Methods, apparatus, and systems for wireless monitoring, sensing, and localization

Abstract

This invention provides a method and system for improving the accuracy and efficiency of wireless monitoring based on motion information calculation and time window classification. [Solution] The method transmits a radio signal from a first wireless device through a wireless multipath channel of a venue. The wireless multipath channel is affected by the motion of objects within the venue. The method also receives a radio signal through the wireless multipath channel by a second wireless device, obtains TSCI (Time Series Channel Information) of the wireless multipath channel based on the received radio signal, performs classification of the sliding time window by analyzing the CI (Channel Information) contained in the TSCI within the sliding time window, calculates MI (Motion Information) for the sliding time window based on the TSCI and the classification of the sliding time window, and monitors the motion of objects based on the MI.

Description

[Technical Field]

[0001] This instruction, as a whole, relates to wireless monitoring, sensing, and localization. More specifically, it relates to improving the accuracy and efficiency of wireless monitoring based on motion information calculation and time window classification, wireless sensing using multiple groups of wireless devices, and wireless monitoring and localization of objects in a venue based on correlation scores. [Background technology]

[0002] With the rapid increase in Internet of Things (IoT) applications, billions of home appliances, telephones, smart devices, security systems, environmental sensors, vehicles and buildings, and other wirelessly connected devices will be able to transmit data, communicate with each other or with people, and be measured and tracked all the while. Among the various approaches to measuring what is happening in the surrounding environment, wireless monitoring using wireless channel information (CI) has attracted a lot of attention in the age of IoT. However, CI (e.g., channel status information (CSI) or channel frequency information (CFI)) can be interfered with by many factors such as hardware imperfections and thermal noise. In addition, there can be many outliers that, without preprocessing, will significantly affect the performance of wireless monitoring.

[0003] In addition, wireless sensing has been attracting increasing attention in recent years due to the ubiquitous deployment of wireless devices. In addition, human activities affect the propagation of wireless signals, and thus, understanding and analyzing how wireless signals respond to human activities can reveal rich information about the activities. As more bandwidth becomes available in the new generation of wireless systems, wireless sensing will enable many more smartphone IoT applications than can be imagined today in the near future. This is because as the bandwidth increases, more multipaths can be seen in rich scattering environments such as indoor areas or urban areas, which can be treated as hundreds of virtual antennas / sensors. Since there can be a large number of IoT devices available for wireless sensing, an efficient and effective method for using multiple devices for wireless sensing is desirable.

[0004] Furthermore, location services and localization technologies are becoming increasingly essential, from map navigation to social networking. The Global Positioning System (GPS) has reshaped human life over decades and is considered a significant technological milestone in modern society. However, while GPS meets user needs for location services in outdoor scenarios, it cannot provide reliable location data in indoor conditions due to signal interruptions. Therefore, indoor location technology has become a topic of interest in academic research. Indoor location systems can be implemented either actively or passively. Active indoor location systems require specialized devices attached to or carried by humans, and the target's location is determined by continuously monitoring signals from these devices. Passive indoor location systems, on the other hand, typically rely on the perception of sensors placed in the environment. Therefore, the target does not need to carry the device. Such designs benefit several applications where continuous user cooperation is impossible or inconvenient, including intruder detection, fall detection, and daily activity monitoring.

[0005] Existing passive indoor locations operate at various resolutions ranging from centimeter / decimeter levels to room or zone levels, corresponding to various applications. The former approach aims to provide fine-grained indoor location information that enables applications such as indoor tracking, while the latter focuses on obtaining coarser location information that can provide behavior analysis and activity logs. As expected, fine-grained localization requires more hardware, complex infrastructure, calibration effort, and user cooperation. Commercially, room-level localization can be achieved using various approaches, including camera-based solutions and pyroelectric infra-red (PIR) sensors. PIR sensors can detect humans entering a room, but there are blind spots, and they cannot continuously detect humans during low-level activities such as reading or napping. Cameras are widely deployed for monitoring rooms for security purposes, but they only function under line-of-sight conditions, incur additional hardware costs, and pose a risk of privacy infringement. Therefore, there is still a great need for a robust, low-cost, and privacy-friendly solution.

[0006] Despite numerous approaches to passive indoor localization, WiFi-based approaches have garnered the greatest research focus. This is due to two factors: superior sensing capabilities and negligible cost. A single WiFi access point has greater coverage and fewer blind spots than other sensors, thanks to its ubiquitous indoor propagation and ability to penetrate walls. Furthermore, the sensitivity of WiFi multipath propagation profiles to changes in the physical environment helps record information linked to human movement. WiFi signals can "see" human movement across multiple scales in indoor environments, from fairly large body movements to chest movements. Additionally, the availability of Channel State Information (CSI) from commercially available WiFi chipsets means these approaches require negligible additional cost and can reuse existing WiFi infrastructure. Most WiFi-based localization approaches using CSI rely on dedicated placement and calibration to infer geometric relationships, which requires significant setup effort. Other efforts using CSI fingerprinting require cumbersome training and fail to generalize well to diverse environments. As a result, neither of these approaches is scalable to real-world scenarios. [Overview of the Initiative]

[0007] This instruction relates to a system, method, and device for improving the accuracy and efficiency of wireless monitoring based on motion information calculation and time window classification, wireless sensing using multiple groups of wireless devices, and wireless monitoring and localization of objects in a venue based on correlation scores.

[0008] In one embodiment, a system for wireless monitoring is described. The system comprises a first wireless device and a processor. A set of heterogeneous wireless devices comprises a first device, a second device, a second wireless device, and a processor. The first wireless device is configured to transmit wireless signals through a wireless multipath channel of a venue. The wireless multipath channel is affected by the motion of objects within the venue. The second wireless device is configured to receive wireless signals through the wireless multipath channel. The received wireless signals are different from the transmitted wireless signals due to the wireless multipath channel and the motion of objects. The processor is configured to use the processor, memory communicatively coupled to the processor, and a set of instructions stored in memory to acquire time-series channel information (TSCI) of a radio multipath channel based on a received radio signal, perform classification of a sliding time window by analyzing the channel information (CI) contained in the TSCI within the sliding time window, calculate motion information (MI) about the sliding time window based on the TSCI and the classification of the sliding time window, and monitor the motion of an object based on the MI.

[0009] In another embodiment, a method performed by a wireless monitoring system is described. This method includes: transmitting a wireless signal from a first wireless device through a wireless multipath channel of a venue, the wireless multipath channel being affected by the motion of objects in the venue; receiving a wireless signal from a second wireless device through the wireless multipath channel, the received wireless signal being different from the transmitted wireless signal due to the wireless multipath channel and the motion of objects; obtaining time-series channel information (TSCI) of the wireless multipath channel based on the received wireless signal using a processor, a memory communicably coupled to the processor, and a set of instructions stored in the memory; performing a sliding time window classification by analyzing the channel information (CI) contained in the TSCI within the sliding time window; calculating motion information (MI) about the sliding time window based on the TSCI and the sliding time window classification; and monitoring the motion of objects based on the MI.

[0010] In yet another embodiment, a system for wireless sensing is described. This system comprises a set of heterogeneous wireless devices in a venue and a processor. The set of heterogeneous wireless devices comprises a first device, a second device, and a specific device. The specific device comprises a first radio and a second radio. The specific device is configured to communicate with the first device through a first radio channel based on a first protocol using the first radio, and to communicate with the second device through a second radio channel based on a second protocol using the second radio. The processor is configured to acquire time-series channel information (TSCI) of a second radio channel based on radio signals communicated between a specific device and a second device through a second radio channel using a second radio of a specific device, wherein each piece of channel information (CI) includes at least one of channel state information (CSI), channel impulse response (CIR), or channel frequency response (CFR); to calculate pairwise sensing analytics based on the TSCI; and to calculate a composite sensing analytics based on the pairwise sensing analytics. The specific device is configured to transmit the composite sensing analytics to a first device through a first radio channel using a first radio of the specific device. A set of heterogeneous radio devices is configured to perform radio sensing tasks based on the composite sensing analytics.

[0011] In yet another embodiment, a method is described in which a set of heterogeneous radio devices in a venue is used for radio sensing. This method involves: coupling a specific device included in the set to a first radio channel via a first radio channel based on a first protocol using a first radio of a specific device; coupling a specific device included in the set to a second device via a second radio channel based on a second protocol using a second radio of a specific device; having the specific device and the second device perform a pairwise subtask based on radio signals communicated between the specific device and the second device via a second radio channel using a second radio of a specific device; and having the specific device extract from the radio signals The method includes obtaining pairwise sensing analysis values ​​calculated based on time-series channel information (TSCI) of a second radio channel, wherein each piece of channel information (CI) includes at least one of channel state information (CSI), channel impulse response (CIR), or channel frequency response (CFR); a specific device calculating a composite sensing analysis value based on the pairwise sensing analysis values; the specific device transmitting the composite sensing analysis value to the first device through a first radio channel using the first radio of the specific device; and performing a radio sensing task based on the composite sensing analysis value.

[0012] In a different embodiment, a system for correlation-based wireless monitoring is described. The system comprises at least two device pairs within a venue, each device pair comprising a first wireless device and a second wireless device. The venue comprises a plurality of objects, each performing an independent motion. For each device pair, the first wireless device of the device pair is configured to transmit an independent wireless signal, and the second wireless device of the device pair is configured to receive an independent wireless signal through an independent wireless multipath channel of the venue, wherein the received wireless signal is different from the transmitted wireless signal due to the independent wireless multipath channel and the motion of the plurality of objects within the venue, and to obtain independent time-series channel information (TSCI) of the independent wireless multipath channels based on the received wireless signal, calculate independent motion information (MI) based on the TSCI, and perform independent sensing tasks based on the independent MI and independent TSCI. The processor is configured to calculate a correlation score based at least in part on a first TSCI, a second TSCI, a first MI, and a second MI, wherein the motion of a first object is detected and monitored in a first sensing task based on a first MI calculated based on a first TSCI associated with a first device pair, and the motion of a second object is detected and monitored in a second sensing task based on a second MI calculated based on a second TSCI associated with a second device pair, and to detect the first object and the second object as the same object if the correlation score is greater than a first threshold, and to detect the first object and the second object as two different objects if the correlation score is less than a second threshold.

[0013] In another embodiment, a method for correlation-based wireless monitoring is described. This method comprises a plurality of first wireless devices and a plurality of second wireless devices in a venue, each forming at least two device pairs, each comprising a first wireless device and a second wireless device, wherein the venue comprises a plurality of objects performing individual motions; for each device pair, the first wireless device of the device pair transmits an individual wireless signal; and the second wireless device of the device pair receives an individual wireless signal through an individual wireless multipath channel of the venue, wherein the received wireless signal is different from the transmitted wireless signal due to the individual wireless multipath channel and the motion of the plurality of objects in the venue; and based on the received wireless signal, individual time-series channel information (TSCI) of the individual wireless multipath channel is obtained; and based on the TSCI, individual motion information (MI) is calculated. This includes: performing individual sensing tasks based on individual MIs and individual TSCIs; detecting and monitoring the motion of a first object in a first sensing task based on a first MI calculated based on a first TSCI associated with a first device pair; detecting and monitoring the motion of a second object in a second sensing task based on a second MI calculated based on a second TSCI associated with a second device pair; calculating a correlation score based at least partially on the first TSCI, the second TSCI, the first MI, and the second MI; detecting the first object and the second object as the same object if the correlation score is greater than a first threshold; and detecting the first object and the second object as two different objects if the correlation score is less than a second threshold.

[0014] In yet another embodiment, an apparatus for correlation-based wireless monitoring is described. The apparatus comprises a memory having a set of instructions stored internally, and a processor communicatively coupled to the memory. The processor is configured to calculate a correlation score based at least in part on first time-series channel information (TSCI), second TSCI, first motion information (MI), and second MI. The motion of a first object is detected and monitored in a first sensing task associated with a first device pair, based on a first MI calculated based on first TSCI obtained from first wireless signals communicated between first device pairs in a venue, the venue comprising a plurality of objects, each performing individual motions. The motion of a second object is detected and monitored in a second sensing task associated with a second device pair, based on a second MI calculated based on second TSCI obtained from second wireless signals communicated between second device pairs in a venue, each device pair comprising a first wireless device and a second wireless device. For each device pair, the first wireless device of the device pair is configured to transmit a separate wireless signal. The second wireless device of the device pair receives a separate wireless signal through a separate wireless multipath channel of the venue, the received wireless signal being different from the transmitted wireless signal due to the separate wireless multipath channel and the motion of multiple objects within the venue, and based on the received wireless signal, obtains a separate TSCI of the separate wireless multipath channel, calculates a separate MI based on the TSCI, and performs a separate sensing task based on the separate MI and the separate TSCI. The processor is further configured to detect the first object and the second object as the same object if the correlation score is greater than a first threshold, and to detect the first object and the second object as two different objects if the correlation score is less than a second threshold.

[0015] Other concepts relate to software for implementing this instruction concerning wireless monitoring, sensing, and localization. Further novel features are partially described in the following description and may become apparent to those skilled in the art by examining the following and accompanying drawings, or by learning through the creation or operation of embodiments. Novel features of this instruction may be realized and achieved by implementing or using various embodiments of the methods, means, and combinations described in the detailed examples discussed below. [Brief explanation of the drawing]

[0016] The methods, systems, and / or devices described herein will be further described in relation to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, and similar reference numerals represent similar structures through some of the drawings.

[0017] [Figure 1] Figure 1 shows an exemplary block diagram of a first wireless device for a system for wireless sensing or monitoring, according to some embodiments of the present disclosure.

[0018] [Figure 2] Figure 2 shows an exemplary block diagram of a second wireless device for a system for wireless sensing or monitoring, according to some embodiments of the present disclosure.

[0019] [Figure 3] Figure 3 shows a flowchart illustrating an exemplary method for precise wireless monitoring according to several embodiments of the present disclosure.

[0020] [Figure 4] , [Figure 5] , [Figure 6] , [Figure 7] , [Figure 8]Figures 4 to 8 are flowcharts detailing the operation for precise wireless monitoring according to several embodiments of the present disclosure.

[0021] [Figure 9] , [Figure 10] , [Figure 11] , [Figure 12] , [Figure 13] , [Figure 14] , [Figure 15] , [Figure 16] , [Figure 17] Figures 9 to 17 are flowcharts detailing the operation of compensated wireless monitoring according to several embodiments of the present disclosure.

[0022] [Figure 18] Figure 18 shows exemplary scenarios for wireless sensing in a venue according to several embodiments of the present disclosure.

[0023] [Figure 19] Figure 19 shows exemplary plan views and arrangements of wireless devices for wireless sensing according to some embodiments of the present disclosure.

[0024] [Figure 20] Figure 20 shows a flowchart illustrating an exemplary method for hybrid wireless plus-assisted fall detection based on wireless sensing, according to several embodiments of the present disclosure.

[0025] [Figure 21] Figure 21 shows an exemplary system for performing wireless sensing in a venue using multiple device groups with room-by-room deployments, according to some embodiments of the present disclosure.

[0026] [Figure 22]Figure 22 shows an exemplary system for performing wireless sensing in a venue using multiple device groups in a whole-house configuration, according to some embodiments of the present disclosure.

[0027] [Figure 23] Figure 23 shows a flowchart illustrating an exemplary method for performing wireless sensing at a venue using multiple groups of devices, according to some embodiments of the present disclosure.

[0028] [Figure 24] Figure 24 illustrates an exemplary floor plan for wireless sensing, displaying sensing motion statistics and analytical values, according to several embodiments of the present disclosure.

[0029] [Figure 25] Figure 25 shows a flowchart illustrating an exemplary method of a wireless sensing presentation system according to several embodiments of the present disclosure.

[0030] [Figure 26] Figure 26 shows a flowchart illustrating an exemplary method for performing a sensing-by-proxy procedure according to several embodiments of the present disclosure.

[0031] [Figure 27A] Figure 27A shows exemplary environments for operation monitoring and localization according to several embodiments of the present disclosure.

[0032] [Figure 27B] Figure 27B shows different activities extracted by a wireless system according to several embodiments of the present disclosure.

[0033] [Figure 28A] Figure 28A shows an exemplary alternative environment for motion monitoring and localization according to some embodiments of the present disclosure.

[0034] [Figure 28B] Figure 28B shows different activities extracted by different wireless systems according to several embodiments of the present disclosure.

[0035] [Figure 29] Figure 29 shows an exemplary diagram of a wireless system for motion monitoring and localization according to several embodiments of the present disclosure.

[0036] [Figure 30] Figure 30 shows exemplary features extracted from a CSI time series by a wireless system according to several embodiments of the present disclosure.

[0037] [Figure 31A] , [Figure 31B] Figures 31A and 31B show exemplary device setups and movement positions in two scenarios for wireless monitoring according to some embodiments of the present disclosure.

[0038] [Figure 32A] , [Figure 32B] , [Figure 32C] , [Figure 32D] Figures 32A to 32D show exemplary motion statistics and correlation thresholds from a receiver in different scenarios for wireless monitoring according to several embodiments of the present disclosure.

[0039] [Figure 33A] , [Figure 33B] Figures 33A to 33B show exemplary positions of the transmitter and receiver in different setup environments according to several embodiments of the present disclosure.

[0040] [Figure 34]Figure 34 shows flowcharts of exemplary methods for wireless monitoring and localization according to several embodiments of the present disclosure.

[0041] [Figure 35] Figure 35 shows flowcharts illustrating detailed operations for wireless monitoring and localization according to several embodiments of the present disclosure. [Modes for carrying out the invention]

[0042] The symbol " / " disclosed herein means "and / or". For example, "A / B" means "A and / or B". In some embodiments, methods / devices / systems / software for wireless monitoring systems are disclosed. Time-series channel information (CI) of a wireless multipath channel is obtained using a processor, memory communicably coupled to the processor, and a set of instructions stored in memory. Time-series CI (TSCI) can be extracted from wireless signals transmitted through the channel from a type 1 heterogeneous wireless device (e.g., a wireless transmitter (TX), a "Bot" device) in a venue to a type 2 heterogeneous wireless device (e.g., a wireless receiver (RX), an "Origin" device). The channel is affected by the representation / motion of an object in the venue. The characteristics / spatial-temporal information (STI) / motion information (MI) of the object / representation / motion can be calculated / monitored based on the TSCI. Tasks can be performed based on the characteristics / STI / MI. Task-related presentations can be generated within the user interface (UI) on the user's device.

[0043] Representation may include arrangement, arrangement of movable parts, location / velocity / acceleration / position / orientation / direction / identifiable place / area / existence / spatial coordinates, static representation / presentation / state / size / length / width / height / angle / scale / curve / surface / area / volume / pose / posture / embodiment / body language, dynamic representation / motion / sequence / movement / action / gesture / stride / extension / contraction / distortion / deformation, bodily representation (e.g., head / face / eyes / mouth / tongue / hair / voice / neck / limbs / arms / hands / feet / muscles / movable parts), surface representation / shape / texture / material / color / electromagnetic (EM) properties / visual patterns / wetness / reflection / transparency / flexibility, material properties (e.g., biological tissue / hair / cloth / metal / wood / leather / plastic / artificial material / solid / liquid / gas / temperature), changes in representation, and / or any combination thereof.

[0044] A wireless multipath channel may include communication channels, analog frequency channels (e.g., having carrier frequencies close to 700 / 800 / 900MHz, or 1.8 / 1.8 / 1.9 / 2.4 / 3 / 5 / 6 / 27 / 60 / 70+GHz), coding channels (e.g., in CDMA), and / or channels in wireless / cellular networks / systems (e.g., WLAN, WiFi, mesh, 4G / LTE / 5G / 6G / 7G / 8G, Bluetooth, Zigbee, UWB, RFID, microwave). It may include multiple channels that are continuous (e.g., adjacent / overlapping bands) or discontinuous (e.g., non-overlapping bands, 2.4GHz / 5GHz). While channels are used to transmit wireless signals and perform sensing measurements, data (e.g., TSCI / features / components / characteristics / STI / MI / analysis values / task output, auxiliary / non-sensing data / network traffic) may be communicated / transmitted over the channels.

[0045] A wireless signal may include a sequence of probe signals. It can be any of the following: EM radiation, radio frequency (RF) / optical / band-limited / baseband signals, licensed / unlicensed ISM band signals, or wireless / mobile / cellular / optical communication / network / mesh / downlink / uplink / unicast / multicast / broadcast signals. It may comply with standards / protocols (e.g., WLAN, WWAN, WPAN, WBAN, international / national / industrial / de facto, IEEE / 802 / 802.11 / 15 / 16, WiFi, 802.11n / ac / ax / be / bf, 3G / 4G / LTE / 5G / 6G / 7G / 8G, 3GPP(registered trademark) / Bluetooth / BLE / Zigbee / NFC / RFID / UWB / WiMax). A probe signal may include any of the following: protocol / standard / beacon / pilot / sounding / excitation / illumination / handshake / synchronization / reference / source / motion probe / detection / sensing / management / control / data / null data / beacon / pilot / request / response / association / reassociation / disassociation / authentication / action / report / pole / announcement / extension / query / acknowledgment frames / packets / signals, and / or null-data-frames (NDP) / RTS / CTS / QoS / CF-Poll / CF-Ack / block acknowledgment / reference / training / synchronization. It may include line-of-sight (LOS) / non-LOS components (or paths / links). Data may be embedded. Probe signals may be replaced (or embedded) by data signals. Each frame / packet / signal may include a preamble / header / payload. It may include training sequences, short (STF) / long (LTF) training fields, L-STF / L-LTF / L-SIG / HE-STF / HE-LTF / HE-SIG-A / HE-SIG-B, and channel estimation fields (CEF). It can be used to wirelessly transfer power from a Type 1 device to a Type 2 device. The sounding rate of the signal can be adjusted to control the amount of power being transferred. The probe signal may be transmitted in bursts.

[0046] TSCI can be extracted / acquired from radio signals (e.g., by ICs / chips) at the layers of a Type 2 device (e.g., OSI reference model layers, PHY / MAC / Data Link / Logical Link Control / Network / Transport / Session / Presentation / Application Layer, TCP / IP / Internet / Link Layer). It can be extracted from received radio / derived signals. It can include radio sensing measurements acquired via communication protocols (e.g., radio / cellular communication standards / networks, 4G / LTE / 5G / 6G / 7G / 8G, WiFi, IEEE802.11 / 11bf / 15 / 16). Each CI can be extracted from a probe / sounding signal and associated with a timestamp. TSCI can be associated with CI / sampling / sounding frequency / period start time / stop time / duration / amount. Motion detection / sensing signals can be recognized / identified based on probe signals. TSCI can be stored / retrieved / accessed / preprocessed / processed / postprocessed / conditioned / analyzed / monitored. TSCI / features / components / characteristics / STI / MI / analysis values / task results can be communicated to edge / cloud servers / Type 1 / Type 2 / hubs / data aggregators / other devices / systems / networks.

[0047] Type 1 / Type 2 devices may include components (hardware / software) such as electronics / chips / integrated circuits (ICs) / RF circuits / antennas / modems / TX / RX / transceivers / RF interfaces (e.g., 2.4 / 5 / 6 / 27 / 60 / 70+GHz radios / fronthaul radios / backhaul radios) / networks / interfaces / processors / memory / modules / circuits / boards / software / firmware / connectors / structures / enclosures / housings / structures. They may be access points (APs) / base stations / mesh / routers / repeaters / hubs / radio stations / clients / terminals / "Origin Satellite" / "Tracker Bot" and / or Internet of Things (IoT) / appliances / wearables / accessories / peripherals / furniture / amenities / gadgets / vehicles / modules / radio enable / unicast / multicast / broadcast / nodes / hubs / targets / sensors / portables / mobile / cellular / communication / motion detection / source / destination / standards-compliant devices. It may include additional attributes such as auxiliary functions / network connectivity / general purpose / brand / model / appearance / formation / shape / color / material / specifications. The above (e.g., components / device type / additional attributes) can be heterogeneous, as they may differ for different Type 1 (or Type 2) devices.

[0048] Type 1 / Type 2 devices may or may not be authenticated / associated / colocated. They may be the same device. Type 1 / Type 2 / portable / nearby / another device, sensing / measurement session / link between them, and / or object / representation / motion / characteristic / STI / MI / task may be associated with identity / identification information / identifiers (IDs), such as UUIDs, associated / unassociated STA IDs (ASID / USID / AID / UID). A Type 2 device may passively observe / monitor / receive radio signals from a Type 1 device without establishing a connection with it (e.g., association / authentication / handshake) or requesting service from the Type 1 device. Type 1 / Type 2 devices may move with the tracked object / another object.

[0049] A Type 1 (TX) device may function as a Type 2 (RX) device temporarily / sporadically / continuously / repeatedly / interchangeably / substitutely / simultaneously, and vice versa. A Type 1 device may also be a Type 2 device. A device may function as a Type 1 / Type 2 device temporarily / sporadically / continuously / repeatedly / simultaneously. There may be multiple radio nodes, each being a Type 1 / Type 2 device. TSCI may be acquired between two nodes as they exchange / communicate radio signals. Object properties / STI / MI may be monitored based on TSCI individually or on multiple TSCI together.

[0050] The motion / representation of an object can be actively monitored using a Type 1 / Type 2 device that moves with the object (e.g., a wearable device / autonomous guided vehicle / AGV), or passively monitored using a Type 1 / Type 2 device that does not move with the object (e.g., both devices are stationary).

[0051] Tasks can be performed using referenced / trained / initial databases / profiles / baselines that are trained / collected / processed / calculated / sent / stored during the training phase. Databases can be retrained / updated / reset.

[0052] A presentation may include UI / GUI / text / messages / forms / web pages / visuals / images / videos / graphics / animations / graphics / symbols / emojis / signs / colors / shades / sounds / music / speech / audio / mechanical / gestures / vibrations / haptic presentations. Time series of characteristics / STI / MI / task results / other quantities may be displayed / presented in the presentation. Any operations may be performed / shared by a processor (or logical unit / chip / IC) / Type 1 / Type 2 / user / neighbor / another device / local / edge / cloud server / hub / data / signal analysis subsystem / sensing initiator / responder / SBP initiator / responder / AP / non-AP. A presentation may include any other presentation related to monthly / weekly / daily / simplified / detailed / section / small / large / form factor / color coding / comparison / summary / web display, animation / voice announcement / repetitive motion / periodic / repetitive characteristics of representation.

