wherein the wireless multipath channel is further affected within the current sliding time window by the quasi-periodic motion of the plurality of objects; calculating current features related to the quasi-periodic motion of the plurality of objects within the current sliding time window based on at least one of the at least a portion of the at least one TSCI within the current sliding time window, the at least a portion of the at least one TSCI within the past sliding time window, and past features related to the quasi-periodic motion of the plurality of objects within the past sliding time window; and estimating a number of the plurality of objects based on the current features.

19. The method according to any one of claims 11 to 18.

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