[0053] Multiple Type 1 (or Type 2) devices may interact with Type 2 (or Type 1) devices. Multiple Type 1 (or Type 2) devices may be synchronous / asynchronous and / or use the same / different channels / sensing parameters / settings (e.g., sounding frequency / bandwidth / antenna). A Type 2 device may receive another signal from a Type 1 / another Type 1 device. A Type 1 device may transmit another signal to a Type 2 / another Type 2 device. The radio signals transmitted (or received) by them may be sporadic / temporary / continuous / repeated / synchronous / simultaneous. They may operate independently / jointly. Their data (e.g., TSCI / features / characteristics / STI / MI / intermediate task results) may be processed / monitored / analyzed independently or together / jointly.

[0054] Any device may operate based on some state / internal state / system state. A device may communicate directly or via another / nearby / portable device / server / hub device / cloud server. A device / system may be associated with one or more users with associated settings. Settings may be selected / pre-programmed / changed / adjusted / modified / altered over time. Methods may be executed in the indicated order / or another order. Steps may be executed in parallel / iteratively / repeatedly. Users may include humans / adults / elderly / males / females / young people / children / babies / pets / animals / living organisms / machines / computer modules / software. Steps / operations / processes may differ for different devices based on location / orientation / direction / role / user-related characteristics / settings / available resources / bandwidth / power / network connectivity / hardware / software / processor / coprocessor / memory / battery life / antenna / directional antenna / power settings / device parameters / characteristics / conditions / situations / states. Any / all devices can be controlled / regulated by a processor (e.g., Type 1 / Type 2 / Nearby / Portable / Another device / Server / Associated with a specified source). Some devices are physically located within a common device / devices in a common device / devices mounted in a common device.

[0055] A Type 1 (or Type 2) device may be able to wirelessly couple with multiple Type 2 (or Type 1) devices. A Type 1 (or Type 2) device may be made to switch / establish wireless coupling (e.g., association / authentication) from one Type 2 (or Type 1) device to another Type 2 (or another Type 1) device. Switching may be controlled by a server / hub device / processor / Type 1 device / Type 2 device. The wireless channels may be different before and after switching. A second wireless signal may be transmitted between a Type 1 (or Type 2) device and a second Type 2 (or second Type 1) device through a second channel. A second TSCI on the second channel may be extracted / acquired from the second signal. The first / second signals, first / second channels, first / second Type 1 devices, and / or first / second Type 2 devices may be located in the same / similar / same location.

[0056] A Type 1 device can transmit / broadcast a radio signal to multiple Type 2 devices, with or without establishing a connection (association / authentication) with each individual Type 2 device. This signal may be sent to a specific / common MAC address, which could be the MAC address of several devices (e.g., dummy receivers). Each Type 2 device may adapt to a specific MAC address to receive the radio signal. A specific MAC address may be associated with a venue, which can be recorded in the association table of an association server (e.g., a hub device). The venue may be identified by a Type 1 / Type 2 device based on the radio signal received at that specific MAC address.

[0057] For example, a Type 2 device may be moved to a new venue. A Type 1 device may be newly set up at the venue so that Type 1 and Type 2 devices do not recognize each other. During setup, a Type 1 device may be commanded / guided / made / controlled (e.g., by a dummy receiver, hardware pin configuration / connection, saved configuration, local configuration, remote configuration, downloaded configuration, hub device, and / or server) to transmit radio signals (e.g., a sequence of probe signals) to a specific MAC address. Once powered on, a Type 2 device may scan for probe signals according to a table of MAC addresses (e.g., stored on a specified source, server, hub device, cloud server) that can be used to broadcast to different locations (e.g., different MAC addresses used for different venues such as houses / offices / enclosures / floors / multistore buildings / stores / airports / malls / stadiums / halls / stations / subways / lots / areas / zones / regions / districts / cities / countries / continents, etc.). When a Type 2 device detects a radio signal sent to a specific MAC address, it can use the table to identify the venue.

[0058] Channels may be selected from a set of candidate / selectable / acceptable channels. Candidate channels may be associated with different frequency bands / bandwidths / carrier frequencies / modulations / radio standards / coding / encryption / payload characteristics / networks / IDs / SSIDs / characteristics / configurations / parameters. Specific MAC addresses / selected channels may change / adjust / modify / modify over time (e.g., according to time tables / rules / policies / modes / conditions / situations / changes). Selection / modification may be based on availability / collisions / traffic patterns / identical channels / inter-channel interference / effective bandwidth / random selection / pre-selection lists / plans. This may be done by servers (e.g., hub devices). They may be communicated (e.g., between type 1 / type 2 / hubs / other devices / local / edge / cloud servers).

[0059] A wireless connection (e.g., association / authentication) may be established between a Type 1 device and a nearby / portable / other device (e.g., using a signaling handshake). The Type 1 device may send a first handshake signal (e.g., a sounding frame / probe signal / request-to-send (RTS)) to the nearby / portable / other device. The nearby / portable / other device may respond to the first signal by sending a second handshake signal (e.g., a command / clear-to-send (CTS)) to the Type 1 device, triggering the Type 1 device to send / broadcast a wireless signal to multiple Type 2 devices without establishing a connection with a Type 2 device. The second handshake signal may be a response / acknowledgment (e.g., ACK) to the first handshake signal. The second handshake signal may contain information about the venue / Type 1 device. A nearby / portable / another device may be a dummy device intended to establish a wireless connection with a Type 1 device for the purpose of receiving a first signal or transmitting a second signal (e.g., primary purpose, secondary purpose). The nearby / portable / another device may be physically attached to the Type 1 device.

[0060] In another example, a nearby / portable / other device may trigger a Type 1 device to broadcast a signal to multiple Type 2 devices without establishing a connection with those devices by sending a third handshake signal to the Type 1 device. The Type 1 device may respond to the third signal by sending a fourth handshake signal to that other device.

[0061] A nearby / portable / another device may be used to trigger multiple Type 1 devices to broadcast. It may have multiple RF circuits for triggering multiple transmitters in parallel. The triggers may be sequential / partially sequential / partially / fully parallel. Parallel triggering may be achieved using an additional (one or more) device to perform similar triggering in parallel with the nearby / portable / another device. After establishing a connection with a Type 1 device, the nearby / portable / another device may interrupt / stop communication with the Type 1 device. It may enter an inactive / hibernation / sleep / standby / low power / off / power-down mode. The interrupted communication may be resumed. The nearby / portable / another device may have a specific MAC address, and the Type 1 device may transmit signals to that specific MAC address.

[0062] A (first) radio signal may be transmitted to several first Type 2 devices by a first antenna of a Type 1 device through a first channel in a first venue. A second radio signal may be transmitted to several second Type 2 devices by a second antenna of a Type 1 device through a second channel in a second venue. The first / second signals may be transmitted at first / second (sounding) rates, respectively, possibly individually to first / second MAC addresses. Several first / second channels / signals / rates / MAC addresses / antennas / Type 2 devices may be the same / different / synchronous / asynchronous. The first / second venues may have the same / different sizes / shapes / multipath characteristics. The immediate areas around the first / second venues / first / second antennas may overlap. The first / second channel / signal can be WiFi+LTE (one is WiFi and the other is LTE), or WiFi+WiFi, or WiFi(2.4GHz)+WiFi(5GHz), or WiFi(5GHz, channel=a1, BW=a2)+WiFi(5GHz / channel=b1, BW=b2). Some first / second items (e.g., channel / signal / rate / MAC address / antenna / type 1 / type 2 device) may change / adjust / modify / correct over time (e.g., based on timetable / rules / policies / modes / conditions / situations / other changes).

[0063] Each Type 1 device can be a signal source for multiple Type 2 devices (i.e., it sends individual probe signals to individual Type 2 devices). Each individual Type 2 device can asynchronously select a Type 1 device from among all Type 1 devices as its signal source. TSCI can be obtained by each individual Type 2 device from individual sequences of probe signals from Type 1 devices. A Type 2 device can (e.g., initially) select a Type 1 device as its signal source from among all Type 1 devices based on the identity / identification / identifier of the Type 1 / Type 2 device, task, past signal sources, history, characteristics, signal strength / quality, thresholds for switching signal sources, and / or user / account / profile / access information / parameters / input / requirements / criteria information.

[0064] The database of available / candidate Type 1 (or Type 2) devices may be initialized / maintained / updated by Type 2 (or Type 1) devices. A Type 2 device may receive radio signals from multiple candidate Type 1 devices. It may select its Type 1 device (i.e., signal source) based on any of the following: signal quality / strength / regularity / channel / traffic / characteristics / attributes / state / task requirements / training task results / MAC address / identity / identifier / past signal sources / history / user instructions / other considerations.

[0065] Undesirable / bad / problematic / unsatisfactory / unacceptable / unacceptable / defective / severe / undesirable / insufficient / lacking / inferior / unsuitable conditions may occur when (1) the timing between adjacent probe signals in the received radio signal becomes irregular and deviates from the agreed sounding rate (e.g., time perturbations exceeding the acceptable range), and / or (2) the processed / signal intensity of the received signal is too weak (e.g., below the third threshold or below the fourth threshold for a significant percentage of time), where processing includes any low-pass / band-pass / high-pass / median / moving / weighted average / linear / nonlinear / smoothing filtering. Any thresholds / percentages / parameters may change over time. Such conditions may occur when Type 1 / Type 2 devices gradually move further away or when channels become congested.

[0066] Some settings (e.g., Type 1-Type 2 device pairing / signal source / network / association / probe signal / sounding rate / method / channel / bandwidth / system state / TSCI / TSMA / task / task parameters) may be changed / modified / adjusted / modified. Changes may be subject to time tables / rules / policies / modes / conditions (e.g., undesirable conditions) / other changes. For example, the sounding rate is normally 100Hz, but may be changed to 1000Hz in severe situations or to 1Hz in low-power / standby situations.

[0067] Settings can change based on task requirements (e.g., 100Hz normally, 1000Hz instantaneously for 20 seconds). In a task, the instantaneous system can be adaptively / dynamically associated with class / state / condition (e.g., low / normal / high priority / emergency / critical / normal / privileged / non-subscription / subscription / paying / non-paying). Settings (e.g., sounding rate) can be adjusted as needed. Changes can be controlled by server / hub / type 1 / type 2 devices. Scheduled changes can be made according to a time table. Changes can be made immediately when an emergency is detected, or gradually when an ongoing state is detected.

[0068] Characteristics / STI / MI can be monitored / analyzed individually based on TSCIs associated with a specific Type 1 / Type 2 device pair, jointly based on multiple TSCIs associated with multiple Type 1 / Type 2 pairs, jointly based on any TSCIs associated with a specific Type 2 device and any Type 1 device, jointly based on any TSCIs associated with a specific Type 1 device and any Type 2 device, or globally based on any TSCIs associated with any Type 1 / Type 2 device.

[0069] Classifiers / classification / recognition / detection / estimation / projection / feature extraction / processing / filtering can be applied to (e.g., CI / CI features / characteristics / STI / MI) and / or trained / retrained / updated. In the training phase, training may be performed based on multiple training TSCIs from several training radio multipath channels, or characteristics / STI / MIs calculated from training TSCIs. Training TSCIs are obtained from training radio signals transmitted from a training type 1 device and received by a training type 2 device. Retraining / updating may be performed in the operating stage based on the training TSCI / current TSCI. Multiple classes (e.g., grouping / category / event / motion / activity / object / location) can be assigned to a venue / region / zone / place / environment / house / office / building / warehouse / facility object / representation / motion / action / process / event / manufacturing / assembly line / maintenance / repair / navigation / object / emotion / mind / state / stage / gesture / gate / action / motion / presence / motion / daily / activity / history / event.

[0070] Classifiers can include linear / nonlinear / binary / multiclass / Bayesian classifiers / Fisher linear discriminant / logistic regression / Markov chains / Monte Carlo / deep / neural networks / perceptrons / self-organizing maps / boosting / meta-algorithms / decision trees / random forests / genetic programming / kernel learning / KNN / support vector machines (SVM).

[0071] Feature extraction / projection may include subspace projection / principal component analysis (PCA) / independent component analysis (ICA) / vector quantization / singular value decomposition (SVD) / eigendecomposition / eigenvalues / time / frequency / orthogonal / nonorthogonal decomposition, processing / preprocessing / postprocessing. Each CI may contain multiple components (e.g., complex-valued vectors / combinations). Each component may be preprocessed to give magnitude / phase or a function thereof.

[0072] Features (feature quantities) may include the following: output of feature extraction / projection, amplitude / amplitude / phase / energy / power / intensity / strength, presence / absence / proximity / likelihood / histogram, time / period / duration / frequency / component / decomposition / projection / band, local / global / maximum / minimum / zero crossing, repetition / periodic / typical / habitual / temporary / atypical / sudden / mutually exclusive / evolutionary / transitive / change / temporal / related / correlated features / patterns / trends / profiles / events / trends / slope / behavior, causality / short-term / long-term / correlation / statistics / frequency / period / duration, motion / movement / location / map / coordinates / height / velocity / acceleration / angle / rotation / size / volume, suspicious / danger / alert event / warning / certainty / approach / impact Sudden events, tracking / respiration / heart rate / walking / behavior / events / statistics / hourly / daily / weekly / monthly / yearly parameters / statistics / analysis, health / disease / medical statistics / analysis, early / immediate / simultaneous / delayed display / suggestion / sign / indicator / verification / detection / symptoms of states / conditions / situations / diseases / biometrics, babies / patients / machines / devices / temperature / vehicles / parking lots / roads / lifts / elevators / spaces / roads / fluid flow / houses / rooms / offices / homes / buildings / warehouses / storage / systems / ventilation / fans / pipes / ducts / people / humans / cars / boats / trucks / airplanes / drones / commercial areas / crowds / impulsive events / cyclosteady / environment / vibration / materials / surfaces / 3D / 2D / local / global, and / or other measurable quantities / variables. Features may include a monotonic function of the features, or a sliding aggregate of the features within a sliding window.

[0073] Training can include AI / machine / deep / supervised / unsupervised / discriminative training / autoencoder / linear discriminant analysis / regression / clustering / tagging / labeling / Monte Carlo operations.

[0074] The current event / motion / representation / object at the present time can be classified by applying a classifier to the current TSCI / characteristics / STI / MI obtained from the current radio signal received by a Type 2 device at a venue from a Type 1 device during the operation phase. If there are multiple Type 1 / Type 2 devices, some / all (or their locations / antenna locations) can be a reorder of the corresponding training Type 1 / Type 2 devices (or locations / antenna locations). The Type 1 / Type 2 device / signal / channel / venue / object / motion can be the same as / different from the corresponding training entity. The classifier can be applied to a sliding window. The current TSCI / characteristics / STI / MI can be augmented by training the TSCI / characteristics / STI / MI (or fragments / extracts) to bootstrap the classifier.

[0075] The first section / segment (having a first duration / start / end time) of the first TSCI (associated with a first type 1-type 2 device pair) may be aligned with the second section / segment (having a second duration / start / end time) of the second TSCI (associated with a second type 1-type 2 device pair) (using dynamic time warping / DTW / aligned filtering, perhaps based on some mismatch / distance / similarity score / cost, or correlation / autocorrelation / cross-correlation), and each CI in the first section is mapped to a CI in the second section. The first / second TSCIs may be preprocessed. Several similarity scores (per component / item / link / segment) may be calculated. The similarity scores may include mismatch / distance / similarity score / cost. Component-wise similarity scores can be calculated between the components of the first item (CI / feature / characteristic / STI / MI) in the first section and the corresponding components of the corresponding mapped item (second item) in the second section. Item-wise similarity scores may be calculated between the first and second items (for example, based on the set of corresponding component-wise similarity scores). The total score may include summation / weighted sum, weighted mean / robust / trimmed mean / arithmetic / geometric / harmonic mean, median / mode. Link-wise similarity scores can be calculated between the first and second items related to the links (TX-RX antenna pairs) of the first and second type 1-type 2 device pairs (for example, based on the set of corresponding item-wise similarity scores). Segment-wise similarity scores may be calculated between the first and second segments (for example, based on the set of corresponding link-wise similarity scores). The first and second segments may be slid.

[0076] In DTW, constraints can be met by the first / second segments, the first / second items, another first (or second) item in the first (or second) segment, or any of the corresponding timestamp / duration / difference / diff features. The time difference between the first and second items can be constrained (e.g., upper / lower bounds). The first (or second) section can be the entire first (or second) TSCI. The first / second duration / start / end times can be the same or different.

[0077] In one example, the first / second Type 1-Type 2 device pair may be the same, and the first / second TSCIs may be the same / different. If they are different, the first / second TSCIs may include a pair of current / reference, current / current, or reference / reference TSCIs. In the case of "current / reference," the first TSCI may be the current TSCI acquired during the operational phase, and the second TSCI may be the reference TSCI acquired during the training phase. In the case of "reference / reference," the first / second TSCIs may be two TSCIs acquired during the training phase (e.g., for two training events / states / classes). In the case of "current / current," the first / second TSCIs may be two TSCIs acquired during the operational phase (e.g., related to two different antennas or two measurement setups). In another example, the first / second Type 1-Type 2 device pair may be different but share a common device (Type 1 or Type 2).

[0078] Aligned first / second segments (or parts of each) can be represented as first / second vectors. A part may include all items (for "segment-wise"), or all items associated with TX-RX links (for "link-wise"), or an item (for "item-wise"), or a component of an item (for "component-wise"). Similarity scores may include any combination / aggregation / function of: dot product / correlation / autocorrelation / correlation index / covariance / discrimination score / distance / Euclidean / absolute / L_k / weighted distance (between first / second vectors). Similarity scores may be normalized by vector length. Parameters derived from similarity scores may be modeled using statistical distributions. The scale / location / other parameters of the statistical distribution may be estimated.

[0079] It should be noted that multiple sliding segments may exist. The classifier can be applied to the sliding first / second segment pair to obtain a provisional classification result. It can then associate the current event with a specific class based on one segment pair / provisional classification result, or multiple segment pairs / provisional classification results (e.g., whether the similarity score is dominant (e.g., largest / smallest / dominant / unparalleled / most important / superior), or whether it is sufficiently significant among all candidate classes for N consecutive time periods (e.g., higher / lower than some threshold), or for a sufficient percentage of high / low, or for the period with the lowest / lowest frequency).

[0080] Channel information (CI) may include any of the following: signal strength / phase / phase / timestamp, spectral power measurement, modem parameters, dynamic beamforming information, transfer function components, measurable variables, data / measurements, coarse / fine layer information (e.g., PHY / MAC / data link layer), digital gain / RF filter / front-end switch / DC offset / correction / IQ-compensation settings, environmental effects on radio signal propagation, input-to-output conversion, stable behavior of environmental profile, state profile, radio channel measurement / received signal strength indicator (RSSI) / channel state information (CSI) / channel impulse response (CIR) / channel frequency response (CFR) / frequency component characteristics (e.g., subcarrier) / channel characteristics / channel filter response, auxiliary information, data / meta / user / account / access / security / session / status / monitoring / device / network / household / neighbor / environment / real-time / sensor / storage / encrypted / compressed / protected data, identity / identifier / identification information.

[0081] Each CI can be associated with a timestamp / arrival time / frequency bandwidth / signature / phase / amplitude / trend / characteristics, frequency-like characteristics, time / frequency / time-frequency domain elements, and orthogonal / non-orthogonal decomposition characteristics of the signal passing through the channel. The timestamp of a TSCI may be irregular and can be corrected to be regular (e.g., by interpolation / resampling) at least during a sliding time window.

[0082] A TSCI can be / may include a link-wise TSCI associated with the antennas of a Type 1 device and a Type 2 device. For a Type 1 device with M antennas and a Type 2 device with N antennas, an MN link-wise TSCI may exist.

[0083] CI / TSCI may be pre-processed / processed / post-processed / stored / retrieved / transmitted / received. Some modem / radio state parameters may be kept constant. Modem parameters may be applied to a radio subsystem and may represent radio states. Motion detection signals (e.g., baseband signals, and then decoded / demodulated packets) may be obtained by processing (e.g., down-converting) radio signals (e.g., RF / WiFi / LTE / 5G / 6G signals) by the radio subsystem using radio states represented by stored modem parameters. Modem parameters / radio states may be updated (e.g., using previous modem parameters / radio states). Both previous / updated modem parameters / radio states may be applied in the radio subsystem (e.g., to process signals / decode data). In the disclosed system, both may be obtained / compared / analyzed / processed / monitored.

[0084] Each CI may contain N1 CI components (CIC:CI component) each with a corresponding CIC index (e.g., time / frequency domain component, decomposition component). Each CIC may contain real / imaginary / complex quantities, magnitude / phase / boolean / flags, and / or several combinations / subsets. Each CI may contain a vector / matrix / set / collection of CICs. The CICs of a TSCI associated with a particular CIC index may form a CIC time series. A TSCI may be divided into N1 time series of CICs (TSCICs) associated with each CIC index. Characteristics / STI / MI may be monitored based on the TSCICs. Some TSCICs may be selected for further processing based on several criteria / cost functions / signal quality metrics (e.g., SNR, interference level).

[0085] Multiple multi-component characteristics / STI / MI of TSCICs (e.g., two components with indices 6 and 7, or three components indexed at 6, 7, and 10) can be calculated. In particular, a k-component characteristic can be a function of k TSCICs, each having k corresponding CIC indices. When k=1, it is a single-component characteristic that can constitute / form a one-dimensional (1D) function, since the CIC indices extend to all possible values. When k=2, a two-component characteristic can constitute / form a 2D function. In a special case, it may depend only on the difference between the two indices. In this case, it may constitute a 1D function. A sum characteristic can be calculated based on one or more multi-component characteristics (e.g., a weighted average / summary value). The characteristics / STI / MI of an object / motion / expression can be monitored based on any multi-component characteristic / summary characteristic.

[0086] Characteristics / STI / MI may include the following: instantaneous / short-term / repetitive / short-term / repetitive / periodic / repetitive / periodic / periodic / periodic / repetitive / periodic / periodic / repetitive / temporal / chronological / orthogonal / transformative / deterministic / stochastic / principal / characteristic / significant / indicatorial / typical / prototypical / persistent / abnormal / sudden / sudden / abnormal / atypical / danger / alert / developmental / transient / transient quantity / characteristic / feature / information, causal and effect indicators / autocorrelation / covariance, autocorrelation function (ACF), spectrum / spectrometry Power spectral density, time / frequency function / transformation / projection, initial / final / time / change / trend / behavior / activity / history / profile / event, place / location / localization / spatial coordinates / change on map / path / navigation / tracking, straight line / rotation / horizontal / vertical / position / distance / displacement / height / velocity / acceleration / change / angular velocity, direction / azimuth, size / length / width / height / azimuth / area / volume / capacity, deformation / deformation, object / direction of motion / angle / shape / form / shrinkage / Magnification, behavior / activity / action, occurrence, fall / accident / security / event, period / frequency / rate / cycle / rhythm / number / quantity, timing / duration / interval, start / beginning / end / present / past / next time / quantity / information, type / grouping / classification / composition, presence / absence / proximity / approach / retreat / entrance / exit, identity / identifier, head / mouth / eyes / breathing / heart / hands / arms / body / gestures / legs / gait / organ characteristics, tidal volume / breathing depth / airflow velocity / inspiratory / expiratory time / ratio, gait / tools / machines / complex movements, signals / movement characteristics Sex / information / features / statistics / parameters / magnitude / phase / degree / dynamics / anomaly / variation / detection / estimation / recognition / identification / display, function slope / derivative / higher derivative / features / mapping / transformation of other properties, mismatch / distance / similarity score / cost / metric, Euclidean / statistics / weighted distance, L1 / L2 / Lk norm, inner product / cross product, tag, test quantity, consumption / unconsumption quantity, state / physical / health / happiness / emotion / mental state, output response, arbitrary configuration / combination, and / or any related properties / information / combination.

[0087] Test quantities can be calculated. Characteristics / STI / MI can be calculated / monitored based on CI / TSCI / features / similarity scores / test quantities. Static (or dynamic) segments / profiles can be identified / calculated / analyzed / acquired / marked / presented / displayed / highlighted / memorized / communicated by analyzing CI / TSCI / features / features / functions / test quantities / features / STI / MI (e.g., target motion / presence / detection / estimation / recognition / identification information). Test quantities can be based on CI / TSCI / features / features / functions / features / STI / MI. Test quantities can be processed / tested / analyzed / compared.

[0088] The test quantity consists of one / all of the following functions: data / vector / matrix / structure, characteristics / STI / MI, CI information (CII, e.g., CI / CIC / feature / magnitude / phase), direction information (DI, e.g., direction CII), dominant / representative / characteristic / indicative / key / typical / exemplary / striking / common / shared / typical / archetypal / average / regular / constant / normal / abnormal / abnormal / non-representative data / vector / matrix / structure, similarity / discrepancy / distance score / cost / metric, auto / cross-correlation / covariance, sum / mean / weighted / trim / arithmetic mean / geometric mean / harmonic mean, variance / deviation / absolute / squared deviation / mean / median / sum / standard deviation / derivative / slope / variability / sum / absolute / squared variation / spread / Variance / Dispersion, Divergence / Skewness / Cartosis / Range / Interquartile Range / Coefficient of Variation / Variance / L Denominator / Interquartile Variance Coefficient / Mean Absolute Value / Square Difference / Gini Coefficient / Relative Mean Difference / Entropy / Maximum (max) / Minus (min) / Median / Percentile / Interquartile, Variance to Mean Ratio, Maximum to Minus Ratio, Dispersion / Regularity / Similarity Scale, Transient Events / Behavior, Statistics / Mode / Likelihood / Histogram / Probability Distribution Function (pdf) / Moment Generation Function / Expectation Function / Value, Behavior, Repetition / Periodicity / Pseudoperiodicity, Impulsivity / Suddenness / Occurrence / Reproducibility, Temporal Profile / Characteristics, Time / Shortest Time / Period / Periodicity / Frequency / Trend / History, Start / Start / End Time / Quantity / Number of Times, Movement Classification / Type, Change, Temporal / Frequency / Periodic Change, etc.

[0089] Identification information / identity / identification information / ID may include MAC address / ASID / USID / AID / UID / UUID, label / tag / index, web link / address, coded / alphabetical ID, name / password / account / account ID, and / or other IDs (e.g., via software / firmware / user / hardware, hardwired, or dongle). IDs may be stored / retrieved (e.g., locally / remotely / permanently / temporarily stored, in a database / memory / cloud / edge / local / hub server). IDs may be associated with any of the following: user / customer / household / information / data / address / telephone number / social security number, user / customer number / record / account, or timestamp / duration / timing. IDs may be made available to Type 1 / Type 2 devices / sensing / SBP initiators / responders. IDs are used for registration / initialization / communication / identification / verification / discovery / recognition / authentication / access control / cloud access / networking / social networking / logging / recording / cataloging / classification / tagging / association / pairing / transactions / electronic transactions / intellectual property control (local / cloud / server / hub, type 1 / type 2 / neighbor / user / other devices, by user, etc.).

[0090] Objects include people / pets / animals / plants / users, babies / men / professionals / staff / personnel / personnel / personnel / personnel / personnel / nurses / technicians / servicemen / patients / students / travelers / inmates / inmates / tracked objects, vehicles / cars / drones / robots / wagons / transportation machinery / movable objects / items / items / parts / machinery / lifts / elevators, goods / luggage / people / items / packaging / luggage / equipment / workflow / assembly lines / warehouses / factories / stores / supermarkets / distribution / logistics / transportation / manufacturing / retail / wholesale / business centers / facilities / hubs, cleaning tools in telephones / computers / laptops / tablets / dongles / plugins / companions / tools / peripherals / accessories Lee / wearable / furniture / electrical appliances / amenities / gadgets, IoT / network / smart / portable devices, watches / glasses / speakers / toys / strollers / keys / wallets / handbags / backpacks, goods / cargo / luggage / equipment / motors / machines / appliances / tables / chairs / air conditioners / doors / windows / heaters / fans, lighting / appliances / stationary objects / televisions / cameras / audio / video / surveillance equipment / parts, tickets / parking tickets / toll tickets / airline tickets, credit cards / plastic cards / access cards, fixed / changing / intangible objects, mass / solids / liquids / gases / fluids / smoke / fire / flames, signs, electromagnetic (EM) sources / mediums, and / or other objects.

[0091] An object can have multiple parts, each with a different motion (e.g., change of position / direction). The object could be a person walking forward. While walking, their left / right hand can move in different directions with different instantaneous motions / velocities / accelerations.

[0092] The object may / may not be able to communicate with several networks, including WiFi, MiFi, 4G / LTE / 5G / 6G / 7G / 8G, Bluetooth / NFC / BLE / WiMax / Zigbee / mesh / adhoc networks, etc. AC-powered machinery that is moved during installation, cleaning, maintenance, and renewal can be bulky. It may be placed on / within a movable platform such as an elevator / conveyor / lift / pad / belt / robot / drone / forklift / car / boat / vehicle. Type 1 / Type 2 devices can be attached to / moved to the object. Type 1 / Type 2 devices may be part of a portable / another device with a module (e.g., a module / device with a module, large / large / small / large / large / lightweight, e.g., coin-sized / cigarette-pack-sized), and Type 1 / Type 2 / portable / another device may or may not be attached to / moved with an object, and may have wireless (e.g., via Bluetooth / BLE / Zigbee / NFC / WiFi) or wired (e.g., USB / micro USB / Firewire / HDMI®) connection (e.g., via WiFi / cellular network) to nearby devices for network access. Nearby devices may be objects / telephones / APs / IoT / devices / appliances / peripherals / amenities / furniture / vehicles / gadgets / wearables / network / computing devices. Nearby devices may be connected to several servers (e.g., cloud servers via a network / internet). It may or may not be portable / movable, and may or may not move with an object. Type 1 / Type 2 / Portable / Proximity / Other devices can be powered by battery / solar / DC / AC / other power sources, and are replaceable / non-replaceable, rechargeable / non-rechargeable. They may also be wirelessly charged.

[0093] Type 1 / Type 2 / Portable / Proximity / Other devices may include any of the following: computers / laptops / tablets / pads / telephones / printers / monitors / batteries / antennas, peripherals / accessories / sockets / plugs / chargers / switches / adapters / dongles, Internet of Things (IoT), televisions / soundbars / HiFi / speakers / set-top boxes / remote controls / panels / game consoles, APs / cables / broadband / routers / repeaters / extenders, electrical appliances / utilities / fans / refrigerators / washing machines / dryers / microwave ovens / ovens / stoves / ranges / lighting / lamps / pipes / faucets / lighting / air conditioners / heaters / smoke detectors, personal belongings / watches / glasses / goggles / buttons / bracelets / chains / jewelry / rings / belts / clothing / faucets / shirts / pants / dresses / gloves / handwear / shoes / footwear / hats / headwear / bags / wallets / purses / makeup / cosmetics / ornaments / books / magazines / paper / stationery / signs / posters / displays / printed materials, furniture / fixtures / Tables / desks / chairs / sofas / beds / cabinets / shelves / racks / storage / boxes / buckets / baskets / packaging / cars / tiles / boards / bricks / blocks / mats / panels / curtains / cushions / pads / carpets / materials / building materials / glass, amenities / sensors / clocks / pots / dishes / containers / bottles / cans / cookware / plates / cups / bowls / toys / balls / tools / pens / rackets / keys / bells / cameras / microphones / paintings / frames / mirrors / coffee makers / doors / windows, food / pills / medicines, implantable / implantable / gadgets / devices / equipment / machines / controllers / machine tools, garage openers, keys / plastics / payment / credit cards / tickets, solar panels, key trackers, fire extinguishers, trash cans / garbage cans, WiFi-enabled devices, smart devices / machines / systems / residential / office / building / warehouse / facility / vehicles / automobiles / bicycles / motorcycles / boats / ships / airplanes / carts / wagons, residential / vehicles / office / factory / building / manufacturing / production / computing / security / other devices.

[0094] One / two / more Type 1 / Type 2 / Portable / Proximity / Another device / Server can determine the object's initial properties / STI / MI and / or share intermediate information. One of the Type 1 / Type 2 devices can move with the object (e.g., "Tracker Bot"). The other Type 1 / Type 2 device cannot move with the object (e.g., "Origin Satellite", "Origin Register"). Both can have known properties / STI / MI. Initial STI / MI can be calculated based on known STI / MI.

[0095] A venue can be a space such as: sensing area, room / house / home / office / workplace / building / facility / warehouse / factory / shop / vehicle / real estate, indoor / outdoor / closed / semi-closed / open / semi-open / closed / aerial / floating / underground space / area / structure / enclosure, wood / glass / metal / material / structure / frame / beam / panel / column / wall / floor / door / ceiling / window / cavity / gap / opening / reflective / refracting medium / fluid / structural material / space / area with fixed / adjustable layout / shape, human body / animal / plant body / cavity / organ / bone / blood / blood vessel / air tube / ventilator / teeth / soft / hard / non-hard tissue, manufacturing / repair / maintenance / mining / parking / storage / transportation / ship / logistics / sports / entertainment / amusement / public / entertainment / government / region / elderly / senior / space facility / terminal / hub, logistics center / shop, machine Engines / devices / assembly lines / workflows, cities / rural areas / suburbs / metropolitan areas, stairs / escalators / elevators / corridors / passages / tunnels / caves / caves / waterways / ducts / pipes / tubes / lifts / wells / passages / roofs / basements / depressions / alleys / roads / paths / highways / sewers / ventilation systems / networks, automobiles / trucks / buses / vans / containers / ships / boats / submarines / trains / trams / airplanes / mobile homes, stadiums / cities / sports fields / parks / fields / tracks / courts / gymnasiums / halls / markets / supermarkets / squares / plazas / construction sites / hotels / museums / schools / hospitals / universities / garages / malls / airports / railway stations / bus stops / terminals / hubs / platforms, valleys / forests / trees / terrain / landscapes / gardens / parks / patios / land, and / or gas / oil / water pipes / lines. A venue may consist of the interior / exterior of a building / facility. A building / facility may be single-story or multi-story, and may be partially underground.

[0096] Events may be monitored based on TSCI. Events may also be object / motion / gesture / walking related, such as falls, rotations / hesitations / pauses, impacts, shocks (e.g., punching bags / doors / beds / chairs / tables / desks / cabinets / boxes / another person / animal / bird / flights / balls / bowling / tennis / soccer / volleyballs / soccer / baseball / basketball), two-person actions (e.g., balloon releasers / fish catchers / molding clay / paper / typing computers), movement in a garage, smartphones / people walking around venues, and motion of autonomous / mobile objects / machines (e.g., vacuum cleaners / utilities / autonomous vehicles / cars / drones).

[0097] The tasks may include: (a) Sensing tasks, i.e., any of the following: object / vehicle / machine / tool / human / baby / elderly / patient / intruder / pet presence / proximity / activity / daytime activity / health status / respiration / vital signs / heart rate / health status / sleep / sleep stage / walking / position / distance / speed / acceleration / navigation / tracking / movement / safety / danger / fall / intrusion / security / threat to life / movement / motion / decrease / pattern / periodic / repetitive / periodic / steady / regular / transient / sudden / suspicious movement / irregularity / trend / change / respiration / human biometric information / environmental information / gate / gesture / room / area / zone / street, monitoring / sensing / detection / recognition / estimation / verification / identification / authentication / classification / location / guidance / navigation / tracking / counting, (b) Computational tasks, i.e., any of the following: signal processing / preprocessing / postprocessing / conditioning / denoising / calibration / analysis / feature extraction / transformation / mapping / supervised / unsupervised / semi-supervised / classification / machine learning / deep learning / learning / clustering / learning / PCA / eigendecomposition / frequency / time / function decomposition / neural networks / map-based / model-based processing / correction / shape estimation / analytical calculations, (c) IoT tasks, i.e., any of the following: smart tasks for venues / users / objects / people / pets / homes / houses / offices / workplaces / buildings / facilities / warehouses / factories / stores / vehicles / property / structures / assembly lines / IoT / devices / systems, energy / power management / transfer, wireless power transfer, interaction / cooperation with users / objects / intruders / people / animals (: presence / motion / gestures / walking / activity / behavior / voice / commands / instructions / queries / music / sound / images / video / location / movement / danger / threat detection / recognition / monitoring / analysis / response / execute / synthesis, generating / acquiring / playing / displaying / rendering / synthesizing / exchanging / responding / presenting / experience / media / multimedia / expression / sound / voice / music / images / video / animation / web page Page / text / message / notification / attention / inquiry / warning, detection / recognition / monitoring / interpretation / analysis / recording / storage of user / intruder / object input / action / gesture / location / activity), activation / control / setting (on / off / control / lock / unlock / open / close / adjust / set) of devices / systems (e.g., vehicles / drones / electric / machinery / air conditioning / heating / lighting / ventilation / learning / entertainment / IoT / security / siren / access system / device / door / window / garage / lift / elevator / escalator / speaker / television / lighting / peripherals / accessories / wearable / furniture / appliances / amenities / gadgets / alarm / camera / game / coffee / cooking / heater / fan / housekeeping / household / office equipment / device / robot / vacuum cleaner / assembly line), (d) Various tasks, namely any of the following: data / parameter / analysis / transmission / encoding / encryption / storage / analysis, upgrade / management / configuration / adjustment / broadcast / synchronization / network / encryption / communication / protection / compression / storage / database / archive / query / cloud computing / presentation / augmentation / virtual reality / other processing / tasks. The task may be performed by a Type 1 / Type 2 / Nearby / Portable / Another device, and / or by a hub / Local / Edge / Cloud server.

[0098] Tasks may also include: detecting / recognizing / monitoring / locating / interpreting / analyzing / recording / storing users / visitors / intruders / objects / pets; interacting / engaging / conversing / exchanging with users / objects / visitors / intruders / humans / babies / pets; detecting / locating / recognizing / monitoring / analyzing / interpreting / learning / training / responding / exercising / synthesizing / generating / recording / storing / summarizing health / health status / daily life / activities / behavior / patterns / exercise / diet Intake / Toilet / Work / Play / Rest / Sleep / Relax / Danger / Routine / Restriction / Habit / Tendency / Normal / Abnormal / Regularity / Irregularity / Change / Presence / Action / Gesture / Walking / Facial Expression / Emotion / State / Voice / Command / Instruction / Question / Inquiry / Music / Sound / Place / Movement / Fall / Threat / Discomfort / Illness / Environment / , Generate / Search / Play / Display / Render / Compose / Exchange / Response / Presentation / Report / Experience / Media / Multimedia / Expression / Sound / Voice / Music / Image / Video / Animation / Webpage / Text / Message / Notification / Reminder / Inquiry / Warning, User / Intruder / Object Input / Action / Gesture / Location / Activity Detection / Recognition / Monitoring / Interpretation / Analysis / Record / Save), Detect / Check / Monitor / Location / Manage / Control / Adjust / Set / Lock / Unlock / Alarm / Release / Open / Close / Overall / Partial / Activate / O Turning on / off (e.g., vehicles / robots / drones / electricity / machinery / air conditioning / heating / ventilation / HVAC / lighting / cleaning / entertainment / IoT / security / sirens / access systems / devices / items / components, doors / windows / garages / lifts / elevators / escalators / speakers / televisions / lighting / peripherals / accessories / wearables / furniture / appliances / amenities / gadgets / alarms / cameras / games / coffee / cooking / heaters / fans / housekeeping / household / office machinery / devices / vacuum cleaners / assembly lines / windows / garages / doors / blinds / curtains / panels / solar panels / shades), detecting / monitoring / identifying where users / pets do things (e.g., sitting on the sofa / sleeping / sleeping in the bedroom / running on the treadmill / cooking / watching TV / kitchen / eating in the dining room / going up / down stairs / outside / inside / using the toilet),Do something (e.g., automatically do something upon detection (generate a message / response / warning / clarification / notification / report), automatically do something for the user upon detecting the user's presence, turn on / off / alarm / control / adjust / dimm lights / music / radio / TV / HiFi / STB / computer / speaker / smart device / air conditioning / ventilation / heating system / curtains / lightshades, turn on / off / preheat / control coffee maker / kettle / cooker / oven / microwave / other cooking appliances, check / manage temperature / settings / weather forecast / phone / message / email / system check, present / interact / engage / dialogue / conversation (via smart speaker / display / screen, via web page / email / messaging system / notification system, etc.)).

[0099] When the user arrives home by car, the task may automatically detect the user / car's approach, open the garage / door upon detection, turn on the driveway / garage lights as the user approaches the garage, and / or turn on the air conditioner / heater / fan. When the user enters the house, the task may automatically turn on the porch light / garage light, play a greeting message with the user's favorite music / radio / news / channel, monitor the user's mood, adjust the lighting / sound environment according to the mood / current / imminent events (for example, if the user has dinner plans with their girlfriend soon, play romantic lighting / music), prepare a warm meal using the microwave prepared in the morning, check the weather forecast for tomorrow / news, check the user's interests, calendar / to-do list, answer phone calls / messaging system / email, provide verbal reports using dialogue system / speech synthesis, and / or TV / entertainment. Using voice tools with visual tools such as entertainment systems / computers / notebooks / displays / lights / colors / brightness / pattern symbols, using haptics / virtual reality / gestures / tools, using smart devices / appliances / materials / furniture / fixtures, using servers / hub devices / cloud / fog / edge servers / home / mesh networks, using messaging / notification / communication / scheduling / email tools, using UI / GUI, using scents / smells / fragrances / tastes, using neural / nervous system / tools, or any combination) to prepare the user for someone's birthday / phone call. The task may pre-turn on the air conditioning / heating / ventilation system and / or pre-adjust the temperature setting of a smart thermostat. When the user moves from the entrance to the living room, the task may turn on the living room lights, open the living room curtains, open the windows, turn off the entrance light behind the user, turn on the TV / set-top box, set the TV to the user's preferred channel and / or adjust appliances according to the user's preferences / conditions / state (e.g., adjusting the lighting, selecting / playing music to create a romantic atmosphere).

[0100] If the user wakes up in the morning, tasks could include detecting the user's motion turning around in the bedroom, opening blinds / curtains / windows, turning off the alarm clock, adjusting the night-to-day temperature to a profile, turning on the bedroom light, turning on the bathroom light as the user approaches the bathroom, checking radio / streaming channels, playing the morning news, turning on the coffee machine, turning on the preheating water, and / or turning off the security system. If the user walks from the bedroom to the kitchen, tasks could include turning on the kitchen / hallway light, turning off the bedroom / bathroom light, moving music / messages / reminders from bedroom to bedroom, turning on the kitchen TV, changing the TV to the morning news channel, lowering the kitchen blinds, opening the kitchen window, unlocking the back door for the user to check the backyard, and / or adjusting the kitchen temperature settings.

[0101] When a user leaves home for work, the task is to detect the user's departure, play a farewell / goodbye message, open and close the garage door, turn the garage / driveway lights on / off, close / lock all windows / doors (if the user forgets), shut off electrical appliances (stove / microwave / oven, etc.), turn on / activate the security system, adjust the lighting / air conditioning / heating / ventilation system to the "away" profile to save energy, and / or send alerts / reports / updates to the user's smartphone.

[0102] Motion can include: no motion, motion sequences, static / non-moving motion, changes in action / position / location, daily / weekly / monthly / yearly / repeated / activity / behavior / routine, transient / temporal variation / fall / repeated / periodic / pseudoperiodic motion / breathing / heartbeat, deterministic / non-deterministic / probabilistic / chaotic / random motion, complex / compound motion, non / pseudo / cyclonic / stationary random motion, changes in electromagnetic properties, human / animal / plant / body / machine / vehicle / drone motion. Air / wind / weather / water / fluid / ground / surface / earthquake motion, human-machine interaction, normal / abnormal / danger / warning / suspicious motion, imminent / rain / fire / flood / tsunami / explosion / collision, head / face / eyes / mouth / tongue / neck / fingers / hands / arms / shoulders / up / down / body / chest / abdomen / waist / legs / feet / joints / knees / elbows / skin / subcutaneous / subcutaneous tissue / blood vessels / organs / heart / lungs / stomach / intestines / eating / breathing / talking / singing / dancing / coordinated motion, facial / eye / mouth expressions, and / or hand / arm / gestures / walking / UI / keystrokes / typing strokes.

[0103] Type 1 / Type 2 devices may include heterogeneous ICs, low-noise amplifiers (LNAs), power amplifiers, transmit / receive switches, media access controllers, baseband radios, and / or 2.4 / 3.65 / 4.9 / 5 / 6 / sub-7 / over-7 / 28 / 60 / 76GHz / other radios. Heterogeneous ICs may include processors / memory / software / firmware / instructions. They may support broadband / wireless / mobile / mesh / cellular networks, WLAN / WAN / MAN, standards / IEEE / 3GPP(registered trademark) / WiFi / 4G / LTE / 5G / 6G / 7G / 8G, IEEE 802.11 / a / b / g / n / ac / ad / af / ah / ax / ay / az / be / bf / 15 / 16, and / or Bluetooth / BLE / NFC / Zigbee / WiMax.

[0104] The processor may include any of the following: general-purpose / dedicated / embedded / multicore processors, microprocessors / microcontrollers, multi / parallel / CISC / RISC processors, CPUs / GPUs / DSPs / ASICs / FPGAs, and / or logic circuits. Memory may include non-volatile memory, RAM / ROMs / EPROMs / EEPROMs, hard disks / SSDs, flash memory, CD- / DVD-ROMs, magnetic / optical / organic / storage systems / networks, network / cloud / edge / local / external / internal storage devices, and / or any non-temporary storage media. The instruction set may consist of machine-executable code in hardware / ICs / software / firmware and may be embedded / preloaded / loaded during boot-up / on-the-fly / on-demand / pre-installation / installation / download.

[0105] Processing / preprocessing / postprocessing can be applied to data (e.g., TSCI / features / characteristics / STI / MI / test quantities / intermediate / data / analysis) and may have multiple steps. Steps / processing / preprocessing / postprocessing may include any of the following: operand / LOS / non-LOS / single-link / multi-link / component / item / quantity calculation functions, magnitude / norm / phase / features / energy / time axis / similarity / distance / characteristic score / measure calculation / extraction / correction / cleaning, linear / nonlinear / FIR / IIR / MA / AR / ARMA / Kalman / particle filtering, low-pass / band-pass / high-pass / median / rank / quartile / percentile / mode / selection / adaptive filtering, interpolation / extrapolation / decimation / subsampling / upsampling / resampling.Matched filtering / enhancement / restoration / denoising / smoothing / conditioning / spectral analysis / average subtraction / removal, linear / nonlinear / inverse / frequency / time transformation, Fourier transform (FT) / DTFT / DFT / FFT / wavelet / Laplace / Hilbert / Hadamard / trigonometric / sine / cosine / DCT / power of 2 / sparse / fast / frequency transformation, zero / cyclic / padding, graph-based transformation / processing, decomposition / orthogonal / non-orthogonal / perfect projection / eigendecomposition / SVD / PCA / ICA / compressed sensing, grouping / folding / sorting / comparison / soft / hard / threshold / clipping, first-order / second-order / higher-order derivative / integral / convolution / multiplication / division / addition / subtraction, local / global / maximization / minimization, recursion / iteration / constraint / batch processing, least squares mean / absolute error / deviation, cost function optimization, neural network / detection / recognition / Classification / identification / estimation / labeling / association / tagging / mapping / remapping / learning / clustering / machine learning / supervised / unsupervised / semi-supervised learning / networks, vectors / quantization / encryption / compression / matching pursuit / scrambling / encoding / storage / retrieval / transmission / reception / time domain / frequency domain / normalization / scaling / representation / combination / partition / tracking / monitoring / shape / silhouette / motion / activity / analysis, pdf / histogram estimation / importance / Monte Carlo sampling, error detection / protection / correction, do nothing, time variation / adaptive processing, conditioning / weighting / averaging / selected components / on links, arithmetic / geometric / harmonic / trimmed mean / centroid / medoid calculation, morphological / logical operations / permutation / combination / sorting / AND / OR / XOR / sum / intersection, vector operations / addition / subtraction / multiplication / division, and / or other operations. Processing may be applied individually or jointly. Acceleration using GPU / DSP / coprocessor / multicore / multiprocessing can also be applied.

[0106] Functions can include: characteristics / features / magnitude / phase / energy, scalar / vector / discrete / continuous / polynomial / exponential / logarithmic / triangular / transcendental / logical / peacewise / linear / algebraic / nonlinear / circular / peacewise linear / real / complex / vector value / reciprocal / absolute value / index / limit / floor / circular / sign / composition / slide / movement function, differential / integral, function of a function, one-to-one / one-to-many / many-to-one / many-to-many function, mean / mode / median / percentile / maximum / minimum / range / statistics / histogram, local / global maximum / minimum / zero crossing, variance / variability / Spread / variance / deviation / standard deviation / divergence / range / interquartile range / total variation / absolute value / total deviation, arithmetic mean / geometric mean / harmonic mean / trimmed mean / square / cube / square root / power, thresholding / clipping / rounding / truncating / quantization / approximation, time functions processed by operations (e.g., filtering), sine / cosine / tangent / bitangent / elliptic / parabola / hyperbola / game / zeta function, stochastic / random / ergodic / stationary / deterministic / periodic / repeating function, inverse transform / frequency / discrete time / Laplace / Hilbert / sine / cosine / triangular / small wave / integer / power of 2 / sparse variable Conversion, orthogonal / non-orthogonal / eigenprojection / decomposition / eigenvalues / singular values / PCA / ICA / SVD / compressed sensing / neural networks / feature extraction / movement window function for neighboring items in time series / filtering function / convolution / short time / discrete transform / Fourier / cosine / sine / Hadamard / wavelet / sparse transform / matching pursuit / approximation, graph-based processing / transformation / graph signal processing, classification / identification / class / group / category / labeling, processing / preprocessing / postprocessing, machine / learning / detection / estimation / feature extraction / learning network / feature extraction / denoising / signal enhancement / Encoding / Encryption / Mapping / Vector Quantization / Remapping / Low-pass / High-pass / Band-pass / Matched / Kalman / Particle / FIR / IIR / MA / AR / ARMA / Median / Mode / Adaptive Filtering, First-order / Second-order / Higher-order Differential / Integral / Zero-crossing / Smoothing, Up / Down / Random / Importance / Monte Carlo Sampling / Resampling / Transformation, Interpolation / Extrapolation, Short-term / Long-term Statistics / Automatic / Cross-correlation / Moment Generation Function / Time-averaged / Weighted average, Special / Bessel / Beta / Gamma / Gauss / Poisson / Integral Complementary Error Function.

[0107] The sliding time window can change width / size over time. To enable fast and accurate imaging, it can start small or large and increase / decrease over time to a steady state size that corresponds to the frequency / period / duration / characteristics / STI / MI of the motion being monitored. The window size / time shift between adjacent windows can be changed / adjusted / varied / modified constantly / adaptively / dynamically / automatically (e.g., based on battery life / power consumption / available computing power / changes in the amount of monitored / nature of the monitored motion / user requests / selections / instructions / commands).

[0108] The characteristic / STI / MI may be determined based on the characteristic values / points of the function and / or the associated arguments of the function (time / frequency, etc.). The function may be the result of a regression. The characteristic values / points may include the local / global / constrained / significant / 1st / 2nd / ith maximum / minimum / extreme / zero crossings of the function (e.g., with positive / negative time / frequency / arguments). The local signal-to-noise ratio (SNR) or SNR-like parameter may be calculated for each pair of adjacent local maximums (peaks) / local minimums (troughs) of the function, which may be some function (e.g., linear / logarithmic / exponential / monotonical / power / polynomial) of the fraction or difference of the amount of the local maximum (e.g., power / magnitude) to the amount of the local minimum. A local maximum (or minimum) may be significant if its SNR is greater than a threshold and / or its amplitude is greater than (or less than) another threshold. Local maximums / minims may be selected / identified / calculated using persistence-based approaches. Several significant local maximums / minims may be selected based on selection criteria (e.g., quality criteria / conditions, the strongest / most consistent significant peak within a range). Unselected significant peaks may be remembered / monitored as "reserved" peaks for use in future selections within a future sliding time window. For example, a particular peak (e.g., at a specific argument / time / frequency) may appear consistently over time. Initially, it may be significant but not selected (because other peaks may be stronger). Later, it may become stronger / consistently dominant. Once selected, it may be backtraced in time and selected at an earlier time to replace previously selected peaks (which may be instantaneously strong / dominant but not persistent / consistent). Peak consistency may be measured by tracing, or the duration for which it is significant. Alternatively, local maximums / minims may be selected based on a finite state machine (FSM). The decision threshold may change over time and be adjusted adaptively / dynamically (e.g., based on backtrace timing / FSM, or data distribution / statistics).

[0109] The similarity score (SS) / component SS may be calculated based on two temporally adjacent CI / CIC, one TSCI, or two different TSCIs. Pairs may arise from the same / different (one or more) slide windows. The SS or component SS may include time reversal resonating strength (TRRS), auto / cross-correlation / covariance, dot product of two vectors, L1 / L2 / Lk / Euclidean / statistical / weighted / distance score / norm / index / quality index, signal quality condition, statistical features, discrimination score, neural network / deep learning network / machine learning / training / discrimination / weighted averaging / preprocessing / denoising / signal tuning / filtering / time correction / timing compensation / phase offset compensation / transformation / component-wise operation / feature extraction / FSM, and / or other scores.

[0110] Any threshold can be fixed (e.g., 0, 0.5, 1, 1.5, 2), predetermined, and / or adaptively / dynamically determined (e.g., by FSM, or based on time / space / location / antenna / path / link / state / battery life / remaining battery life / available resources / power / computational power / network bandwidth). Thresholds can be applied to test quantities to distinguish between two events / conditions / situations / states. Data (e.g., CI / TSCI / feature / similarity score / test quantity / feature / STI / MI) can be collected under A / B in training conditions. Test quantities calculated based on the data (e.g., their distribution) can be compared under A / B to select thresholds based on several criteria (e.g., maximum likelihood (ML), maximum a posterio probability (MAP), discrimination training, minimum type 1 (or 2) error for a given type 2 (or 1) error, quality criteria, signal quality conditions). The threshold may be adjusted (for example, to achieve different sensitivities) based on (e.g., object / movement / direction / action / characteristics / STI / MI / size / characteristics / habit / behavior / venue / feature / fixture / furniture / barrier / material / living thing / object / boundary / surface / location / map / machine / model / event / state / situation / condition / time / timing / duration / state / history / user / preference). The iterative algorithm may stop after N iterations, after a timeout period, or after conditions are met in which the test quantity can be fixed / adaptive / dynamically adjusted (e.g., an update amount greater than the threshold).

[0111] Searching for local extrema involves constraints / minimization / maximization, statistical / dual / constrained / convex / global / local / combinatorial / infinite-dimensional / multiobjective / multimodal / non-differential / particle swarm / simulation-based optimization, linear / nonlinear / quadratic / higher-order regression, linear / nonlinear / stochastic / constrained / dynamical / mathematical / connection-prohibited / convex / semi-defined / conical / internal / fractional / integer / sequential / quadratic programming, conjugate / gradient / sub-gradient / coordinate / scalar descent, Newtonian / complex / iteration / point / elliptic / quasi-Newtonian / interpolation / memory / genetics / evolution / pattern / gravity search / algorithms, constraint satisfaction, variational methods, optimal control, spatial mapping, heuristics / metahouristics, numerical analysis, simultaneous perturbation stochastic approximation, stochastic tunneling, dynamic relaxation, hill climbing, simulated annealing, differential evolution, robust / line / tab / reaction search / optimization, curve fitting, least squares, variational methods, and / or variational methods. It can be associated with the objective function / loss function / cost function / utility function / fitness function / energy function.

[0112] Regression can be performed using a regression function to fit data or a function of data (e.g., ACF / transformed / mapped) within a regression window. The length / position of the regression window can be changed during iteration. The regression function may be linear / quadratic / cubic / polynomial / another function. Regression may minimize any of the following: mean / weighted / absolute / squared deviation, error, total value / component / weighted / mean / absolute / squared / higher-order / other errors / cost (e.g., in the projection region / selected axis / orthogonal axis), robust error for smaller error magnitudes (e.g., first error (e.g., squared), second error for larger error magnitudes (e.g., absolute), and / or weighted sum / average of multiple errors (e.g., absolute / squared error). Errors associated with different links / paths may have different weights (e.g., links with less noise may have higher weights). Regression parameters (e.g.) For example, the maximum / minimum regression error of the regression function in the regression window, and the time offset related to the window's position / width, may be initialized and / or updated during iteration (based on, for example, target values / range / profile, characteristics / STI / MI / test quantities, object motion / quantity / count / position / state, past / current trends, extreme values / quantity / distribution in the previous window, signal carrier / subcarrier frequencies / bandwidth, antenna quantities related to the channel, noise characteristics, histogram / distribution / center / F-distribution, and / or thresholds). Upon convergence, the current time offset becomes the center / left / right (or fixed relative position) of the regression window.

[0113] In a presentation, information may be displayed / presented (e.g., using a venue map / environment model). The information may include: current / past / corrected / approximate / map / location / velocity / acceleration / zone / region / area / segmentation / coverage area, direction / route / trace / history / traffic / summary, frequently visited areas, customer / crowd events / distribution / behavior, crowd control information, acceleration / velocity / vital signs / respiration / heart rate / activity / emotion / sleep / state / rest information, motion statistics / MI / STI, presence or absence of moving objects / people / pets / objects / vital signs, gestures (e.g., hands / arms / legs / body / head / face / mouth / eyes) / meaning / control (control of devices using gestures), location-based gesture control / motion interpretation, ID / identifier (e.g., object / person / user / pet / zone / region, device / machine / vehicle / drone / car / boat / bicycle / television / air conditioner / fan / , self-guided machine / device ID / identifier). Objects / people / users / pets / zones / regions, devices / machines / vehicles / drones / cars / boats / bicycles / televisions / air conditioners / fans / self-guided machines / devices / vehicles), environmental / weather information, gestures / gesture control / motion tracing, earthquakes / explosions / storms / fires / temperature, collisions / impacts / vibrations, events / doors / windows / opening / closing / falls / accidents / burns / freezes / water / wind / air movement events, repetitive / pseudo-periodic events (e.g., running on a treadmill, jumping, jump rope, somersaults, etc.), and / or vehicle events. Location may be 1 / 2 / 3-dimensional (e.g., represented as a 1D / 2D / 3D rectangle / polar coordinate), relative (e.g., rt map / environment model), or relational (e.g., near / distance at a point, midway between two points, around a corner, on an upper floor, on a tabletop, on the ceiling, on the floor, on a sofa).

[0114] Information (e.g., location) may be marked / displayed by some symbol. The symbol may change over time, blink, or pulsate, while changing color, intensity, size, or orientation. The symbol may be a number that reflects an instantaneous quantity (e.g., analysis / gesture / state / status / action / motion / breathing / heart rate, temperature / network traffic / connectivity / remaining power). The symbol / size / orientation / color / intensity / rate of change / characteristics may reflect the respective motion. The information may be in text form, or presented visually / orally (e.g., using pre-recorded speech / speech synthesis) / mechanically (e.g., animated gadgets, movement of moving parts).

[0115] User devices may include smartphones / tablets / speakers / cameras / displays / TVs / gadgets / vehicles / appliances / devices / IoT, devices with UI / GUI / voice / speech / recording / capture / sensors / playback / display / animation / VR / AR (augmented reality) / voice (assistance / recognition / synthesis) capabilities, and / or tablets / laptops / PCs.

[0116] Maps / floor plans / environmental models (e.g., homes / offices / buildings / stores / warehouses / facilities) may be 2 / 3 / high-dimensional. They may change / evolve over time (e.g., rotate / zoom / move / jump on the screen). Walls / windows / doors / entrances / exits / restricted areas may be marked. They may include multiple layers (overlays). They may include maintenance maps / models that include water pipes / gas pipes / cables / air ducts / crawl spaces / ceilings / underground layouts.

[0117] A venue can be divided / subdivided / zoned / grouped into multiple zones / regions / sectors / sections / territory / district / area / neighborhood / area / stretch / expansion, such as bedrooms / living rooms / dining rooms / rest areas / storage areas / utilities / warehouses / meeting rooms / correspondence / areas / areas / regions, etc. It can be presented in maps / floor plans / models that have presentation characteristics (e.g., brightness / intensity / luminance / color / chrominance / texture / animation / flashing / rate).

[0118] An example of a disclosed system / device / method. Stephen 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 staircases, Stephen decides to use one Type 2 device (named A) and two Type 1 devices (named B and C) on the first floor. The first floor has three rooms lined up: a kitchen, a dining room, and a living room, with the dining room in the middle. A is placed in the dining room, B in the kitchen, and C in the living room, dividing the first floor into three zones (dining room, living room, kitchen). When motion is detected by the A / B pair and / or the A / C pair, the system analyzes the TSCI / feature / characteristic / STI / MI and associates the motion with one of the three zones.

[0119] When Stephen and his family go on a holiday camping trip, he uses a mobile phone app (e.g., an Android phone app or an iPhone® app) to turn on a motion detection system. When the system detects motion, an alert signal (SMS, email, push message to the mobile phone app, etc.) is sent to Stephen. If Stephen pays a monthly fee (e.g., $10 / month), the service provider (e.g., a security company) receives the alert signal via a wired (e.g., broadband) / wireless (e.g., WiFi / LTE / 5G) network and takes security action (e.g., calling Stephen to confirm the issue, sending a message to someone to check at home, contacting the police on Stephen's behalf).

[0120] Stephen loves his elderly mother and cares for her well-being when she's home alone. When his mother is home while the family is on break (e.g., work / shopping / vacation), Stephen uses his mobile app to turn on a motion detection system to make sure she's okay. He uses the mobile app to monitor his mother's motion while she's at home. If Stephen uses the mobile app to see his mother moving around the house in one of three areas, he knows she's okay, according to her daily routine. Stephen appreciates that the motion detection system can help him monitor his mother's well-being while he's away from home.

[0121] On a typical day, his mother wakes up at 7 a.m., cooks breakfast in the kitchen for 20 minutes, and eats breakfast in the dining room for 30 minutes. Then, she exercises daily in the living room, and afterwards sits on the sofa in the living room and watches her favorite TV shows. The motion detection system allows Stephen to see the timing of motion in three areas of the house. If the motion is consistent with the daily routine, Stephen knows that his mother should be fine. However, if the motion pattern is abnormal (for example, no motion until 10 a.m., or the kitchen / stillness is too long), Stephen suspects something is wrong and calls his mother to check on him. Stephen can even have someone else (for example, a family member / neighbor / paid staff / friend / social worker / service provider) check on his mother.

[0122] One day, Stephen feels like changing the device's position. He simply unplugs it from the AC power outlet of the original device and plugs it into another AC power outlet. He is pleased that the motion detection system is plug-and-play and that the repositioning does not affect the system's operation. It works as soon as it is powered on.

[0123] Subsequently, Stephen decided to install a similar setup (i.e., one Type 2 and two Type 1 devices) on the second floor to monitor the bedrooms there. Again, the system setup is very simple, requiring only the connection of the Type 2 and Type 1 devices to an AC power outlet on the second floor. No special installation is required. He can monitor motion on both the ground floor and the second floor using the same mobile app. Each Type 2 device on the ground floor and the second floor can interact with all the Type 1 devices on the ground floor and the second floor. Stephen has more than twice the capability of the combined system.

[0124] The disclosed system can be applied to many uses. Type 1 / Type 1 / Type 2 devices may be any WiFi-enabled device in a home / office / table, ceiling, floor, or wall (e.g., smartphone IoT / IoT / appliance / STB / refrigerator / speaker / STB / refrigerator / fan / heater / fan / air conditioner / router / tablet / computer / tablet / plug / pipe / lamp / smoke sensor / furniture / shelf / cabinet / door / lock / sofa / table / chair / piano / device / wearable / watch / tag / key / ticket / belt / wallet / pen / hat / necklace / embedded / phone / glasses / glass panel / game device). They may be placed in a conference room to count people. They may form a welfare monitoring system to monitor the daily activities of the elderly and detect any signs of symptoms (e.g., dementia, Alzheimer's disease). They may be used in an infant monitor to monitor the vital signs (respiration) of an infant. They may be placed in a bedroom to monitor sleep quality and detect any sleep apnea. They may be placed inside vehicles to monitor the health of passengers and drivers and to detect drowsy drivers or babies left in hot vehicles. They can be used in logistics to prevent human trafficking by monitoring people hidden in trucks / containers. They can be deployed by emergency services in disaster areas to search for victims trapped in rubble. They can be deployed in security systems to detect intruders.

[0125] In some embodiments, motion statistics (MS) or motion information (MI) may be calculated within a sliding time window based on all CIs (e.g., CSI / CIR / CFR) within that sliding time window in order to perform a wireless sensing task. The task may be to monitor the motion of an object in a venue based on the MS / MI. The MS / MI may be STIs or characteristics of an object or the motion of an object. In some cases, a certain CI may be anomalous (e.g., due to interference, noise), thereby causing anomalous behavior of the MS / MI and interfering with / interrupting the wireless sensing task. Such anomalous CIs (and anomalous MS / MIs) may be considered outliers. In some embodiments, methods are disclosed for detecting / suppressing / removing / excluding outliers or the effects of outliers within a sliding time window. The system can classify the sliding time window into one of three categories: "normal," "moderately abnormal," or "severely abnormal."

[0126] In some embodiments, for a “normal” sliding time window (or, for example, a reliable / regular time window with no abnormal / outlier CIs), the MS / MI may be calculated in the usual way, and the task may be executed based on that MS / MI. For a “severely abnormal” sliding time window (or, for example, a severely unreliable / irregular time window with a lot of abnormal / outlier CIs), the MS / MI for the current window is not calculated, and the task may be executed in an alternative way without the MS / MI for the current time window (for example, using a substitute MS / MI calculated from adjacent MS / MIs). For a "moderately abnormal" sliding time window (or, for example, a moderately reliable / irregular time window with an acceptable amount of good / normal / reliable CIs in a specific sequence (e.g., at least N1 consecutive ones) at a specific position within the sliding time window (e.g., at the beginning / end / middle)), an alternative MS / MI (e.g., a simplified / reduced / alternative MS / MI) may be calculated based on some good / normal / reliable CIs within the sliding time window, so that a task can be performed based on the alternative MS / MI (which can replace the MS / MI). The alternative MS / MI may be an MS / MI calculated without abnormal CIs (i.e., with abnormal CIs removed or excluded). Not all good / normal / reliable CIs may be included in the calculation of the alternative MS / MI.

[0127] In some embodiments, a test score (TS) may be calculated. A characteristic value may be calculated for the current sliding time window having N timestamps. The system may calculate at least one test score (TS) based on M temporally adjacent CIs in the sliding time window of the TSCI, where M can be 2, 3, or more. Some or all of the M temporally adjacent CIs may be consecutive. Each TS may be a scalar. Each TS may be associated with a separate time. The test score may include / may include any of the following, or may include / may include any measure or score of similarity, dissimilarity, difference, distance, norm, distinction, ratio, proportion, variance, variation, divergence, spread, deviation, TRRS, correlation, covariance, autocorrelation, cross-correlation, inner product, etc. In some embodiments, each TSCI is associated with a “link”, which is a pairing of a Tx antenna of a type 1 device and an Rx antenna of a type 2 device. For a pair of Type 1 / Type 2 devices, one or more links may exist along with two or more associated TSCIs.

[0128] In some embodiments, a component-wise test score (CTS) may be calculated. Each CI may have L components (e.g., L subcarriers if CI=CFR, L tabs if CI=CIR). The CTS may be calculated for each component. The CTS may be a scalar. Each CTS may be associated with a separate time. All CTS(t) for any given time t may be calculated based on M temporally adjacent CIs (e.g., the same as / identical to those used for the TS) in the sliding time window of the test, where M may be 2, 3, or more. The M temporally adjacent CIs used to calculate all L CTS(t) at the same time t may be the same / different for all components. CTS may include / may include any of the following: similarity, dissimilarity, difference, distance, norm, distinction, ratio, proportion, variance, variation, divergence, spread, deviation, component-wise TRRS, correlation, covariance, autocorrelation, cross-correlation, inner product, etc., or may include / may include any of these measures or scores. In some embodiments, TS(t) may be an aggregate of L CTS(t) (e.g., sum, mean, weighted mean, median, mode, maximum, minimum, percentile), or an aggregate of weighted quantities of each CTS(t), or an aggregate of functions of each CTS(t) (e.g., magnitude, phase, magnitude squared).

[0129] In some embodiments, several components / associated CTS may be “selected” (e.g., K of the largest (magnitude) CTS, or based on the magnitude of a CTS > a threshold, or on the function of individual CTS related to the function of other CTS), and TS(t) may be the sum of only the selected CTS / quantities (e.g., function, magnitude, phase, magnitude squared) for the selected CTS. If TS is a weighted quantity of CTS (e.g., weighted sum, weighted average, weighted product), the CTS of the selected components may have a greater weight than the CTS of the unselected components. The weights may be calculated per component (e.g., based on the component’s CTS relative to the CTS of other components). TS may be a weighted quantity of CTS, with each CTS weighted by its individual weight.

[0130] In some embodiments, the link-wise test score (LTS) may be calculated based on the CI within a sliding time window of the TSCI. For example, the LTS may be the first combined value of two or more TSs. The LTS may also be an "overall" test score (i.e., M=N) based on all CIs included in the TSCI within the sliding time window. For a given link, the sliding time window may be classified as "normal," "moderately abnormal," or "severely abnormal" based on the LTS or two or more TSs associated with the TSCI. The link-wise MS / MI (or alternative link-wise MS / MI) may / may not be calculated based on the CI within a sliding time window of the TSCI. Any aggregate value may include any of the following: mean, weighted mean, trimmed mean, sum, weighted sum, product, weighted product, arithmetic mean, geometric mean, harmonic mean, median, weighted median, mode, histogram, statistics, autocorrelation function, spectrum, spectrogram, variance, variation, divergence, spread, range, deviation, minimum, maximum, percentile, characteristic value, etc.

[0131] In some embodiments, the device pairwise test score (TTS) may be calculated over a sliding time window based on two or more TSCIs associated with a pair of Type 1 and Type 2 devices (each TSCI is associated with a “link” associated with the TX antenna of the Type 1 device and the RX antenna of the Type 2 device). For example, the TTS may be a second sum of two or more associated LTSs, each LTS associated with a separate link. The TTS may also be an “overall” test score based on all CIs included in all two or more TSCIs within the sliding time window. For a Type 1-Type 2 device pair, the sliding time window may be classified as “normal” or “severely abnormal” based on the TTS, two or more LTSs, or two or more TSs (each TS associated with a separate link). The device pairwise MS / MI (or alternative device pairwise) may / may not be calculated based on the CIs within the sliding time window of the TSCIs.

[0132] In some examples, outlier detection / suppression / removal is performed on the TSCI. TS(t), a test score (TS) associated with time t, can be a difference score such as the CI difference (CID: CI-difference) between two or more temporally adjacent CIs. In a time window with N CIs within each TSCI, the system may calculate (N-1) CIDs, where each CID associated with time t is CID1(t) = CI(t) - CI(t-1). For each CID, a CID feature or f(CID(t)) (CID feature or CIDF, where feature f(.) includes magnitude / squared magnitude / norm / absolute value / power or monotonic function)) may be calculated.

[0133] In some embodiments, CID may be CID2(t) = f(CI(t)) - f(CI(t-1)). In such cases, CIDF may be CIDF2(t) = f2(CID2(t)), where f and f2 can be different. In some embodiments, CID may be CID3(t) = CI(t) - CI(tk) for some k = 1, -1, 2, -2, 3, -3, ... In some embodiments, CID may also be CID = (CID1 + CID3) / 2 = CI(t)(CI(t-1) + CI(tk)) / 2. When k = -1, CID = CI(t) - (CI(t-1) + CI(t+1)) / 2. When k = 2, CID = CI(t) - (CI(t-1) + CI(t-2)) / 2. In some embodiments, CID can also be a linear combination of CID3 with individual k, or a linear combination of any CID1, CID2, and CID3. For example, CID can be CID=(CID1+CID1+CID3) / 3=CI(t)(2*CI(t-1)+CI(tk)) / 3. In some embodiments, CID can also be CI(t)-CI(t)-(CI(t-1)+CI(t-2)+CI(t-3)) / 3, or even CI(t) obtained by subtracting a weighted average of several adjacent CIs, such as CID=CI(t)-(4*CI(t-1)+2*CI(t-2)+CI(t-3)) / 7.

[0134] In some embodiments, TS can be any CID, any CIDF, or any combination. A large TS may suggest / indicate a rapid change, anomaly, or a high probability / likelihood of anomaly. LTS can be the sum of N-1 CIDs (e.g., arithmetic / geometric / harmonic / trim / weighted mean or sum, median / mode / maximum / percentile / minimum, variance / volatility / divergence / spread / deviation, etc.). The arithmetic mean, geometric mean, median, or mode may reflect "typical" behavior. The maximum or minimum or percentile (e.g., 95% or 5%) may reflect worst / best-case behavior. Variance / volatility / divergence may reflect fluctuation behavior.

[0135] To perform a classification of CI-level abnormalities, for a CI at time t, if the associated TS(t) satisfies a first condition (e.g., is greater than or less than a threshold), it may be classified / calculated / determined as “abnormal” / outlier / atypical / unusual / unusual / unrepresentative / error / anomalous / strange / irregular / deviation / divergence / eccentric / exceptional / singular / reverse / perverse / broken. Otherwise, it may be classified as “normal”. In some embodiments, TS(t) may be a score / measure of local dissimilarity, difference (e.g., CID / CIDF), distance, distinction, ratio, proportion, variance, variation, divergence, spread, or deviation within a window around time t, and the first condition may be that TS(t) is greater than a threshold. TS(t) can be a score / measure of local similarity, TRRS, correlation, covariance, autocorrelation, cross-correlation, or inner product within a window around time t, and the first condition is that TS(t) is less than a threshold. For "abnormal" CIs, an "abnormality score" / AS (e.g., a real number between 0 and 1, or between -1 and +1) can be calculated (e.g., based on TS, or on all / selected, associated CTS, or on a number of adjacent / temporarily adjacent TS or associated CTS). For consecutive runs of abnormal CIs, the run-wise abnormality score / RAS can be calculated as the sum of the AS of each abnormal CI in that run (sum, weighted sum, product, weighted product, mean, weighted mean / median / mode, (weighted) arithmetic / geometric / harmonic mean).

[0136] To perform the classification of link-level anomalies, for the sliding time window of the CI of a link, e.g., the k-th link (or link k), if the second condition is satisfied (e.g., when LTS(k) is greater than or less than a threshold, or when the maximum / minimum / majority / minority / percentage / sufficient amount / minimum amount / maximum amount of TS(t) in the sliding time window of link k is greater than or less than another threshold, or when the first condition is satisfied, or when the distribution characteristics of abnormal CIs in link k satisfy a certain condition), it can be determined / classified / operated as "abnormal" (e.g., "moderately abnormal" or "severely abnormal"). Otherwise, the sliding time window of link k can be classified as "normal". If it is "abnormal", when some additional condition (e.g., for certain T1, T2, T1 < AS < T2) is satisfied, it can be further classified as "moderately abnormal" or "severely abnormal" (or an abnormal level based on AS). There may be two or more classes or subclasses of "moderately abnormal".

[0137] In some embodiments, if the percentage / amount of normal CIs in the sliding time window of link k (or in the initial part of the time window, or in the ending part of the time window) exceeds a threshold, or if the initial (e.g., the first) run of normal CIs has a run length exceeding the threshold, or if the ending (e.g., the last) run of normal CIs has a run length exceeding the threshold, or if some other condition is satisfied, it can be classified as "moderately abnormal". Otherwise, it can be "severely abnormal". Any threshold can depend on the anomaly score / AS / run-wise AS / RAS. Any threshold can be predetermined or calculated adaptively based on the related AS / RAS. In some embodiments, if the maximum value of TS(t) in the sliding time window of link k is greater than a threshold, the sliding time window of link k can be classified as "abnormal".

[0138] In some embodiments, runs of consecutive "normal" CIs and their individual "run lengths" can be determined / operated / identified. For example, an isolated normal CI is a run of 1 and has a run length of 1. Two consecutive normal CIs are a run of 2 and have a run length of 2, and so on. The sliding time window of link k can be further classified as "moderately abnormal" if the run length of any run of normal CIs meets a condition (e.g., greater than 1 / 2, or 1 / 3, or 1 / 4 of the length of the sliding time window). Otherwise, it can be further classified as "severely abnormal".

[0139] In some embodiments, runs of consecutive "normal CIs" can further include (consecutive / connected) "abnormal" CIs having an AS smaller than a threshold. In some embodiments, runs of consecutive abnormal CIs having an AS smaller than a threshold can be reclassified as "normal CIs" if their individual run lengths are greater than another threshold. Alternatively, runs of consecutive abnormal CIs where all ASs are smaller than T1 and the percentage of ASs is T2 < T1 can be reclassified as "normal CIs" if their individual run lengths are greater than T3. In some embodiments, the amount of TS(t) greater than a first threshold can be calculated. The sliding time window of link k can be further classified as "moderately abnormal" if the percentage of TS(t) greater than the first threshold (e.g., the percentage of the maximum value of TS(t) in the sliding time window of link k) is smaller than a second threshold, or alternatively, if a certain percentile (e.g., 90th percentile) of TS(t) in the sliding time window of link k is smaller than a certain threshold.

[0140] In some embodiments, arbitrary thresholds may be predetermined or adaptively calculated based on the relevant anomaly score / AS / Runwise AS / RAS. In some embodiments, MS / MI may be calculated differently according to the classifications "normal," "abnormal," "moderately abnormal," and / or "severely abnormal." In some embodiments, a second condition is met (e.g., LTS(k) is greater than or less than a threshold, or the majority / minority / percentage / sufficient / minimum / maximum of TS(t) in the sliding time window of link k is greater than or less than another threshold, or the first condition is met, or the distribution characteristics of the abnormal CI in link k satisfy a certain condition). In some embodiments, LTS(k) may be a score / measure of the window-wide total, maximum, minimum, percentile (e.g., 95% or 5%), dissimilarity, difference, distance, distinction, ratio, proportion, variance, variation, divergence, spread, or deviation of TS(t) in the sliding time window of link k.

[0141] To perform device pair-level anomaly classification, for a pair of Type 1 and Type 2 devices (i.e., a device pair) that includes M antenna pairings (M=M1*M2, such that a Type 1 device has M1 antennas and a Type 2 device has M2 antennas), and therefore includes M links and M associated TSCIs, the sliding time window of the device pair may be determined / classified / calculated as "abnormal" if a third condition is met (TTS is greater than or less than a threshold, or the majority / minority / percentage / sufficient / minimum / maximum amount of LTS is greater than / less than another threshold or satisfies the second condition, or the majority / minority / percentage / sufficient / minimum / maximum amount of TS within the slicing time window is greater than / less than yet another threshold or satisfies the first threshold, or the distribution characteristics of the abnormal link satisfy a certain condition, or the distribution characteristics of the abnormal CI satisfy another condition). Otherwise, the sliding time window of the device pair may be classified as "normal".

[0142] In some embodiments, if the current time window is "normal", the MS / MI may be calculated for the current sliding time window based on all CIs in link k (or two or more links) within the time window. This may be the normal way of calculating the MS / MI. If the current time window is "abnormal", especially "severely abnormal", the MS / MI may / may not be calculated based on CIs in the current time window. If it has already been calculated, the MS / MI may be discarded (or not used). Alternative / replacement / another / auxiliary / fallback / standby / fill-in / stand-in / proxy MS / MIs may be used. Alternative MS / MIs may be predicted / estimated / replacement MS / MI values ​​calculated based on several adjacent MS / MIs (e.g., spatially adjacent, such as temporally adjacent, simultaneous, past or future, or other TSCIs of the same TX / RX device pair, or TSCIs of "adjacent" TX / RX device pairs). Alternative MS / MI can be a composite value such as mean / median / mode / weighted mean / trimmed mean, or zero-order / first-order / second-order / higher-order predictor / estimator, or another composite value of multiple adjacent MS / MIs. If the current time window is "moderately anomalous," reduced / partial / simplified / limited / slope / biased / partial / distorted / colored / one-sided MS / MI may be calculated based on some (or all) of the remaining / available "normal" CIs within the current time window. For example, reduced MS / MI may be calculated based on one or more long / longest runs of normal CIs (each having a sufficiently long run length, e.g., run length > T1), or based on runs of the first or last normal CIs within the time window (with a sufficient run length, e.g., run length > T2).

[0143] In some embodiments, if there are two or more runs of normal CI with sufficient length within the current time window, multiple provisional reduced MS / MI values ​​may be calculated based on each individual run of normal CI, and the reduced MS / MI may be calculated as a sum of the multiple provisional reduced MS / MI values. The start or end run of normal CI in the current time window may be combined with a concatenated run of normal CI in a temporally adjacent time window in order to calculate the reduced MS / MI. In particular, the start (or end) run of normal CI may be combined with the end (or start) run of normal CI in a previous (or next) adjacent time window, and the reduced MS / MI may be calculated based on the combined run of normal CI.

[0144] In some embodiments, MS / MI may be calculated as a reduced MS / MI. Alternatively, MS / MI may be calculated as the sum of the reduced MS / MI and one or more adjacent MS / MIs (for example, when the longest run-length is less than T1 but greater than T3). Any threshold may be predetermined, or adjusted or adaptively adjusted.

[0145] Figure 1 shows an exemplary block diagram of a first wireless device (e.g., Bot 100) of a wireless sensing or monitoring system according to one embodiment of this teaching. Bot 100 is an example of a device that can be configured to implement the various methods described herein. As shown in Figure 1, Bot 100 comprises a housing 140 including a processor 102, memory 104, a transceiver 110 with a transmitter 112 and a receiver 114, a synchronization controller 106, a power module 108, an optional carrier configurator 120, and a wireless signal generator 122.

[0146] In this embodiment, the processor 102 controls the overall operation of the bot 100 and may include one or more processing circuits or modules, such as a central processing unit (CPU) and / or a general-purpose microprocessor, microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), programmable logic device (PLD), controller, state machine, gate logic, individual hardware components, dedicated hardware finite state machine, or any other suitable circuit, device and / or structure capable of performing data calculations or other operations.

[0147] Memory 104, which may include both read-only memory (ROM) and random access memory (RAM), can provide instructions and data to the processor 102. Part of memory 104 may also include non-volatile random access memory (NVRAM). The processor 102 generally performs logical and arithmetic operations based on program instructions stored in memory 104. Instructions stored in memory 104 (also known as software) can be executed by the processor 102 to perform the methods described herein. Together, the processor 102 and memory 104 form a processing system for storing and executing software. As used herein, “software” means any type of instruction, whether called software, firmware, middleware, microcode, etc., that can configure a machine or device to perform one or more desired functions or processes. Instructions may include code (e.g., source code, binary code, executable code, or any other suitable code format). When executed by one or more processors, instructions cause the processing system to perform the various functions described herein.

[0148] A transceiver 110, including a transmitter 112 and a receiver 114, enables the bot 100 to send and receive data to and from a remote device (e.g., Origin or another bot). An antenna 150 is typically mounted on a housing 140 and electrically coupled to the transceiver 110. In various embodiments, the bot 100 comprises multiple transmitters, multiple receivers, and multiple transceivers (not shown). In one embodiment, the antenna 150 is replaced by a multi-antenna array 150 capable of forming multiple beams, each pointing in a distinct direction. The transmitter 112 may be configured to wirelessly transmit signals of different types or functions, such signals being generated by a processor 102. Similarly, the receiver 114 is configured to receive wireless signals of different types or functions, and the processor 102 is configured to process multiple different types of signals.

[0149] In this example, Bot 100 may function as a Bot, a Type 1 device, a Transmitter, or an STA in the system disclosed herein. For example, a radio signal generator 122 may generate a radio signal via transmitter 112 and transmit it through a radio multipath channel that is affected by the motion of objects in the venue. The radio signal carries channel information. Because the channel is affected by motion, the channel information includes motion information that can represent the motion of objects. Thus, motion can be indicated and detected based on the radio signal. The generation of the radio signal in the radio signal generator 122 may be based on a request for motion detection from another device (e.g., an origin) or on a pre-configuration of the system. That is, Bot 100 may or may not know that the transmitted radio signal is to be used for motion detection.

[0150] In this example, the synchronization controller 106 may be configured to control the operation of the bot 100 to be synchronous or asynchronous with another device (e.g., an origin or another bot). In one embodiment, the synchronization controller 106 may control the bot 100 to be synchronous with an origin that receives radio signals transmitted by the bot 100. In another embodiment, the synchronization controller 106 may control the bot 100 to transmit radio signals asynchronously with other bots. In yet another embodiment, each of the bot 100 and the other bots may transmit radio signals individually and asynchronously.

[0151] The carrier configurator 120 is an optional component within the bot 100 for configuring transmission resources (e.g., time and carrier) for transmitting the radio signal generated by the radio signal generator 122. In one embodiment, each CI included in the CI time series has one or more components corresponding to the carrier or subcarrier of the radio signal transmission. Motion detection may be based on motion detection of any one or any combination of components.

[0152] The power module 108 may include one or more power sources such as batteries and a power regulator for providing regulated power to each of the modules described above in Figure 1. In some embodiments, when the bot 100 is coupled to a dedicated external power source (e.g., a wall outlet), the power module 108 may include a transformer and a power regulator.

[0153] The various modules described above are coupled to one another by a bus system 130. The bus system 130 may include a data bus and, in addition to the data bus, a power bus, a control signal bus, and / or a status signal bus. It is understood that the modules of Bot 100 can be coupled to one another operably using any suitable technology and medium.

[0154] Although several separate modules or components are shown in Figure 1, it will be understood by those skilled in the art that one or more of these modules may be combined or generally implemented. For example, the processor 102 may implement not only the functions described above with respect to the processor 102, but also the functions described above with respect to the wireless signal generator 122. Conversely, each of the modules shown in Figure 1 may be implemented using multiple separate components or elements.

[0155] Figure 2 shows an exemplary block diagram of a second wireless device (e.g., origin 200) of a wireless sensing or monitoring system according to one embodiment of this teaching. Origin 200 is an example of a device that can be configured to implement various methods described herein. In this example, origin 200 can function as an origin, receiver, type 2 device, or AP in the systems disclosed herein. As shown in Figure 2, origin 200 comprises a housing 240 including a processor 202, memory 204, a transceiver 210 with a transmitter 212 and a receiver 214, a power module 208, a synchronization controller 206, a channel information extractor 220, and an optional motion detector 222.

[0156] In this embodiment, the processor 202, memory 204, transceiver 210, and power module 208 operate similarly to the processor 102, memory 104, transceiver 110, and power module 108 in bot 100. The antenna 250 or multi-antenna array 250 is typically mounted on the housing 240 and electrically coupled to the transceiver 210.

[0157] Origin 200 may be a second wireless device having a different type from the first wireless device (e.g., Bot 100). In particular, the channel information extractor 220 in Origin 200 is configured to receive wireless signals through wireless multipath channels affected by the motion of objects in the venue, and to obtain time-series channel information (CI) of the wireless multipath channels based on said wireless signals. The channel information extractor 220 may transmit the extracted CI to an optional motion detector 222, or to an external motion detector to detect the motion of objects in the venue.

[0158] The motion detector 222 is an optional component in the origin 200. In one embodiment, it is located within the origin 200, as shown in Figure 2. In another embodiment, it is outside the origin 200 and located in another device, which may be a bot, another origin, a cloud server, a fog server, a local server, and an edge server. The optional motion detector 222 may be configured to detect the motion of an object in a venue based on motion information related to the motion of the object. The motion information associated with the first and second wireless devices is calculated based on a time-series CI by the motion detector 222 or another motion detector outside the origin 200.

[0159] In this example, the synchronization controller 206 may be configured to control the operation of the origin 200 to be synchronized or asynchronous with another device, such as a bot, another origin, or an independent motion detector. In one embodiment, the synchronization controller 206 may control the origin 200 to be synchronized with a bot transmitting a radio signal. In another embodiment, the synchronization controller 206 may control the origin 200 to receive a radio signal asynchronously with another origin. In another embodiment, each of the origin 200 and the other origin may receive radio signals individually and asynchronously. In one embodiment, an optional motion detector 222 or an external motion detector of the origin 200 is configured to asynchronously calculate individual heterogeneous motion information related to the motion of an object based on individual time-series CIs.

[0160] The various modules described above are coupled to one another by a bus system 230. The bus system 230 may include a data bus and, in addition to the data bus, a power bus, a control signal bus, and / or a status signal bus. It is understood that the modules of origin 200 can be operably coupled to one another using any suitable technology and medium.

[0161] Although several separate modules or components are shown in Figure 2, it will be understood by those skilled in the art that one or more of these modules may be combined or generally implemented. For example, the processor 202 may implement not only the functions described above with respect to the processor 202, but also the functions described above with respect to the channel information extractor 220. Conversely, each of the modules shown in Figure 2 may be implemented using multiple separate components or elements.

[0162] In one embodiment, in addition to bot 100 and origin 200, the system may further include auxiliary devices, a third radio device (e.g., another bot) configured to transmit additional heterogeneous radio signals through additional radio multipath channels affected by the motion of objects in the venue, or a fourth radio device (e.g., another origin) having a different type from the third radio device. The fourth radio device may be configured to receive additional heterogeneous radio signals through additional radio multipath channels affected by the motion of objects in the venue, and to acquire additional time-series channel information (CI) of the additional radio multipath channels based on the additional heterogeneous radio signals. The additional CI of the additional radio multipath channels is associated with a different protocol or configuration than the CI of the radio multipath channels. For example, the radio multipath channels are associated with LTE, and the additional radio multipath channels are associated with Wi-Fi. In this case, the optional motion detector 222 or an external motion detector of the origin 200 is configured to detect the motion of objects within the venue based on both motion information associated with the first and second wireless devices and additional motion information associated with the third and fourth wireless devices, calculated by at least one of the additional motion detectors and the fourth wireless device based on an additional time-series CI.

[0163] Figure 3 shows a flowchart of an exemplary method 300 for precise wireless monitoring according to several embodiments of the present disclosure. In various embodiments, method 300 may be performed by the system disclosed above. In operation 302, a radio signal is transmitted from the first radio device through the venue's radio multipath channel, which is affected by the motion of objects within the venue. In operation 304, the radio signal is received by the second radio device through the radio multipath channel, and the received radio signal differs from the transmitted radio signal due to the radio multipath channel and the motion of objects. In operation 306, time series of channel information (TSCI) of the radio multipath channel is obtained based on the received radio signal. In operation 308, the classification of the sliding time window is performed by analyzing the channel information (CI) contained in the TSCI within the sliding time window. In operation 310, motion information (MI) for the sliding time window is calculated based on the TSCI and the classification of the sliding time window. In operation 312, the motion of objects is monitored based on the MI.

[0164] Figure 4 shows a flowchart of an exemplary method 400 for performing sliding time window classification according to several embodiments of the present disclosure. In various embodiments, method 400 may be performed by a system such as those disclosed above. In operation 402, the system may classify the current sliding time window into at least three classes and calculate the MI / MS / STI of the current sliding time window in different ways based on at least three classes. In operation 404, if the current sliding time window is window class 1 (e.g., "NORMAL"), the system may calculate the MI / MS / STI in a first way based only on the CIs included in the TSCI of the current sliding time window. In operation 406, if the current sliding time window is window class 2 (e.g., "SEVERELY ABNORMAL"), the system calculates the MI / MS / STI in a second way based on at least one CI included in the TSCI outside the current sliding time window. In operation 408, if the current sliding time window is window class 3 (e.g., "Moderately Abnormal"), the system may calculate MI / MS / STI in a third way based on the first subset of CIs contained in the TSCI within the current sliding time window, without using a second subset of CIs contained in the TSCI within the sliding time window, where the first and second subsets are relatively prime.

[0165] Figure 5 shows a flowchart of an exemplary method 500 for calculating test scores (TS) and performing item-wise classification of each CI, according to several embodiments of the present disclosure. In various embodiments, method 500 may be performed by a system such as those disclosed above. In operation 510, the system may calculate a test score (TS) for each CI included in the TSCI within the sliding time window, based on one of sub-operations 512 or 514. In suboperation 512, the system may calculate the TS for CS based on temporally adjacent CIs. In suboperation 514, the system may calculate a component test score (CTS) for each component of CI, and then calculate the TS for CI as the sum of the CTS for CI. In some embodiments, the TS includes at least one of the following: test quantity, similarity / dissimilarity / match / mismatch score, distance score, TRRS, distance score / correlation / cross-correlation, dot product, norm, sum, difference, absolute / squared difference, variance / variability / variability, deviation / standard deviation, spread, variance, divergence, skewness, range, kurtosis, interquartile range, Gini coefficient, entropy, mean, median, mode, maximum, minimum, percentile, quartile, maximum-to-minimum ratio, variance-to-mean ratio, regularity, irregularity, statistics, histogram The difference between a linear combination of CI(t) and CI(t+k) for k=+-1 / 2 / 3 / ..., the difference between a linear combination of F(CI(t)) and F(CI(t+k)), the dot product of a linear combination of CI(t) and CI(t+k) for k=+-1 / 2 / 3 / ..., the dot product of a linear combination of F(CI(t)) and F(CI(t+k)), the dissimilarity score between a linear combination of CI(t) and CI(t+k) for k=+-1 / 2 / 3 / ..., or the dissimilarity between a linear combination of F(CI(t)) and F(CI(t+k)). In operation 520, the system may perform item-wise classification of each CI contained within the TSCI within a sliding time window, based on individual TS.

[0166] Figure 6 shows a flowchart of an exemplary method 600 for performing sliding time window classification according to several embodiments of the present disclosure. In various embodiments, method 600 may be performed by a system such as those disclosed above. In operation 610, the system may calculate a Linkwise Test Score (LTS) based on multiple TSs for CIs included in the TSCI within a sliding time window. In some embodiments, the LTS is a sum of the multiple TSs. The sum may include at least one of the following: sum, weighted sum, mean, weighted mean, geometric mean, weighted geometric mean, harmonic mean, weighted harmonic mean, arithmetic mean, weighted mean, trimmed mean, median, weighted median, mode, histogram, statistic, percentile, maximum, minimum, variance, variation, divergence, spread, range, deviation, or characteristic value. In operation 620, including sub-operations 622 and 624, the system may perform a classification of sliding time windows based on LTS. In suboperation 622, the system may classify the sliding time window as window class 1 if LTS is greater than T1. In suboperation 624, the system may classify the sliding time window as a class that includes window class 1 and window class 2 if LTS is less than T2.

[0167] Figure 7 shows a flowchart of an exemplary method 700 for performing sliding time window classification according to several embodiments of the present disclosure. In various embodiments, method 700 may be performed by a system such as those disclosed above. In operation 710, the system may perform item-wise classification on each CI included in the TSCI within the sliding time window. Each CI is classified as CI-class 1 (e.g., "normal CI") if its respective TS is less than T3, and as CI-class 2 ("abnormal CI") if its TS is greater than T4. In operation 720, the system may classify a sliding time window as window class 1 if all CIs within the sliding time window are classified as CI class 1 (first class CI). In operation 730, the system may classify a sliding time window as window class 2 if all CIs within the sliding time window are classified as CI class 2 (second class CIs). In operation 740, if at least one CI is classified as CI-Class 1 and at least one is classified as CI-Class 2, the system may identify at least one run of the first-class CI and at least one run of the second-class CI within the sliding time window, and classify the sliding time window based on the runs of the first-class and second-class CIs and their individual run lengths. Operation 740 includes sub-operations 742, 744, and 746. In suboperation 742, the system may search for and select a preferred run of a first class CI based on the run length of each run of the first class CI and a plurality of TS associated with that run. In some embodiments, the N1 run of the first class CI having the longest run length among all runs is selected. For example, if N1=1, the run of the first class CI having the longest run length among all runs is selected. In some embodiments, any run of the first class CI with a run length greater than T5 is selected. In some embodiments, the first run of the first class CI with a run length greater than T6 is selected. In some embodiments, the first run of the first class CI with a run length greater than T6 is selected. In some embodiments, the count of preferred runs of the first class CI is less than or equal to N1. In some embodiments, a run of the first class CI is selected if all associated TSs satisfy the condition. In some embodiments, a run of the first class CI is selected if all associated TSs are less than a threshold. In suboperation 744, if at least one selective run of the first class CI is selected, the system may classify the sliding time window as window class 3 and calculate MI / MS / STI based on at least one selective run of the first class CI. In suboperation 746, if no selective run of the first class CI is selected, the system may classify the sliding time window as window class 2.

[0168] Figure 8 shows a flowchart of an exemplary method 800 for calculating MI / MS / STI according to several embodiments of the present disclosure. In various embodiments, method 800 may be performed by a system such as those disclosed above. In operation 810, the system may calculate at least one provisional MI / MS / STI for a sliding time window. Each provisional MI / MS / STI is based on a separate selection run of first class CIs contained in the TSCI within the sliding time window. The operation may include sub-operations 812, 814. In suboperation 812, the system may calculate a specific provisional MI / MS / STI based on a composite run of CIs included in TSCI, which is formed by combining the first (selected) run in the sliding time window with the last run in the previous sliding time window. In suboperation 814, the system may calculate a specific provisional MI / MS / STI based on a composite run of CIs included in TSCI, which is formed by combining the last (selected) run in the sliding time window with the first run in the next sliding time window. In operation 820, the system may calculate MI / MS / STI by at least one of sub-operations 822 and 824. In suboperation 822, the system may calculate MI / MS / STI as a weighted sum of all provisional MI / MS / STI. Each provisional MI / MS / STI is weighted by individual weights calculated based on the run-length of individual selection runs. In suboperation 824, the system may calculate MI / MS / STI as a sum of all provisional MI and at least one adjacent MI. Each adjacent MI is associated with either a past or future adjacent sliding time window of a CI included in a TSCI, or an adjacent sliding time window of a CI included in another TSCI.

[0169] Figure 9 shows a flowchart of an exemplary method 900 for performing compensated wireless monitoring according to several embodiments of the present disclosure. In various embodiments, method 900 may be performed by one or more systems as disclosed above. In operation 910, a radio signal is transmitted from a Type 1 heterogeneous device to a Type 2 heterogeneous device via a radio channel that is affected by the motion of an object in the venue. In operation 920, the processor, memory, and instruction set are used to obtain time-series channel information (TSCI) of the radio channel based on the received radio signal. In operation 930, the system may calculate time series of motion information (TSMI) based on TSCI, and calculate time series analysis values ​​(TSA) based on TSMI. In operation 940, the system may calculate the compensated time series analysis values ​​by applying compensation to the TSA calculation, where the compensation includes monotonic mapping. In one embodiment, the system may, in operation 950, modify the compensation / monotonic mapping based on changes in target behavior, radio signal, bandwidth, frequency, specifications, settings, user input, circumstances, events, time table, strategy, and plan, apply the modified compensation, and then, in operation 960, monitor the motion of the object based on the compensated time-series analysis values. In another embodiment, after performing operation 940, the system may, in operation 960, directly monitor the motion of the object based on the compensated time-series analysis values.

[0170] Figure 10 shows a flowchart of an exemplary method 1000 for calculating compensated time-series analysis values ​​according to several embodiments of the present disclosure. In various embodiments, method 1000 may be performed by a system such as those disclosed above. In operation 1010, the system may apply compensation to the calculation of TSA. Such compensation includes monotonic mapping. In some embodiments, monotonic mapping includes at least one of convex / concave / univariate / bivariate / multivariate mapping, linear / nonlinear / affine / piecewise linear mapping, monotonic non-decreasing / non-increasing / decreasing mapping, quadratic / cubic / polynomial / exponential / logarithmic mapping, fitted / nonparametric / parametric / regressive / spline mapping, function / inverse function / function, mapping of mappings / composite mapping, and time-varying / time-invariant mapping. In some embodiments, the compensation / monotonic mapping includes at least one compensation tailor made for (1) a pair of general-purpose type 1 and type 2 devices, (2) a type 1 device, (3) a type 2 device, or (4) a pair of type 1 and type 2 devices. In some embodiments, compensation / monotonic mapping may be applied to at least one of the following: CI / IV / MI / MS / STI / analytical values, features / magnitude / phase / components of CI / IV / MI / MS / STI / analytical values, or TSCI / TSMI / TSA. In operation 1022, the system may calculate the compensated CI based on the compensation / monotonic mapping applied to the CI. In operation 1024, the system may calculate the compensated MI based on the CI / IV, the compensated CI / IV, the compensated features / magnitude / phase / components of the CI / IV, or the compensation / monotonic mapping applied to the MI. In operation 1026, the system may calculate a compensated analytical value based on CI / IV / MI / MS / STI, compensated CI / IV / MI / MS / STI, compensated features / phases / magnitudes / components of CI / IV / MI / MS / STI, or compensation / monotonic mapping applied to the analytical value.

[0171] Figure 11 shows a flowchart of an exemplary method 1100 for calculating a monotonic mapping according to several embodiments of the present disclosure. In various embodiments, method 1100 may be performed by a system such as those disclosed above. In operation 1110, during the calibration phase, a calibration radio signal is transmitted from a calibration type 1 device to a calibration type 2 device via a calibration radio channel that is affected by the calibration operation of the calibration object at the calibration venue. In operation 1120, the time-series calibration CI of the calibration radio channel is obtained based on the received calibration radio signal using the calibration processor / memory / instructions. In operation 1130, the system may calculate the time-series calibration MI / MS / STI based on the time-series calibration CI, and calculate the time-series calibration analysis value based on the time-series calibration MI / MS / STI. In operation 1140, the system may compare the behavior associated with the time-series calibration CI / MI / MS / STI / analysis values ​​with the target behavior. In some embodiments, the behavior includes feature / magnitude / phase / component behavior, statistical / time / frequency / projection / transformation behavior, univariate / binvariate / multivariate / conditional / cumulative / restricted distribution / histogram, CI / IV / MI / MS / STI / analysis values ​​or related observable behavior, period, statistics, mean, median, percentile, maximum, minimum, range, variance, divergence, variability, kurtosis, information, entropy, moment, correlation, covariance, behavior restricted to ACF, etc. In operation 1150, the system may calculate a monotonic mapping based on the above comparison. This operation may include sub-operation 1152. In this sub-operation, the system may calculate a monotonic mapping based on (1) search / inference, (2) specifications / settings / user input / another device, (3) semi-supervised / unsupervised / unsupervised learning, or (4) AI / deep learning / machine learning / neural network.

[0172] Figure 12 shows a flowchart of an exemplary method 1200 for determining a monotonic mapping according to several embodiments of the present disclosure. In various embodiments, method 1200 may be performed by a system such as those disclosed above. In operation 1210, during the calibration phase, the system may determine and search for several candidate monotonic mappings. In operation 1220, for each candidate monotonic mapping, the system may (a) apply a separate compensation, including the candidate monotonic mapping, to the TSA calculation, and (b) after the separate compensation has been applied, calculate a separate similarity score between the target behavior and the resulting behavior associated with the calibration CI / MI / MS / STI / analysis value. In operation 1230, the system may compute a monotonic mapping based on (1) search / inference, (2) specifications / settings / user input / another device, (3) semi-supervised / unsupervised / unsupervised learning, or (4) AI / deep learning / machine learning / neural network. In operation 1240, the system may select a monotonic mapping as the candidate monotonic mapping with the highest similarity score.

[0173] Figure 13 shows a flowchart of an exemplary method 1300 for selecting a monotonic mapping according to several embodiments of the present disclosure. In various embodiments, method 1300 may be performed by a system such as those disclosed above. In operation 1310, during the calibration phase, the system may determine the behavior of the univariate observable X associated with the CI / MI / MS / STI / analysis values ​​based on the calibration CI / MI / MS / STI / analysis values. Here, the behavior includes N scalar values ​​of X, {X_1, X_2, ..., X_N}, where X_1 <X_2<..<X_Nである。 In operation 1320, the system can determine the target behavior of the univariate target observable Y. Here, the target behavior includes N scalar values ​​of Y{Y_1, Y_2, ..., Y_N}, where Y_1 <Y_2<..<Y_Nである。 In operation 1330, the system can define N control points of a monotonic mapping by mapping N scalar values ​​of X to N scalar values ​​of Y. Here, the control points are (X_1, Y_1), (X_2, Y_2), ..., (X_N, Y_N). In operation 1340, a monotonic mapping can be selected based on N control points. This operation may include sub-operations 1342 and 1344. In suboperation 1342, the system may estimate the monotonic mapping as a monotonic line connecting N control points. In some embodiments, the monotonic line includes linear / affine / quadratic / cubic / polynomial / exponential / logarithmic / convex / concave / spline maps, piecewise linear / quadratic / cubic / polynomial maps, or monotonically increasing / non-decreasing maps. In suboperation 1344, the system may estimate the monotonic mapping as a curve that fits N control points according to a fitting criterion, for example, using linear / robust / orthogonal / Deming / major axis / segmented / polynomial regression of N control points.

[0174] Figure 14 shows a flowchart of an exemplary method 1400 for estimating a monotonic mapping according to several embodiments of the present disclosure. In various embodiments, method 1400 may be performed by a system such as those disclosed above. In operation 1410, during the calibration phase, the system may determine the behavior of the univariate observable X associated with the calibration CI / MI / MS / STI / analysis values, based on the calibration CI / MI / MS / STI / analysis values. Here, the behavior includes the cumulative univariate distribution F_X of X. In operation 1420, the system may determine the target behavior of the univariate target observable Y, where the target behavior includes the cumulative univariate target distribution F_Y of Y. In operation 1430, the system may estimate a monotonic mapping based on the cumulative univariate distributions F_X and F_Y (for example, based on one of the sub-operations 1432 or 1414). In suboperation 1432, the system may estimate the monotonic mapping as F_Y⁻¹[F_X(X)], where F_Y⁻¹ is the reciprocal of the function F_Y. In suboperation 1434, the system may estimate the monotonic mapping as an approximation of F_Y⁻¹[F_X(X)], where F_Y⁻¹ is the reciprocal of the function F_Y. In some embodiments, the approximation includes linear / affine / quadratic / cubic / polynomial / exponential / logarithmic / convex / concave / spline maps, piecewise linear / quadratic / cubic / polynomial maps, or monotonically increasing / non-decreasing maps.

[0175] Figure 15 shows a flowchart of an exemplary method 1500 for selecting a monotonic mapping according to several embodiments of the present disclosure. In various embodiments, method 1500 may be performed by a system such as those disclosed above. In operation 1510, during the calibration phase, the system may determine a monotonic mapping that includes a first and a second monotonic mapping. In operation 1520, the system may determine and search for several candidate first monotonic mappings. In operation 1530, for each candidate first monotonic mapping, the system may (a) estimate a separate second monotonic mapping, (b) apply separate compensations, including the candidate first monotonic mapping and the separately estimated second monotonic mapping, to the TSA calculation, and (c) after the separate compensations have been applied, calculate separate similarity scores between the target behavior and the resulting behavior associated with the calibration CI / MI / MS / STI / analysis values. In operation 1540, the system may select monotonic mappings as candidate first monotonic mapping and a separate estimated second monotonic mapping having the highest similarity score.

[0176] Figure 16 shows a flowchart of an exemplary method 1600 for calculating target behavior according to several embodiments of the present disclosure. In various embodiments, method 1600 may be performed by a system such as those disclosed above. In operation 1610, during the pre-calibration phase prior to the calibration phase, a reference radio signal is transmitted from a reference type 1 device to a reference type 2 device via a reference radio channel that is influenced by the reference motion of a reference object at a reference venue. In operation 1620, a reference processor / memory / instruction is used to obtain a time-series reference CI of the reference radio channel based on the received reference radio signal. In operation 1630, the system may calculate the time-series reference MI / MS / STI based on the time-series reference CI, and calculate the time-series reference analysis value based on the time-series reference MI / MS / STI. In operation 1640, the system may calculate the target behavior as at least one of the following behaviors: a time-series baseline CI, a time-series baseline MI, or a time-series baseline analysis value.

[0177] Figure 17 shows a flowchart of an exemplary method 1700 for updating a monotonic mapping according to several embodiments of the present disclosure. In various embodiments, method 1700 may be performed by a system such as those disclosed above. In operation 1710, during the recalibration phase following the calibration phase, the system may obtain a recalibrated TSCI. In operation 1720, the system may calculate the time series recalibration MI / MS / STI based on the time series recalibration CI, and calculate the time series recalibration analysis value based on the time series recalibration MI / MS / STI. In operation 1730, the system can compare the behavior of time-series recalibration CI / MI / MS / STI / analysis values ​​with the target behavior. In operation 1740, the system may calculate an updated monotonic mapping based on the above comparison. In operation 1750, the system may replace the monotonic mapping with an updated monotonic mapping in the compensation for the TSA calculation.

[0178] The sequence of operations in any one of the drawings of this specification may be modified according to various embodiments of this teaching.

[0179] Motion statistics (MS) or motion information (MI) may be calculated within a sliding time window to perform wireless sensing and monitoring, but one objective of this teaching is to provide precise wireless monitoring by detecting, suppressing, removing, and / or eliminating outliers or the effects of outliers within the sliding time window. In some embodiments, the sliding time window may be classified as “normal,” “moderately abnormal,” or “severely abnormal.” Depending on the classification, the MS / MI for the sliding time window may be calculated differently. In the case of “normal,” the MS / MI may be calculated in the usual way. In the case of “severely abnormal,” the MS / MI may be calculated based on adjacent CIs or MS / MIs rather than on CIs within the sliding time window. In the case of “moderately abnormal,” the MS / MI may be calculated based on good / reliable (or “normal”) CIs within the sliding time window. Poor / unreliable (or “abnormal,” or “severely abnormal”) CIs within the sliding time window are not used to calculate the MS / MI.

[0180] The following numbered sections provide implementation examples for wireless monitoring.

[0181] Item 1. A method for wireless monitoring, comprising: transmitting a wireless signal from a first wireless device through a wireless multipath channel of a venue, wherein the wireless multipath channel is affected by the motion of an object in the venue; receiving a wireless signal by a second wireless device through the wireless multipath channel, wherein the received wireless signal is different from the transmitted wireless signal due to the wireless multipath channel and the motion of an object; and using a processor, a memory communicably coupled to the processor, and an instruction set stored in the memory, obtaining TSCI (Time Series Channel Information) of the wireless multipath channel based on the received wireless signal; performing a classification of a sliding time window by analyzing the CI (Channel Information) contained in the TSCI within the sliding time window; calculating MI (Motion Information) for the sliding time window based on the TSCI and the classification of the sliding time window; and monitoring the motion of an object based on the MI.

[0182] Section 2. A method according to Section 1, wherein MI is calculated based on at least one of the similarity scores of two temporally adjacent CIs included in the TSCI, the ACF (autocorrelation function) of the TSCI, and the feature points of the ACF.

[0183] Item 3. A method according to Item 1, further comprising: if the sliding time window is classified as a first sliding window class based on classification, calculating MI in a first method based only on CIs contained within the sliding time window; if the sliding time window is classified as a second sliding window class based on classification, calculating MI in a second method based on at least one CI contained within the TSCI outside the sliding time window; and if the sliding time window is classified as a third sliding window class based on classification, calculating MI in a third method based on a first subset of CIs contained within the sliding time window without using a second subset of CIs contained within the TSCI within the sliding time window, wherein the first subset and the second subset are relatively prime.

[0184] Section 4. A method according to Section 3, further comprising: calculating a test score (TS) for each CI included in a TSCI within a sliding time window based on a certain number of temporally adjacent CIs, wherein the TS includes at least one of the following: difference, magnitude, vector similarity, vector dissimilarity, dot product, and cross product; and classifying each CI included in a TSCI within a sliding time window based on the corresponding TS.

[0185] Section 5. A method according to Section 4, further comprising: calculating an LTS (Linkwise Test Score) based on the sum of all TSs for CIs included in a TSCI within a sliding time window; and performing a classification of the sliding time window based on the LTS.

[0186] Section 6. The method according to Section 5, wherein if the LTS is greater than a first threshold, the sliding time window is classified as a first sliding window class, and if the LTS is less than a second threshold, the sliding time window is classified as a second sliding window class or a third sliding window class.

[0187] Section 7. A method according to Section 6, wherein each CI included in the TSCI within a sliding time window is classified as a first CI class if the corresponding TS is less than a third threshold, and as a second CI class if the corresponding TS is greater than a fourth threshold.

[0188] Section 8. A method of Section 7, wherein if all CIs contained within a TSCI in a sliding time window are first-class CIs classified as a first CI class, the sliding time window is classified as a first sliding window class, and if all CIs contained within a TSCI in a sliding time window are second-class CIs classified as a second CI class, the sliding time window is classified as a second sliding window class, and the TSCI in the sliding time window includes at least one first-class CI and at least one second-class CI.

[0189] A method according to Section 9.8, comprising: identifying at least one run of a first class CI and at least one run of a second class CI within a sliding time window, wherein each run comprises individual run lengths of consecutive CIs of the same individual CI class within the sliding time window, and each run length is one of a number, quantity or count greater than zero; and classifying the sliding time window based on the runs of the first class CI and the second class CI and the individual run lengths.

[0190] Section 10. A method according to Section 9, wherein if at least one selection run of a first class CI is selected, the sliding time window is classified as a third sliding window class, and if no selection run of a first class CI is selected, the sliding time window is classified as a second sliding window class.

[0191] Section 11. A method according to Section 10, further comprising: selecting at least one selective run of a first class CI based on the run length of each run of a first class CI and the TS associated with said run; and calculating an MI based on the at least one selected run of a first class CI, wherein the at least one selected run is at least one run of a first class CI having the longest run length among all runs of the first class CI within a sliding time window.

[0192] Section 12. A method according to Section 11, wherein if the individual run length of the first run of a series of first class CIs, including the first CI in a sliding time window, is greater than a first individual threshold, a first selected run is selected as the first run; if the individual run length of the last run of a series of first class CIs, including the last CI in a sliding time window, is greater than a second individual threshold, a second selected run is selected as the first run; and if the individual run length of the first run or the last run is greater than a third individual threshold, any selected run that is neither the first nor the last run is selected.

[0193] Item 13. A method according to Item 12, wherein at least one selection run is selected such that the quantity of at least one selection run is less than or equal to a predetermined number, and for each selection run, at least one selection run is selected such that all associated TSs are less than a threshold.

[0194] Section 14. A method of Section 13, further comprising: constructing a first subset by including all of at least one selected run of first class CIs contained in a TSCI within a sliding time window, when the sliding time window is classified as a third sliding window class; constructing a second subset by including all of second class CIs contained in a TSCI within a sliding time window; and calculating an MI as the sum of at least one adjacent MI, when the sliding time window is classified as a second sliding window class, wherein each adjacent MI is associated with one of the past adjacent sliding time windows of a CI contained in a TSCI, a future adjacent sliding time window of a CI contained in a TSCI, or an adjacent sliding time window of a CI contained in another TSCI, and the adjacent MI is calculated based on at least one CI contained in a TSCI outside the sliding time window.

[0195] Section 15. A method of Section 14, further comprising: calculating at least one provisional MI for a sliding time window, each provisional MI being calculated based on individual selected runs of first class CIs included in the TSCI within the sliding time window; and calculating the MI as a sum of at least one provisional MI.

[0196] Section 16. A method of Section 15, further comprising: determining a selected run as the leading run of CIs included in a TSCI within a sliding time window; compositing the leading run of CIs with the trailing run of CIs included in a TSCI within a previous sliding time window to form a composite run of CIs included in a TSCI; and calculating a first provisional MI based on the composite run of CIs.

[0197] Section 17. A method of Section 15, further comprising: determining a selected run as the last run of CIs included in a TSCI within a sliding time window; compositing the last run of CIs with the first run of CIs included in a TSCI within the next sliding time window to form a composite run of CIs included in a TSCI; and calculating a second provisional MI based on the composite run of CIs.

[0198] Section 18. A method according to Section 15, further comprising: calculating a separate calculated weight for each provisional MI calculated based on the run-length of the separate selection run; and calculating an MI as a weighted total of at least one provisional MI, wherein each provisional MI is weighted by the separate calculated weight.

[0199] Section 19. A method of Section 15, further comprising calculating MI as a sum of at least one provisional MI and at least one adjacent MI, wherein each adjacent MI is associated with one of the past adjacent sliding time windows of a CI included in a TSCI, a future adjacent sliding time window of a CI included in a TSCI, and an adjacent sliding time window of a CI included in another TSCI.

[0200] Item 20. A wireless monitoring system comprising: a first wireless device configured to transmit a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is affected by the motion of objects in the venue; a second wireless device configured to receive a wireless signal through the wireless multipath channel, wherein the received wireless signal is different from the transmitted wireless signal due to the wireless multipath channel and the motion of objects; and a processor, wherein the processor is configured to acquire TSCI (Time Series Channel Information) of the wireless multipath channel based on the received wireless signal; perform a classification of a sliding time window by analyzing the CI (Channel Information) contained in the TSCI within the sliding time window using the processor, a memory communicably coupled to the processor, and an instruction set stored in the memory; calculate MI (Motion Information) for the sliding time window based on the TSCI and the classification of the sliding time window; and monitor the motion of objects based on the MI.

[0201] The following numbered sections provide examples for precise wireless monitoring.

[0202] Section A1. A method / device / system / software for a wireless monitoring system, comprising: transmitting a wireless signal from a Type 1 wireless device of a wireless sensing system through a wireless multipath channel of a venue, wherein the wireless multipath channel is affected by the motion of an object in the venue; receiving the wireless signal through the wireless multipath channel by a Type 2 heterogeneous wireless device of the system, wherein the received wireless signal is different from the transmitted wireless signal due to the wireless multipath channel of the venue and the periodic vital sign motion of an object; using a processor, memory and instruction set, obtaining time-series CI (channel information) of the wireless multipath channel based on the received wireless signal; performing a sliding time window classification by analyzing the CI contained in the TSCI within the sliding time window; calculating MI (motion information) about the sliding time window based on the TSCI and the sliding time window classification; and monitoring motion based on the MI.

[0203] In some embodiments, several characteristic steps used to calculate MS / MI in wireless sensing are determining whether the condition is "normal," "abnormal," "moderately abnormal," or "severely abnormal."

[0204] Section A2. A method / device / system / software for a wireless monitoring system of Section A1, comprising calculating MI based on the similarity scores of two temporally adjacent CIs included in TSCI.

[0205] Section A3. A method / device / system / software for a wireless monitoring system as described in Section A1, comprising calculating MI based on the autocorrelation function (ACF) of TSCI.

[0206] Section A4. A method / device / system / software for a wireless monitoring system relating to Section A3, which includes calculating MI based on ACF feature points.

[0207] In some embodiments, each sliding time window may be classified as one of the “moderate abnormal,” “severe abnormal,” or “normal” sliding window classes. In some embodiments, the first sliding window class = “normal,” the second sliding window class = “moderate abnormal,” in some embodiments, the third sliding window class = “moderate abnormal,” in some embodiments, the third sliding window class = “moderate abnormal,” and in some embodiments, if the (current) sliding time window is classified as the “moderate abnormal” sliding window class (the third class), then in some embodiments, each CI within the current sliding time window may be classified as one of the “abnormal” or “normal” CI classes. A CI level test score (TS) may be calculated for each CI based on several CIs in its respective temporal neighborhood. Each CI may be classified based on its respective TS (e.g., “normal” if TS < threshold T1).

[0208] In some embodiments, the second subset of item A5 may be a collection of all abnormal CIs. The first subset may be a collection of all normal CIs (one or more runs of normal CIs, each run containing the run lengths of consecutive normal CIs with run length ≥ 1). The first subset may also be a subset of the collection of normal CIs (e.g., only normal CIs in the longest run or selected run, or only the first run of normal CIs, or only the last run of normal CIs).

[0209] In some embodiments, the reduced MI can be calculated based on a first subset (e.g., only the longest run of normal CIs, or only the first run, or only the last run, or a selected run). Alternatively, there may be multiple mutually exclusive runs of normal CIs. Some runs may be "satisfactory" (e.g., having a sufficient run length, or all having TS<T2<T1). Some runs may not be sufficient. Multiple temporarily reduced MIs may be calculated, each based on an individual mutually exclusive run of (sufficient) normal CIs. The reduced MI can be calculated as an aggregate value of the multiple temporarily reduced MIs. The aggregate value may be a weighted quantity (e.g., sum, weighted average, weighted product, weighted median, etc.). The weight of each temporarily reduced MI can be adaptively calculated based on the run length of the normal CI run used to calculate the temporarily reduced MI. In some embodiments, the (current) MS / MI of the (current) sliding time window can be calculated as the reduced MS / MI. Alternatively, the current MS / MI can be calculated as an aggregate value (e.g., weighted average) of the reduced MS / MI and some adjacent MS / MIs (e.g., past MI, future MI). Any MS / MI (e.g., current MI, past MI, future MI, reduced MI, temporarily reduced MI) can be normalized (e.g., by the amount of CI used to calculate the MS / MI). In some embodiments, each MS / MI can be calculated based on any of features (e.g., magnitude, size, square, phase), decomposition into multiple CI components (CICs) of the CI, decomposition into multiple time series CICs (TSCICs) of the TSCI, similarity score (e.g., TRRS) between two temporally adjacent CIs / CICs, ACF of the CI / CIC, frequency transform / eigen decomposition of the CI / CIC, feature points / values of the ACF / transform / decomposition (e.g., local maximum / local minimum / zero crossing), component processing, etc.

[0210] In some embodiments, if the second sliding window class = "severe anomaly": the (current) sliding time window is severe anomaly, the MS / MI may not be calculated using the sliding time window. Instead, the MS / MI may be replaced (i.e., calculated) by several adjacent MS / MIs (e.g., past MIs, most recent past MIs, future MIs, next MIs, or MIs calculated based on another TSCI) or a set thereof. In some embodiments, any past MI may be associated with a past sliding time window and may be calculated based on several past CIs in each past sliding time window outside the current sliding time window. Similarly, any future MI may be associated with a future sliding time window and may be calculated based on several future CIs in each future sliding time window outside the current sliding time window. In some embodiments, if the first sliding window class = "normal", then in some embodiments, when everything is normal, the MI for each sliding time window may be calculated based only on the CI of the TSCI within the sliding time window, without using the CI of the TSCI outside the sliding time window.

[0211] Section A5. A method / device / system / software for a wireless monitoring system as described in Section A1, comprising: calculating MI in a first way based exclusively on the CIs of TSCIs within a sliding time window if the sliding time window is classified as a first sliding time window class; calculating MI in a second way based on at least one CI of TSCIs outside the sliding time window if the sliding time window is classified as a second sliding time window class; and excluding a second subset of the CIs of TSCIs within the sliding time window in the calculation of MI, and calculating MI in a third way based on a first subset of the CIs of TSCIs within the sliding time window if the sliding time window is classified as a third sliding time window class, wherein the first subset and the second subset are relatively prime.

[0212] In some embodiments, to classify the sliding time window, each of the multiple CIs within the sliding time window may be classified based on its corresponding test score (TS). In some embodiments, for a second (item-wise) classification, each CI may be classified into either a "normal" or "abnormal" CI class.

[0213] Section A6. A method / device / system / software for a wireless monitoring system as described in Section A5, comprising: calculating a test score (TS) for each CI of a TSCI within a sliding time window; and performing an item-wise classification of each CI of a TSCI within a sliding time window based on the respective TS.

[0214] Section A6b. A method / device / system / software for a wireless monitoring system of Section A6, wherein each CI has N1 components, and comprises: calculating a component-wise test score (CTS) for each component of each CI of the TSCI within a sliding time window, and calculating a TS for each CI as the sum of the N1 CTS associated with the CI.

[0215] Section A6c. A method / device / system / software for a wireless monitoring system of Section A6b, comprising calculating a CTS for a component of several temporally adjacent CIs based on each component of each CI.

[0216] Section A6d. A method / device / system / software for a wireless monitoring system relating to Section A6b, comprising calculating the TS of each CI as the sum of a selected subset of N1 CTS associated with the CI, wherein the selected subset is selected based on criteria.

[0217] In some embodiments, TS at time t, i.e., TS(t), may be a function of the time-adjacent CI(ti), where i = 0, ±1, ±2, ±3, ..., etc. In some embodiments, TS(t) = CI(t) - pastCI(t), or TS(t) = mag[CI(t) - pastCI(t)]^a, where mag = magnitude, and pastCI(t) is CI(t-1), or [CI(t-1) + CI(t-2)] / 2, or [2*CI(t-1) + CI(t-2) + CI(t-3)] / 3, or [2*CI(t-1) + CI(t-2) + CI(t-3)] / 4, or [3*CI(t-1) + 2*CI(t-2) + CI(t-3). In some embodiments, TS(t) is also TS(t) = CI(t) - future reCI(TS), or [CI(t)=mag(t)-futureCI(t)]^b, where mag=magCI(t) can be CI(t+1), or [CI(t+1)+CI(t+2)] / 2, or [2*CI(t+1)+CI(t+2)+CI(t+3)] / 3, or [2*CI(t+1)+CI(t+2)+CI(t+3)] / 4, or [3*CI(t+1)+2*CI(t+2)+CI(t+3)] / 6, etc. In some embodiments, TS(t) may also be TS(t)=CI(t)-d*pastCI(t)-(1-d)*future(t), where 0 <d<1である。

[0218] Section A7. A method / device / system / software for a wireless monitoring system of Section A6, comprising calculating a TS for several temporally adjacent CIs, wherein the TS includes at least one of the following: test quantity, similarity score, mismatch score, distance score, TRRS, correlation, covariance, autocorrelation, cross-correlation, inner product, norm, total value, difference, absolute difference, squared difference, variance, standard deviation, spread, variance, variance, diversity, diversity, skewness, kurtosis, range, interquartile range, coefficient, Gini coefficient, entropy, maximum, minimum, median, median, median, median, median, mode, percentile, interquartile, variance-to-median ratio, maximum-to-minimum ratio, regularity, irregularity, statistic, histogram, probability, impulsivity, sudden, occurrence, recurrence, or change.

[0219] In some embodiments, TS may include a difference (i.e., subtraction or vector subtraction). In some embodiments, for a vector X (N tuple), F(X) may be a vector (which is also an N tuple) of the same size, such that the i^th component of F(X) is a function of the i^th component of X. The function may include magnitude, the square of magnitude, phase, another function of magnitude, or another function of phase.

[0220] Chapter A8.ChapterA6Publications. See also Figure 1 in Figure 1 . CI(t)-CI(t-1),CI(t)-CI(t+1),CI(t)-[CI(t-1)+C I(t+1)] / 2,CI(t)-CI(t+2),CI(t)-CI(t+2),CI(t)-[CI(t-2)+CI(t+2)] / 2,CI(t)-CI(tk),CI(t)-CI(t+l),CI(t)-[CI(tk)+CI[,t+CIl(t)] / 2 _1*CI(t-1)+a_2*CI(t-2)+...+a_k*CI(tk)] / (a_1+a_2+...+a_k),CI(t)-[b_1*CI(t+1)+b_2*CI(t+2)+...+b_l*CI(t+l)]b_(2+C_1 I(t)-[a_1*CI(t-1)+a_2*CI(t-2)+...+a_k*CI(tk)+b_1*CI(t+1)+b_2*CI(t+2)+...+b_l*CI(t+l)] / (a_1+a_2+),...+a_k2+b_(F_1+b_ (t))-F(CI(t-1)),F(CI(t))-F(CI(t+1)),F(CI(t))-[F(CI(t-1))+F(CI(t+1))] / 2,F(CI(t))-F(CI(t-2)),F(CI(t))-F(F-(t+2)), I(t-2))+F(CI(t+2))] / 2,F(CI(t))-F(CI(tk)),F(CI(t))-F(CI(t+l)),F(CI(t))-[F(CI(tk))+F(CI(t+l))] / 2,F(CI(t-2*))(+CI(1) F(CI(t-2))+...+a_k*F(CI(tk))] / (a_1+a_2+...+a_k),F(CI(t))-[b_1*F(CI(t+1))+b_2*F(CI(t+2))+...+b_l*F(CI(t+l...)+b__ / ).(+a_k+b_1+b_2+...+b_l). Here, for any vector X, F(X) is a vector of the same size in X such that the i-th component of F(X) is a function of the i-th component of X, a_1, a_2, ..., a_k, b_1, b_2, ..., b_l are scalars, and CI(t) is CI at time t.

[0221] In some embodiments, TS may include vector dissimilarity / similarity, dot product, and cross product.

[0222] Item A9. A method / device / system / software of the wireless monitoring system of Item A6, where TS includes at least one of the following: CI(t)xCI(t - 1), CI(t)xCI(t + 1), CI(t)x[CI(t - 1)+CI(t + 1)] / 2, CI(t)xCI(t - 2), CI(t)xCI(t + 2), CI(t)x[CI(t - 2)+CI(t + 2)] / 2, CI(t)xCI(t - k), CI(t)xCI(t + l), CI(t)x[CI(t - k)+CI(t + l)] / 2, CI(t)x[a_1*CI(t - 1)+a_2*CI(t - 2)+...+a_k*CI(t - k)] / (a_1 + a_2 +...+ a_k), CI(t)x[b_1*CI(t + 1)+b_2*CI(t + 2)+...+b_l*CI(t + l)] / (b_1 + b_2 +...+ b_l), CI(t)x[a_1*CI(t - 1)+a_2*CI(t - 2)+...+a_k*CI(t - k)+b_1*CI(t + 1)+b_2*CI(t + 2)+...+b_l*CI(t + l)] / (a_1 + a_2 +...+ a_k + b_1 + b_2 +...+ b_l), F(CI(t))xF(CI(t - 1)), F(CI(t))xF(CI(t + 1)), F(CI(t))x[F(CI(t - 1))+F(CI(t + 1))] / 2, F(CI(t))xF(CI(t - 2)), F(CI(t))xF(CI(t + 2)), F(CI(t))x[F(CI(t - 2))+F(CI(t + 2))] / 2, F(CI(t))xF(CI(t - k)), F(CI(t))xF(CI(t + l)), F(CI(t))x[F(CI(t - k))+F(CI(t + l))] / 2, F(CI(t))x[a_1*F(CI(t - 1))+a_2*F(CI(t - 2))+...+a_k*F(CI(t - k))] / (a_1 + a_2 +...+ a_k), F(CI(t))x[b_1*F(CI(t + 1))+b_2*F(CI(t + 2))+...+b_l*F(CI(t + l))] / (b_1 + b_2 +...+ b_l), F(CI(t))x[a_1*F(CI(t - 1))+a_2*F(CI(t - 2))+...+a_k*F(CI(t - k))+b_1*F(CI(t + 1))+b_2*F(CI(t + 2))+...+b_l*F(CI(t + l))] / (a_1 + a_2 +...+ a_k + b_1 + b_2 +...+b_l), D[CI(t), CI(t - 1)], D[CI(t), CI(t + 1)], D[CI(t), [CI(t - 1)+CI(t + 1)] / 2], D[CI(t), CI(t - 2)], D[CI(t), CI(t + 2)], D[CI(t), [CI(t - 2)+CI(t + 2)] / 2], D[CI(t), CI(t - k)], D[CI(t), CI(t + l)], D[CI(t), [CI(t - k)+CI(t + l)] / 2], D[CI(t), [a_1*CI(t - 1)+a_2*CI(t - 2)+...+a_k*CI(t - k)] / (a_1 + a_2+...+a_k)], D[CI(t), [b_1*CI(t + 1)+b_2*CI(t + 2)+...+b_l*CI(t + l)] / (b_1 + b_2+...+b_l)], D[CI(t), [a_1*CI(t - 1)+a_2*CI(t - 2)+...+a_k*CI(t - k)+b_1*CI(t + 1)+b_2*CI(t + 2)+...+b_l*CI(t + l)] / (a_1 + a_2+...+a_k + b_1 + b_2+...+b_l)], D[F(CI(t)), F(CI(t - 1))], D[F(CI(t)), F(CI(t + 1))], D[F(CI(t)), [F(CI(t - 1))+F(CI(t + 1))] / 2], D[F(CI(t)), F(CI(t - 2))], D[F(CI(t)), F(CI(t + 2))], D[F(CI(t)), [F(CI(t - 2))+F(CI(t + 2))] / 2], D[F(CI(t)), F(CI(t - k))], D[F(CI(t)), F(CI(t + l))], D[F(CI(t)), [F(CI(t - k))+F(CI(t + l))] / 2], D[F(CI(t)), [a_1*F(CI(t - 1))+a_2*F(CI(t - 2))+...+a_k*F(CI(t - k))] / (a_1 + a_2+...+a_k)], D[F(CI(t)), [b_1*F(CI(t + 1))+b_2*F(CI(t + 2))+...+b_l*F(CI(t + l))] / (b_1 + b_2+...+b_l)], or D[F(CI(t)), [a_1*F(CI(t - 1))+a_2*F(CI(t - 2))+...+a_k*F(CI(t - k))+b_1*F(CI(t + 1))+b_2*F(CI(t + 2))+...+b_l*F(CI(t + l))] / (a_1 + a_2+...+a_k + b_1 + b_2 +... + b_l)]。. Here, for any vector A, F(A) is a vector of the same size as A such that the i-th component of F(A) is a function of the i-th component of A, each of A_1, A_2,..., A_k, B_1, B_2,..., and B_l is a scalar, AxB is the inner product of vector A and vector B, D[A, B] is the dissimilarity score between vector A and vector B, and CI(t) is the CI at time t.

[0223] In some embodiments, the link-wise TS (LTS) of the sliding time window can be calculated based on all the TSs within the sliding time window. The classification of the sliding time window can be based on the LTS.

[0224] Item A10. A method / device / system / software of the wireless monitoring system of item A6, comprising calculating a link-wise test score (LTS) based on a plurality of TSs for the CI of the TSCI within a sliding time window, and performing classification of the sliding time window based on the LTS.

[0225] In some embodiments, the LTS can be the aggregate value (e.g., average, weighted average, median, maximum value, minimum value, percentile) of all the TSs within the sliding time window.

[0226] Item A11. A method / device / system / software of the wireless monitoring system of item A10, comprising calculating the LTS as the aggregate value of a plurality of TSs, and the aggregate value includes at least one of sum, weighted sum, average, weighted average, geometric average, weighted geometric average, weighted geometric average, harmonic average, weighted harmonic average, weighted harmonic average, arithmetic average, weighted average, trimming average, median, weighted median, mode, histogram, statistics, percentile, maximum value, minimum value, variance, variation, divergence, spread, range, deviation, or characteristic value.

[0227] In some embodiments, a sliding time window may be classified as "normal" (first sliding window class) if the LTS is greater than (or less than) the threshold. For example, "normal" if TS = dissimilarity score, LTS = maximum TS (or 95th percentile), and LTS > threshold, or "normal" if TS = similarity score, LTS = minimum TS (or 5th percentile), and LTS < threshold.

[0228] Section A12. A method / device / system / software for a wireless monitoring system according to Section A10, comprising: classifying a sliding time window as a first sliding window class when the LTS is greater than a first threshold; and classifying a sliding time window as a class comprising a second sliding window class and a third sliding window class when the LTS is less than a second threshold.

[0229] In some embodiments, LTS may be calculated directly based on CI within a sliding time window (without calculating TS).

[0230] A method / device / software for a wireless monitoring system as described in Section A13.A1, wherein LTS comprises calculating at least one of the following in a sliding time window: score of TSCI, correlation, covariance, cross-correlation, norm, difference, absolute difference, squared difference, variance, standard deviation, spread, variance, distance, range, interquartile range, coefficient, Gini coefficient, entropy, maximum, mean, median, percentile, variance-to-mean ratio, maximum-to-minimum ratio, linkwise test score (LTS), regularity, irregularity, statistics, histogram, probability, impulsivity, suddenness, occurrence, recurrence, change, and performing classification of the sliding time window based on LTS.

[0231] In some embodiments, in order to classify each CI into a plurality of CI classes, there may be a "normal" CI class and / or an "abnormal" CI class. There may be a "moderately abnormal" CI class, a "severely abnormal" CI class, and / or a "mildly abnormal" CI class. In some embodiments, the first CI class = "normal" and the second CI class = "abnormal". In some embodiments, if TS>T4, the CI can be classified as "abnormal" (or "severely abnormal"), and if TS<T3<=T4, it can be classified as "normal". T4 may be equal to T3. If T3<T4 and T3<TS<T4, the CI can be classified as "moderately abnormal". Alternatively, if T3<TS<T5, it may be classified as "mildly abnormal", and if T5<TS<T4, it can be classified as "moderately abnormal". In some embodiments, the second subset may include all "abnormal" CIs. The second subset can be excluded from the calculation of MI. In some embodiments, the first subset may include all "normal" CIs (or all selected runs of normal CIs). The first subset, or a part / subset of the first subset, can be included in the calculation of MI. In some embodiments, the first subset or the second subset may include "moderately abnormal" CIs or "mildly abnormal" CIs. In particular, the first subset may include some / all of the "moderately abnormal" CIs or "mildly abnormal" CIs. In some embodiments, one or more reduced MIs (or tentatively reduced MIs) can be calculated based on the first subset. Basically, "normal" CIs may be more reliable than "mildly abnormal" CIs, which may in turn be more reliable than "moderately abnormal" CIs. A first reduced MI calculated purely based on "normal" CIs may be "more reliable" than a second reduced MI calculated based on a combination of "normal" CIs and "moderately abnormal" CIs (or "mildly abnormal" CIs). A third reduced MI calculated based on (N-N1) "normal" CIs and N1 "moderately abnormal" CIs may be "more reliable" than a fourth reduced MI calculated based on (N-N2) "normal" CIs and N2 "moderately abnormal" CIs, where N2>N1.In some embodiments, if MI includes a weighted quantity of multiple reduced MIs (e.g., a weighted mean, a weighted sum, or a weighted geometric mean), a more reliable reduced MI (e.g., a first reduced MI, or a third reduced MI) may have a greater weight than an unreliable reduced MI (e.g., a second reduced MI, or a fourth reduced MI). The weight for reduced MI in the weighted quantity may depend on the count of “normal” CIs, the count of “mildly abnormal” CIs, and the count of “moderately abnormal” CIs used in the calculation of reduced MI. In some embodiments, if MI includes a weighted quantity of reduced MIs and several “neighboring MIs” from a “neighboring sliding time window” and / or “neighboring TSCIs,” the weight for reduced MI in the weighted quantity may depend on the count of “normal” CIs, the count of “mildly abnormal” CIs, and the count of “moderately abnormal” CIs used in the calculation of reduced MI.

[0232] Section A14. A method / device / system / software for a wireless monitoring system as described in Section A6, comprising, in the second (itemwise) classification, classifying each CI as a first CI class if each TS is below a third threshold, and classifying each TS as a second CI class if each TS is greater than a fourth threshold.

[0233] In some embodiments, the first CI class is "normal" and the second CI class is "abnormal". In some embodiments, not all "normal" CIs are used in the MI calculation. The first subset may include, for example, only some (e.g., all or not all) of the "normal" CIs. The MI may be calculated based on the longest run of the "normal" CIs. In other words, the second, third, fourth... longest runs of the "normal" CIs may or may not be used in the MI calculation. In some embodiments, the first subset may include all of the "normal" CIs. In some embodiments, the first sliding window class is "normal," in some embodiments, the second sliding window class is "severely abnormal," in some embodiments, the third sliding window class is "moderately abnormal," and in some embodiments, if all (or the majority) of the CIs are "normal" (first CI class), the sliding time window may be classified as "normal" (first CI class). In some embodiments, if all (or the majority) of the CIs are "abnormal" (second CI class), the sliding time window may be classified as "severely abnormal" (second sliding window class). In some embodiments, the majority may be 80%, 90%, 95%, 99%, or 99.9%, or other percentages.

[0234] Section A15. A method / device / system / software for a wireless monitoring system as described in Section A14, comprising classifying a sliding time window as a first sliding window class if all CIs of a TSCI within the sliding time window are first class CIs, and classifying a sliding time window as a second sliding window class if all CIs of a TSCI within the sliding time window are second class CIs, wherein any first class CI is a CI classified as a first class CI, and any second class CI is a CI classified as a second class CI.

[0235] In some embodiments, if there is at least one first class CI (i.e., not all CIs are of the second class) and at least one second class CI (i.e., not all CIs are of the first class), there is at least one run of the first class CI and at least one run of the second class CI, each run having run lengths of consecutive CIs of the same class. In some embodiments, the classification of the sliding time window may be based on run lengths and a plurality of TSs associated with each run (in particular, runs of the first class CI).

[0236] Section A16. A method / device / system / software for a wireless monitoring system as described in Section A15, wherein within a sliding time window there exists at least one first class CI and at least one second class CI of TSCI, and the method / device / software

[0237] In some embodiments, several "selected" runs of a "normal" CI may be selected (or identified). The "selected" runs must meet certain criteria / requirements / conditions. If at least one selected run can be found (and used to calculate the reduced MS / MI), the sliding time window may be classified as "moderately abnormal" (third class). Otherwise, the sliding time window may be classified as "severely abnormal" (second class).

[0238] Section A17. A method / device / system / software for a wireless monitoring system as described in Section A16, comprising classifying a sliding time window as a third sliding window class if at least one optional run of a first class CI is selected, and classifying the sliding time window as a second sliding window class if no optional run of a first class CI is selected.

[0239] In some embodiments, several "selected" runs of "normal" CIs (first-class CIs) may be selected (or identified).

[0240] Section A18. A method / device / system / software for a wireless monitoring system as described in Section A17, comprising: selecting at least one selective run of a Class 1 CI based on the run length of each run of a Class 1 CI and a plurality of TSs associated with the run; and calculating an MI based on the at least one selected run of a Class 1 CI.

[0241] In some embodiments, one "selected" run may be the run with the longest run length in the sliding time window.

[0242] Section A19. A method / device / system / software for a wireless monitoring system as described in Section A18, wherein a particular selected run is a run of Class 1 CI having the longest run length among all runs of Class 1 CI within a sliding time window.

[0243] In some embodiments, at least one selected run may be a run length having the longest run length in the sliding time window.

[0244] Item A20. A method / device / system / software of the wireless monitoring system of Item A19, wherein at least one selected run is at least one run of Class CI having the longest run length among all runs of Class CI within a sliding time window.

[0245] In some embodiments, one "selected" run can be the leading run having a sufficient run length. Runs with very short run lengths may not be sufficient to calculate MI with good accuracy. In some embodiments, the run length requirement for the leading or trailing run may not be stricter than that of other runs. In other words, the run length threshold for the leading or trailing run (e.g., in Item A21 or Item A22) may be smaller (i.e., less strict) than the run length threshold for other runs (e.g., in Item A23).

[0246] Item A21. A method / device / system / software of the wireless monitoring system of Item A18, wherein when the respective run lengths of a particular selected run are each greater than their respective thresholds, it is selected as the leading run of consecutive Class CI that includes exactly the first CI within a sliding time window.

[0247] In some embodiments, one "selected" run may be the trailing run having a sufficient run length.

[0248] Item A22. A method / device / system / software of the wireless monitoring system of Item A18, wherein when the respective run lengths of a particular selected run are each greater than their respective thresholds, it is selected as the trailing run of consecutive Class CI that includes the last CI within a sliding time window.

[0249] In some embodiments, all "selected" runs (excluding the leading or trailing runs) may have a sufficient run length.

[0250] Section A23. A wireless monitoring system method / device / system / software of Section A18, wherein if the run length of each run is greater than the respective threshold, any optional run other than the first run or the last run is selected.

[0251] In some embodiments, the number of selection runs may be limited by a predetermined number.

[0252] Section A24. A method / device / system / software for a wireless monitoring system of Section A18, wherein at least one selected run is selected such that the count of at least one selected run is less than or equal to a predetermined number.

[0253] In some embodiments, the number of selection runs may be a predetermined number.

[0254] Section A25. A method / device / system / software for a wireless monitoring system according to Section A24, wherein at least one selection run is selected such that the count of at least one selection run is a predetermined number.

[0255] In some embodiments, all TSs associated with the selection run may satisfy the conditions.

[0256] Section A26. A method / device / system / software for a wireless monitoring system of Section A18, wherein at least one selection run is selected for each selection run such that all relevant TSs satisfy the conditions.

[0257] In some embodiments, the selected run may have associated TS below a threshold.

[0258] Section A27. A method / device / system / software for a wireless monitoring system of Section A18, wherein at least one selection run is selected such that, for each selection run, all associated TSs are below a threshold.

[0259] In some embodiments, there may be two or more selection runs. The provisionally reduced MS / MI may be calculated based on each selection run. The MS / MI (or reduced MS / MI) may be calculated as a set of two or more provisionally reduced MS / MIs.

[0260] Section A28. A method / device / system / software for a wireless monitoring system of Section A18, comprising calculating at least one provisional MI for a sliding time window, each provisional MI being calculated based on each selected run of a first class CI of the TSCI within the sliding time window, and calculating the MI as the sum of at least one provisional MI.

[0261] In some embodiments, the beginning run of the (current) sliding time window may be combined with the end run of the CI of the previous sliding time window to form a combined run of CIs. In some embodiments, a provisionally reduced MS / MI may be calculated based on the combined run of CIs.

[0262] Section A29. A method / device / system / software for a wireless monitoring system as described in Section A28, comprising: calculating a specific provisional MI based on a selected run which is the leading run of the CI of the TSCI within a sliding time window; combining the trailing run of the CI of the TSCI within a previous sliding time window with the leading run of the CI to form a composite run of the CI of the TSCI; and calculating a specific provisional MI based on the composite run of the CI.

[0263] In some embodiments, the tail run of the (current) sliding time window may be combined with the head run of the CI in the next sliding time window to form a combined run of CI. In some embodiments, a provisionally reduced MS / MI may be calculated based on the combined run of CI.

[0264] Section A30. A method / device / system / software for a wireless monitoring system as described in Section A28, comprising: calculating a specific provisional MI based on a selected run which is the last run of the CI of the TSCI in a sliding time window; combining the last run of the CI with the first run of the CI of the TSCI in the next sliding time window to form a combined run of the CI of the TSCI; and calculating a specific provisional MI based on the combined run of the CI.

[0265] In some embodiments, MS / MI (or reduced MS / MI) may be a weighted sum of two or more provisionally reduced MS / MIs (e.g., weighted sum / mean / mean / median / product). In some embodiments, the weight of each provisionally reduced MS / MI may be adaptively calculated based on the run length of each selected run of the first class CI used to calculate the provisionally reduced MS / MI. The weight of each provisionally reduced MS / MI may also be adaptively calculated based on a plurality of TS associated with each selected run.

[0266] Section A31. A method / device / system / software for a wireless monitoring system as described in Section A28, comprising: calculating a calculated weight for each provisional MI calculated based on the run length of each selected run; and calculating an MI as a weighted aggregate of at least one provisional MI, each provisional MI weighted by the calculated weight.

[0267] In some embodiments, the MS / MI (or reduced MS / MI) may be a weighted sum (e.g., weighted sum / average / mean / median / product) of one or more provisionally reduced MS / MIs and at least one adjacent MS / MI. Each neighbor MI may be associated with a past neighbor sliding time window, a future neighbor sliding time window, or a neighbor (past, present, or future) time window of another TSCI, which may be obtained based on another radio signal communicated between a Type 1 device and a Type 2 device. For example, the other radio signal may be the radio signal in item A1.

[0268] Section A32. A method / device / system / software for a wireless monitoring system as described in Section A28, comprising calculating MI as a sum of at least one provisional MI and at least one neighbor MI, each neighbor MI being associated with one of the past neighbor sliding time window of CI of TSCI, the future neighbor sliding time window of CI of TSCI, or the neighbor sliding time window of CI of another TSCI.

[0269] In some embodiments, if the sliding time window is classified as “moderately abnormal” (third sliding window class), the MS / MI may be calculated using the first subset of CIs of the TSCI within the sliding time window, without using the second subset. The first subset may include all “normal” (first class) CIs of the TSCI within the sliding time window. The second subset may include all “abnormal” (second class) CIs of the TSCI within the sliding time window.

[0270] Section A33. A method / device / system / software for a wireless monitoring system as described in Section A14, which, when a sliding time window is classified as a third sliding window class, includes constructing a first subset by including all first class CIs of TSCI within the sliding time window, and constructing a second subset by including all second class CIs of TSCI within the sliding time window.

[0271] In some embodiments, the first subset may include only one selected run of “normal” (first class) CIs of the TSCI within a sliding time window. In some embodiments, the second subset may include all “abnormal” (second class) CIs of the TSCI within a sliding time window.

[0272] Section A34. A method / device / system / software for a wireless monitoring system as described in Section A18, which, when a sliding time window is classified as a third sliding window class, includes constructing a first subset by including all of at least one selected run of a first class CI of a TSCI within the sliding time window, and constructing a second subset by including all of the second class CI of a TSCI within the sliding time window.

[0273] In some embodiments, the second sliding window class = "severe anomaly". In some embodiments, the MI may not be calculated based on the CI of the TSCI within the (current) sliding time window because the CI is too anomaly / high reliability (i.e., severe anomaly). In some embodiments, the replacement MI may be calculated based on the sum of several adjacent MIs. Each adjacent MI may be associated with a past / future / adjacent sliding time window classified as "normal".

[0274] Section A35. A method / device / system / software for a wireless monitoring system as described in Section A17, wherein a sliding time window is classified as a second sliding window class, comprising calculating MI as a set of at least one adjacent MI, each adjacent MI associated with one of the past adjacent sliding time windows of a CI of a TSCI, a future adjacent sliding time window of a CI of a TSCI, or an adjacent sliding time window of a CI of another TSCI, and the adjacent MI is calculated based on at least one CI of a TSCI outside the sliding time window.

[0275] In some embodiments, the similarity between current channel information (CI) and previous CIs may be calculated for wireless sensing. CI may include any of the following: channel response (CR), channel impulse response (CIR), channel frequency response (CFR), channel status information (CSI), received signal strength (RSS), or RSS indicator (RSSI).

[0276] The following numbered sections provide examples of wireless sensing and positioning.

[0277] Item B1. A wireless sensing system comprising a receiver configured to receive a radio signal transmitted from a transmitter through a radio channel, and one or more data processors configured to: determine channel information (CI) based on the received radio signal, wherein the CI includes at least one of channel response, channel impulse response (CIR), channel frequency response (CFR), channel state information (CSI), received signal strength (RSS), or RSS indicator (RSSI); compare the CI with a previous CI obtained based on a previously received radio signal; and calculate a similarity between the CI and the previous CI, wherein the similarity includes at least one of time-reverse resonance strength (TRRS), pattern recognition, matching, distance, Euclidean distance, correlation, autocorrelation function, or cosine function; and calculate at least one of the following items based on the similarity: Target location, target placement, target configuration, distance from wireless access point device, distance between transmitter and receiver, target identification, target movement, target proximity, target position, target orientation, target pose, target presence, target state, target placement information, change, change indication, target detection, target characteristics, gesture, target falling to the ground, navigation, guidance, target advertising, state within the venue, or state of the venue. It is configured to perform a specific action, and the target is one of the following: a receiver, a transmitter, an object, a person, or an event.

[0278] In some embodiments, two or more radio signals are present. Two or more CIs can be obtained. A composite CI can be calculated based on two or more CIs.

[0279] Item B2. The system of item B1, comprising at least one receiver configured to receive each radio signal transmitted from each transmitter through each radio channel, determining at least one CI based on at least one received radio signal, comparing each previous CI obtained based on each previously received radio signal with one of the at least one CIs, calculating the similarity between the CI and each previous CI, determining a composite CI based on at least one radio signal and at least one CI, and comparing the composite CI with a previous composite CI, wherein the previous composite CI is determined based on at least one previously received radio signal and at least one previous CI, wherein at least one The previous CI is determined based on at least one previously received radio signal; a second similarity is calculated between the composite CI and the previous composite CI, where the second similarity is calculated on at least one of the following items: two CIs, a second TRRS between the two composite CIs, a second pattern recognition based on the CIs, a second pattern recognition based on the composite CIs, a matching based on the composite CIs, a second matching based on the composite CIs, a distance, a second distance based on the composite CIs, a second distance based on the composite CIs, a second Euclidean distance based on the composite CIs, a correlation based on the CIs, a second correlation based on the composite CIs, an autocorrelation function based on the CIs, a second autocorrelation function based on the composite CIs, a cosine function based on the CIs, or a second cosine function based on the composite CIs.

[0280] In some embodiments, the wireless signal may include two or more probe signals. A sequence of CIs can be obtained.

[0281] Item B3. The system of item B1, wherein the radio signal includes a sequence of probe signals transmitted periodically, and the sequence of CIs is determined based on the received radio signal, with one CI for each probe signal.

[0282] In some embodiments, if the current CI is an outlier, it may be discarded.

[0283] Item B4. The system of item B1, wherein one or more data processors are further configured to compare a CI with several other previous CIs, determine that a CI is an outlier CI if the similarity between the CI and several other previous CIs is below a threshold, and discard outlier CIs.

[0284] In some embodiments, a discarded outlier CI may be replaced by another CI (a good CI).

[0285] Item B5. The system of item B1, wherein one or more data processors are further configured to replace a CI with another CI obtained based on another received radio signal transmitted from each transmitter to each receiver, and to calculate similarity based on the other CI instead of the CI, the similarity being between the other CI and the previous CI.

[0286] In some embodiments, if a particular CI is an outlier, it may be discarded.

[0287] Item B6. The system of item B2, wherein one or more data processors are further configured to compare a particular CI of at least one CI with several other previous CIs, determine that the particular CI is an outlier CI if the similarity between the particular CI and several other previous CIs is below a threshold, and discard the particular CI.

[0288] In some embodiments, a discarded outlier CI may be replaced by another CI (a good CI).

[0289] Item B7. The system of item B6, wherein one or more data processors are further configured to replace a particular CI with another CI obtained based on another received radio signal transmitted from each transmitter to each receiver, and to calculate a composite CI based on the other CI instead of the particular CI.

[0290] In some embodiments, if the current CI is innovative / novel compared to several benchmark CIs, it may be used to replace one of the benchmark CIs.

[0291] Section B8. The system of Section B1, wherein one or more data processors are further configured to: compare a CI with multiple reference CIs in the database; determine a CI is new when the similarity between the CI and the multiple reference CIs is below a threshold; and update the database by replacing a specific reference CI in the database with the CI.

[0292] In some embodiments, if the current CI is "similar" to a past CI, the channel / venue may be considered static / static.

[0293] Item B9. The system of item B1, wherein one or more data processors are further configured to: compare a CI with several other previous CIs; determine a radio channel if the similarity between the CI and several other previous CIs is greater than a threshold; and calculate at least one of the calculated items based on the stationarity of the radio channel.

[0294] In some embodiments, the transmitter and receiver and CI may need to be certified (for certain tasks). In some embodiments, the transmitter / receiver / CI may be considered sufficiently good / qualified if the current CI is "similar" to a reference CI.

[0295] Item B10. The system of item B1, wherein one or more data processors are further configured to determine that a CI determined based on a received radio signal communicated between a transmitter and a receiver is sufficiently accurate when the similarity between the CI and several other previous CIs is greater than a threshold.

[0296] In some embodiments, the CI may have multiple components (for example, for subcarriers).

[0297] Item B11. The system of item B2, wherein each radio signal comprises a certain amount of subcarriers, a certain frequency band, and a certain bandwidth, and each CI comprises two or more CI components, each CI component being associated with a subcarrier.

[0298] In some embodiments, the transmitter, receiver, radio signal, and CI may be standard / protocol compliant.

[0299] Item B12. A system according to Item B2, wherein each radio signal is communicated between each transmitter and each receiver in accordance with a radio signaling protocol or standard, and the radio signaling protocol or standard comprises at least one of IEEE 802.11, IEEE 802.11xx, IEEE 802.15, IEEE 802.15.3, IEEE 802.15.4, WiFi, 3G / LTE / 4G / 5G / 6G / 7G / 8G, Bluetooth, BLE, Zigbee, UWB, NFC, radio data transmission standard or protocol, radio data communication standard or protocol, radio network standard or protocol, cellular network standard or protocol, wireless local area network (WLAN) standard, OFDM, OFDMA, or CDMA.

[0300] In some embodiments, the wireless signal may include standard / protocol-compliant frames / packets.

[0301] Item B13. The system of item B12, wherein each radio signal comprises one or more frames having a format compliant with a radio signaling standard or protocol.

[0302] In some embodiments, CI can be obtained based on a standardized mechanism.

[0303] Section B14. The system of Section B12, wherein the CI is determined based on a mechanism standardized according to a wireless signaling standard or protocol.

[0304] In some embodiments, the CI may be obtained based on the preamble within the standards-compliant frame.

[0305] Item B15. The system of item B13, in which the CI is determined based on one of the long preambles in the frame.

[0306] In some embodiments, CI may be obtained based on standard / protocol-compliant signaling (control signals, negotiated parameters).

[0307] Section B16. The system of Section B14, wherein the CI is determined based on parameters and control signals passed between the transmitter and receiver in accordance with a radio signaling standard or protocol.

[0308] Item B17. The system of item B1, wherein one or more data processors are further configured to determine CI based on at least one of discretization, derivation, "exact-sufficient" determination, and a desired precision of system performance.

[0309] In some embodiments, CI may be obtained based on the preamble in the radio signal.

[0310] Item B18. The system of item B103, wherein CI is determined from the received radio signal based on the radio signal preamble.

[0311] Section B19. The system of Section B103, wherein the CI is processed to mitigate variations in CI resulting from at least one of the following: incomplete transmission by each transmitter, incomplete reception by each receiver, incomplete timing, incomplete frequency synchronization, channel frequency offset (CFO), or sampling frequency offset (SFO).

[0312] In some embodiments, CSI is reported locally.

[0313] Item B20. The system of item B103, wherein the CI determined based on each received radio signal is acquired and processed locally in the device that receives each received radio signal.

[0314] In some embodiments, the CI may be processed remotely by another device.

[0315] Section B21. The system of Section B103, wherein the CI determined based on each received radio signal is processed remotely in a device different from the device that receives each received radio signal.

[0316] In some embodiments, the CSI is reported to another device (a sensing initiator in 802.11bf).

[0317] Section B22. The system described in Section B21, wherein CI is reported (or transmitted) from one device to another.

[0318] Item B23. The system of item B1, further configured to detect changes based on comparison and similarity, wherein one or more data processors are configured to detect changes.

[0319] In some embodiments, the transmitter and receiver may be located within the same device.

[0320] Item B24. The system of item B1, further comprising the same device comprising one of the transmitters and one of the receivers.

[0321] In some embodiments, the user may input sensing-related information.

[0322] Item B25. The system of item B1, further configured to enable a user to provide user-provided information and to calculate at least one of the calculated items based on the user-provided information and similarity.

[0323] Section B26. The system of Section B25, wherein the user-provided information comprises at least one of a first identifier associated with a transmitter, a second identifier associated with a receiver, and a service set identifier (SSID) associated with a wireless network access point within the venue.

[0324] Section B27. The system of Section B1, wherein one or more data processors are further configured to determine the identifier of a transmitter or receiver.

[0325] Section B28. The system described in Section B25, wherein user-provided information includes a location or arrangement identifier.

[0326] In some embodiments, there are multiple transmitters having the same receiver (many-to-one), the same transmitter having multiple receivers (one-to-many), and multiple transmitters having multiple receivers (many-to-many).

[0327] Section B29. A system of Section B1, further configured such that at least one of the following is true: at least one transmitter is the same transmitter, at least one receiver is the same receiver, or at least one radio channel is the same radio channel associated with the same radio network.

[0328] In some embodiments, multicast, broadcast, unicast, and trackerbots can be carried by and move with each mobile object.

[0329] Item B30. The system of item B29, wherein one or more data processors are further configured such that at least one transmitter is the same transmitter that transmits at least one radio signal in a unicast, multicast, or broadcast manner, and each of at least one receiver is the respective terminal device to be carried by each user to determine the location of each user.

[0330] In some embodiments, there are many Tx and one Rx.

[0331] Item B31. The system of item B29, wherein one or more data processors are further configured such that at least one receiver is the same receiver and each of at least one transmitter is a terminal device to be carried by each user in order to locate each user's location.

[0332] Item B32. The system of item B1, wherein one or more data processors are configured using at least one of the system's software or firmware.

[0333] In some embodiments, confidence levels may be calculated (for example, to qualify a Tx / Rx / CI / system).

[0334] Item B33. The system of item B1, further configured such that one or more data processors calculate a confidence score associated with at least one of the calculated items.

[0335] In some embodiments, confidence can be calculated in the form of "accuracy" (for example, for a sensing task).

[0336] Item B34. The system of item B1, wherein one or more data processors are further configured to calculate precision based on CI.

[0337] In some embodiments, the size of the CI can be used to calculate similarity.

[0338] Item B35. The system of item B1, wherein one or more data processors are further configured such that similarity is calculated based on the amplitude of CI.

[0339] In some embodiments, the phase of the CI may be used to calculate similarity.

[0340] Item B36. The system of item B1, wherein one or more data processors are further configured such that similarity is calculated based on the phase of CI.

[0341] In some embodiments, it may be determined that the amounts of CI are combined to form a composite CI.

[0342] Item B37. The system of item B2, further configured such that one or more data processors determine the number of individual CIs to be included in the composite channel response in order to achieve a desired accuracy.

[0343] In some embodiments, a “preferred” (e.g., default) amount of CI is combined to form a composite CI.

[0344] Item B38. The system of item B2 is preferably such that several individual CIs are included in the composite channel response.

[0345] Item B38b. A system according to item B2, wherein several individual CIs associated with each transmitter and each receiver are preferred.

[0346] The system of item B38c. item B38b, wherein the number of individual CIs and their respective transmitters is preferred.

[0347] The system according to item B38d. and item B38b, wherein the number of individual CIs and their respective receivers is preferred.

[0348] In some embodiments, each CI is associated with a Tx antenna and an Rx antenna.

[0349] Section B39. The system of Section B1, where each CI is determined for the pairing of the transmitter's transmitting antenna and the receiver's receiving antenna.

[0350] In some embodiments, feature values ​​(e.g., magnitude, phase, space-time information (STI), motion information (MI)) may be calculated based on CI.

[0351] Item B40. The system of item B1, wherein one or more data processors are further configured to determine feature values ​​based on mathematical functions performed on CI and previous CIs, and to perform calculations on at least one of the items calculated based on the feature values.

[0352] In some embodiments, dimensionality reduction of the CI may be performed before the feature values ​​are calculated.

[0353] Item B41. The system of item B40, wherein one or more data processors are further configured to determine the feature values ​​of the CI based on dimensionality reduction of the CI.

[0354] In some embodiments, feature values ​​may be calculated based on similarity scores.

[0355] Item B42. The system of item B40, wherein one or more data processors are further configured to determine feature values ​​based on the similarity between a CI and a previous CI.

[0356] In some embodiments, multiple feature values ​​may exist. These can be normalized.

[0357] Item B43. The system of item B1, wherein one or more data processors are further configured to determine a set of feature values ​​based on a mathematical function executed on a CI and a previous set of CIs, normalize the set of feature values, and perform at least one operation based on the normalized set of feature values.

[0358] In some embodiments, Tx and Rx are housed in their respective devices.

[0359] Item B44. The system of item B1, wherein the transmitter is housed in a first device and the receiver is housed in a second device.

[0360] In some embodiments, Tx (e.g., a sensing transmitter) can be an AP or a non-AP.

[0361] Section B45. A system according to Section B1, wherein the transmitter is one of a wireless network router, a wireless network access point (AP), a wireless terminal device, or a wireless non-AP terminal device.

[0362] In some embodiments, Rx (e.g., a sensing receiver) can be an AP or a non-AP.

[0363] Section B46. A system according to Section B1, wherein the receiver is one of a wireless network router, a wireless network access point (AP), a wireless terminal device, or a wireless non-AP terminal device.

[0364] In some embodiments, Rx (e.g., sensing receiver) may not be associated (e.g., in 802.11bf).

[0365] Section B47. The system described in Section B1, wherein the receiver is not associated with a network router or wireless network access point.

[0366] In some embodiments, the CSI may be sent to the cloud.

[0367] Section B48. The system of Section B1, wherein one or more data processors are further configured to provide CI to another device.

[0368] In some embodiments, the calculation is non-local (for example, in 802.11bf, the CSI is reported from the sensing receiver to the sensing initiator, and the sensing calculation is not performed in the sensing receiver).

[0369] Section B49. The system of Section B48, in which the receiver is part of a first device and one of one or more data processors is part of a second device, and CI is reported from the first device (or receiver) to the second device (or one or more data processors).

[0370] In some embodiments, the CSI is reported in an 802.11bf report frame.

[0371] Item B50.CI is the system of item B49, as reported in the system-generated report.

[0372] In some embodiments, the CSI is reported in the 802.11bf report frame along with the initialization (the 802.11bf sensing measurement initiation phase).

[0373] Item B51. The system described in Item B1, where the system or a service related to the system is initiated.

[0374] In some embodiments, the probe signal transmission is based on a trigger signal (NDP transmission) that is triggered by a trigger frame (TF) or an NDP announcement frame (NDPA) in trigger-based (TB) sensing in 802.11bf.

[0375] Item B52. The system of item B51, wherein the transmission of a radio signal from the transmitter to the receiver of the system is initiated based on a trigger signal.

[0376] Section B53. The system of Section B1, wherein the data processor of the user's user device is configured to provide a user interface (UI) for presenting to the user second information about at least one of the calculated items.

[0377] Item B54. The system according to item B53, wherein the user interface is configured to display a map and allow the user to indicate the location of a CI on the map.

[0378] Section B55. The system of Section B1, wherein one or more data processors are configured to determine a first identifier of a target or object, associate a CI with the first identifier, and store the first identifier together with the CI.

[0379] Item B56. The system of item B1, wherein one or more data processors are configured to acquire predictions and calculate at least one of the calculated items based on the CI and the predictions.

[0380] Item B57. The system of item B56, wherein one or more data processors are configured to compare CIs with predictions and to compute at least one of the items computed based on the comparison.

[0381] Item B58. The system of item B57, wherein the prediction includes a prediction CI.

[0382] Item B59. The system of item B57, wherein the prediction includes coarse-location data received by one or more data processors.

[0383] The system of item B60.Item B57, wherein the prediction comprises coarse position data calculated by one or more data processors based on mapping, the receiver is pre-mapped to a specific position based on mapping, and the more valuable position of the transmitter is calculated based on mapping.

[0384] Item B61. The system of item B60, wherein one or more data processors are configured to determine a coarse position based on at least one of Wi-Fi, Bluetooth, or cellular signals, and to determine the precise position of a transmitter based on the coarse position using a time-reverse positioning system.

[0385] Item B62. The system of item B1, comprising a storage device for storing a 3D rendering of a venue, and a 3D electromagnetic simulator configured to generate multiple channel paths based on data derived from the 3D rendering of the venue.

[0386] Section B63. The system of Section B1, comprising a storage device for storing a three-dimensional model of a venue, constructed using at least one of the ranging techniques such as photographs, images, videos, laser ranging, optical coherence tomography, or echo positioning, wherein the 3D electromagnetic simulator is configured to use the three-dimensional model to generate a database of location-specific estimated CIs within the venue.

[0387] Section B64. A system comprising a receiver configured to receive radio signals transmitted from a transmitter through a radio channel, and one or more data processors configured to determine channel information (CI) based on the received radio signals, wherein the CI includes at least one of channel response, channel impulse response (CIR), channel frequency response (CFR), channel status information (CSI), received signal strength (RSS), or RSS indicator (RSSI), running a 3D electromagnetic simulator to predict multiple channel paths between the transmitter and the receiver, determining predicted CI based on the predicted multiple channel paths between the transmitter and the receiver, (i) training the 3D electromagnetic simulator based on a comparison of CI with predicted CI to produce a trained 3D electromagnetic simulator, or (ii) calibrating the 3D electromagnetic simulator based on a comparison of CI with predicted CI to produce a calibrated 3D electromagnetic simulator, and using the trained or calibrated 3D electromagnetic simulator to generate a database of location-specific estimated CI within a venue, and establishing a mapping between locations within the venue and corresponding CIs.

[0388] Item B65. The system according to item B64, comprising a memory device for storing a 3D rendering of a venue, wherein the 3D electromagnetic simulator is configured to predict multiple channel paths based on data derived from the 3D rendering of the venue.

[0389] Item B66. The system of item B64, comprising a storage device for storing a three-dimensional model of a venue, constructed using at least one of the ranging techniques such as photographs, images, videos, laser ranging, optical coherence tomography, or echo positioning, wherein the 3D electromagnetic simulator is configured to use the three-dimensional model to generate a database of location-specific estimated CIs within the venue.

[0390] Section B67. The system of Section B64, comprising a memory device that stores parameters including the reflectance coefficients of different objects in a venue, wherein the parameters have multiple values ​​to account for one or more environmental factors, including at least one of temperature, humidity, or smog, and a 3D electromagnetic simulator is configured to predict multiple channel paths based on data derived from the parameters.

[0391] Section B68. The system of Section B64, wherein one or more data processors are configured to use a database of location-specific estimated CIs within a venue and a mapping for determining the location of terminal devices within the venue.

[0392] Section B69. The system of Section B64, wherein one or more data processors are configured to receive probe signals from terminal devices, determine CIs based on the received probe signals, and compare the CIs with location-specific estimated CIs in a database to identify the closest match.

[0393] The motion of objects within a venue can be monitored based on TSCI, which is acquired based on radio signals transmitted from a Type 1 device to a Type 2 device. Motion statistics (MS) / motion information (MI) / spatial-temporal information (STI) can be calculated based on TSCI. Analysis values ​​can be calculated based on MS / MI / STI. In many cases, there can be many different possible choices for Type 1 devices. Similarly, there can be many different possible choices for Type 2 devices.

[0394] Different selections of Type 1 devices (or Type 2 devices or both) can cause device-dependent behavior of CI / TSCI / MS / MI / STI / analysis (e.g., linear / affine / nonlinear / monotonic / time-invariant / time-variable / device-dependent / stochastic, shift / mapping / distortion / systematic variation) (e.g., large motion may result in a first MS / MI / STI of approximately 0.9 and a second MS of approximately 0.75, or NIL / no motion may result in a first MS / MI / STI of approximately 0.1 and a second MS of approximately 0.3). Device dependence of behavior is undesirable. In some embodiments, device-dependent compensation is disclosed to "undo" or compensate for device-dependent behavior to make it independent and similar to some criterion / desirable / standard behavior. Undesirable behavior / mapping may be similar to / include / modeled as one of the following: shift, linear / affine / piecewise linear / nonlinear mapping, monotonic mapping, monotonically increasing (or non-decreasing) mapping, monotonically decreasing (or non-decreasing) mapping, etc.

[0395] For example, the choice of Type 1 or Type 2 device may include smartphone speakers, thermostats, TVs, soundbars, DVD players, streaming devices, lamps, light bulbs, plugs, doorbells, cameras, routers, etc. The choice of Type 1 or Type 2 device may also include different brands / models / packaging / parts / components (e.g., Amazon Echo, Echo Dot, Echo Dot generation 1, Echo Dot gen2, Echo Dot gen3, Echo Show, Google Nest, Nest Mini, Nest Hub, or others). A specific model of a specific brand (e.g., Echo) Even for Dot, different devices can still be different (e.g., due to variations / defects / wear / degradation / depreciation in circuit components / parts / amplifiers / antennas / chips / wiring / manufacturing / assembly / production / firmware / software). In particular, there can be different antenna counts and / or antenna types (e.g., with different gains, types, sizes, directivity, and radiation patterns). All of these can cause undesirable behavior in CI / TSCI / MS / MI / STI / analysis. Also, different bands (e.g., 2.4GHz, 5GHz, 6GHz) or different bandwidths (e.g., 20 / 40 / 80 / ) used for radio signals. Undesirable behavior may occur due to the frequency bands (160 / 320 / 640 / 1280MHz). Undesirable behavior may also occur from different amounts of antennas (e.g., an echo has three antennas, but an echo network point has two) or different antenna choices (e.g., dipoles, microstrips, slot antennas, waveguides, spiral antennas, omnidirectional antennas, directional antennas, etc.). Undesirable behavior may also result from different device / device antenna configurations, different transmit power of radio signals, different amplifier sets, different digital / analog gains, different object configurations, or environmental factors such as mechanical / electrical / electromagnetic interference.

[0396] In some embodiments, compensation may be applied to compensate for undesirable behavior. In some embodiments, MS / MI / STI / analysis may be compensated based on compensation to obtain / give / obtain a compensated MS / MI / STI / analysis. One or more separate / each compensation may be applied. After compensation, all compensated MS / MI / STI / analysis for any selection of Type 1 / Type 2 devices may have similar “target behavior” and take similar values ​​in response to the same motion of an object, regardless of the selection of Type 1 / Type 2 devices. Similar target behavior may be a “reference” (or “representative” or “general”) behavior related to a reference / representative / general Type 1 / Type 2 device and may be a user-preferred / predefined / user-specified / user-specified behavior (e.g., MS / MI / STI is around 0.8 for large motion and around 0.2 for NIL / no motion).

[0397] In some embodiments, user-defined / designed / specified / preferred behaviors may be programmable / adjustable. These may be specified by the user in the system configuration. They may be specified once and remain unchanged, or they may change over time. In particular, they may change adaptively based on strategies / time tables / user interventions / user real-time adjustments, or perhaps in response to events / circumstances specified in the system configuration.

[0398] In some embodiments, each Type 1 device (or Type 2 device) may be tested / calibrated / trained in a controlled manner (e.g., in a test / controlled environment, in test settings and procedures, for a test task) to compute / acquire / train each compensation or each compensation sequence. Any compensation may be pre-processing / processing / post-processing applied before / after the computation of MS / MI / STI / analysis. In the case of CI compensation, the compensation may be applied to CI, CI features, CI groups / sliding windows, TSCI, a certain number of TSCIs, a certain number of TSCIs associated with a particular antenna of a Type 1 device, or a certain number of TSCIs associated with a particular antenna of a Type 2 device. For intermediate value compensation, the compensation may be applied to intermediate values ​​(or a number thereof), which may be computed based on the CI / CI features / sliding windows of the CI / TSCI, the compensated CI / CI features / sliding windows of the CI / TSCI, MS / MI / STI, and / or the compensated MS / MI / STI. For MS / MI / STI compensation, compensation can be applied to the MS / MI / STI / analysis (or any of them) that can be calculated based on the CI / CI feature / sliding window of CI / TSCI, the compensated CI / CI feature / sliding window of CI / TSCI, the midpoint, and / or the compensated midpoint. For analytical compensation, compensation can be applied to the analysis that can be calculated based on TSCI, the compensated CI / CI feature / sliding window of CI / TSCI, MS / MI / STI, the compensated MS / MI / STI, the midpoint, and the compensated midpoint. In some embodiments, each compensation sequence may include any of the above compensations. The compensation can be adaptive, time-varying, or time-invariant. Compensation may vary for different frequency bands (e.g., 2.4GHz, 5GHz, 6GHz, 24GHz, 60GHz, 77GHz) or bandwidths (e.g., 20MHz, 40MHz, 80MHz, 160MHz, 320MHz, 640MHz, 1280MHz) of radio signals.

[0399] In some embodiments, compensation may include the following mappings: univariate / bivariate / multivariate mapping, real / imaginary component mapping, magnitude / phase mapping, feature mapping, monotonic mapping, piecewise monotonic mapping, monotonically increasing / decreasing mapping, monotonically non-decreasing / non-increasing mapping, piecewise linear mapping, linear mapping, DE adjustment, affine mapping, compound mapping, and mapping of mappings. Mappings may be time-varying (e.g., adaptively modified) or time-invariant. Mappings may be spatially variable or spatially independent. Mappings may / may not vary with respect to frequency band / bandwidth. Mappings may be the same or different for different sensing tasks (e.g., motion detection, respiration detection / estimation, fall detection, presence detection, walking / gesture detection / estimation, etc.).

[0400] In some embodiments, compensation may be calculated for a pair of Type 1 and Type 2 devices for a wireless sensing task during the calibration / adjustment / customization / factory testing / pre-shipment testing / training phase of the pair of Type 1 and Type 2 devices for the wireless sensing task. (In some embodiments, the Type 1 device may need to be calibrated / representative / customized / trained, and the Type 2 device may be a reference / representative / representative / Type 2 device, and the Type 1 device may need to be calibrated / customized / tested / trained, so that the calculated compensation for the Type 1 device for the wireless sensing task is performed.) In some embodiments, a time-series training / calibration radio signal (e.g., training sounding signal) is transmitted from the calibrated Type 1 device (e.g., Type 1 device or reference / representative / Type 1 device) to the calibrated Type 2 device (e.g., Type 2 device or reference / representative Type 2 device) through the training / calibration radio multipath channel of the training / calibration venue, where the radio calibration multipath channel is affected by the training / calibration operation of the training / calibration object during the training / calibration period. The training / calibration radio signal may span some or all possible bands of the radio (sounding) signal. The training / calibration TSCI can be obtained / extracted from the received time series of the training / calibration radio signal. The training / calibration MI / MS / STI for radio sensing tasks can be calculated based on the training / calibration TSCI.

[0401] In some embodiments, compensation may be calculated based on the behavior of the training CI / TSCI / MI / MS / STI / analysis / intermediate values. The behavior may include any of the following: MS / CI / STI / analysis domain behavior, temporal / frequency transformation / analysis domain behavior, magnitude / phase / component behavior, histogram, restricted histogram, conditional histogram, histogram between two timestamps, two time / frequency / transformation / projection domain stamps (i.e., range within the time / transformation / projection domain), histogram between local maximum and local minimum values, histogram between local maximum and local minimum values ​​above a threshold, filtered / processed local maximum and local minimum values ​​(e.g., low / high / median filtering), statistics, trimmed statistics, primary / quadratic statistics, weighted average, arithmetic mean, geometric mean, harmonic mean, median, mode, maximum, minimum, zero crossing, percentile, range, variance, magnitude, phase, etc. Statistical behavior can be compared to target behavior (e.g., target statistical behavior, target time / frequency / transformation / projection domain behavior, target CI / TSCI / MS / MI / STI / analysis behavior). Target behavior can be associated with target MS. Target behavior can be the behavior of target MS. Target behavior can be user-preferred / predefined / user-defined / user-designed / user-specified / user-selected / user-selected behavior (e.g., specified / provided / designed / defined / input / selected / chosen by a user using some user interface on a user device). The target behavior may be learned from one or more "reference" Type 1 devices and / or one or more "reference" Type 2 devices (e.g., in a "pre-calibration" / reference behavior generation / learning procedure / stage), and one or more reference sensing tasks (e.g., sensing tasks) are performed by transmitting one or more time series of reference radio (sounding) signals from the Type 1 device to the Type 2 device through one or more reference radio channels in one or more reference venues, the reference radio channels being influenced by the reference motion of a reference object. One or more "reference" TSCIs may be obtained / extracted from the received time series of reference radio sounding signals.Reference MS / MI / STI can be calculated based on reference TSCI. Reference analysis can be calculated based on reference MS / MI / STI. The resulting behavior (or aggregate / mean / characteristic / clustered behavior) can be used as target behavior. In some embodiments, the time series of the radio sounding signal can use the 40 MHz bandwidth. The time series of the reference radio sounding signal can cover / scan / use some or all of the possible 40 MHz bandwidth.

[0402] In some embodiments, compensation may be a mapping that can be estimated or obtained by lookup. In the case of an estimated mapping, compensation may be a mapping that can be estimated from a comparison of the behavior and the target behavior such that, when the mapping is applied, the behavior ("compensated behavior") matches / approaches / resembles the target behavior. In some embodiments, the behavior (and target behavior) may be a univariate histogram of the magnitude of the MS (e.g., a marginal distribution or a cumulative distribution). The mapping may map the MS values ​​to the compensated MS values ​​such that the behavior (univariate histogram) of the compensated MS values ​​matches (or approximates or resembles) the target behavior (target univariate histogram).

[0403] In some embodiments, several "control points" can be determined for the MS univariate histogram and the target univariate histogram. Control points may include the mean (mean of MS, or mean of target MS), mode, median (median of MS), percentile (percentile value of MS), 0%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or any other percentile. Each pair of corresponding control points in the MS histogram and target histogram may / may form an ordered pair (x,y) or point on an xy plot (or function y=f(x)), where x is the MS value and y is the target MS value. For example, (10th percentile of MS, 10th percentile of target MS) is an ordered pair that constitutes a “control point” on an xy plot. A straight line (or polynomial line or piecewise spline) is monotonic non-decreasing (or monotonically increasing). A mapping y=f(x) can be used to connect control points so that a mapping y=f(x) is obtained. Compensation can be a mapping y=f(x) obtained by connecting control points using some kind of monotonic line (e.g., a straight line, a quadratic function, a cubic function, a polynomial function, an exponential function, a logarithmic function, a convex function, a concave function, etc.). In this way, the control points are in / on the mapping function y=f(x). In some embodiments, line fitting (e.g., least squares regression, linear regression, robust linear regression, orthogonal regression, Deming regression, major axis regression, split regression, polynomial regression, regression dilution) can be applied to obtain / calculate the mapping for compensation. Line fitting can be applied piecewise. The resulting mapping can be monotonically increasing or non-increasing. In this way, there may be one or more control points that are not in / on the mapping.

[0404] In some embodiments, there may be two control points, a 0% point and a 100% point, and all MS values between the 0% MS point and the 100% MS point may be mapped to the 0% target MS point and the 100% target MS point. The line may be a straight line, or a quadrilateral, or a cube, or a polynomial, or other monotonic line, or a regression line. Or, they may be 1% point and 99% point, and thus essentially all MS values are mapped to the target MS values. Outside the range of the 1% point and the 99% point, the mapping may be the same function (i.e., y = f(x) = x). In some embodiments, there may be three control points such as {0% point, 50% point (median), and 100% point}, or {1% point, 50% point, 99% point}, or {0% point, 40% point, 100% point}, or {1% point, 30% point, 99% point}, etc. In some embodiments, there may be four control points such as {0% point, 30% point, 70% point, 100% point}, or {1% point, 20% point, 50% point, 99% point}, etc.

[0405] In some embodiments, for classifying (or quantizing) MS into N classes (e.g., two classes of "MOTION" and "NO MOTION"), classification (or quantization) may be applied to MS based on the univariate histogram of MS. Clustering / training may be performed on the histogram of MS to calculate MS or N - 1 thresholds T_1, T_2,..., T_{N - 1}, and as a result, any MS < T_1 (MS in region 1) may be classified / quantized as class 1, T_1 < MS < T_2 (MS in region 2) may be class 2,..., T_{N - 1} < MS (MS in region N) may be class N. And N - 1 "control points" may be obtained based on the N - 1 thresholds and corresponding percentile values. Two control points of 0% point and 100% point can also be added. Using the above method, the compensation may be the mapping y = f(x) obtained by connecting / fitting / regressing the control points using some kind of monotonic line.

[0406] In some embodiments, the center of gravity value can be calculated for each determination / quantization region (i.e., MS < T_1 is region 1, T_1 < MS < T_2 is region 2, ···, T_{N - 1} < MS is region N). The N "control points" may be obtained based on the N center of gravity values. Two control points, the 0% point and the 100% point, can also be added. Using the above method, the compensation can be a mapping y = f(x) obtained by connecting / fitting / regressing the control points using some kind of monotonic line.

[0407] In some embodiments, the cumulative distribution F1 of MS can be obtained. The cumulative distribution F2 of the target MS is also obtained. The compensation can be a mapping y = f(x) = F2^{-1}[F1(x)], where the function is followed by F2^{-1}, which is the inverse function of F2. In some embodiments, the function F2^{-1}[F1(x)] can be implemented using a table lookup. However, it is complex and may require considering operations. Therefore, an alternative embodiment is to approximate the function F2^{-1}[F1(x)] with a simpler function such as a linear function, an affine function, a piecewise linear function, a polynomial, a piecewise polynomial, a spline, a regression function, etc. The simpler function can be used as a mapping for compensation.

[0408] In some embodiments, the behavior can be a bivariate or multivariate histogram / distribution. Similar to the above, the control points may be determined for the bivariate / multivariate distribution. Three or four or more control points can be connected by a plane, a hyperplane, a surface, or a manifold. The compensation (or mapping) can be a set of planes / hyperplanes / curves / manifolds.

[0409] In some embodiments, for a multivariate distribution (e.g., N variables), the MS vector is an N-tuple X=[X_1,X_2,...,X_N], the target MS vector is an N-tuple T=[T_1,T_2,...,T_N], and the mapped / compensated MS vector is Y=[y_1,y_2,...,y_N]. The cumulative distribution function F1 of the MS (i.e., the MS vector X) and the cumulative function F2 of the target multivariate distribution (i.e., the cumulative function F2 of the target MS vector T) can be obtained. The compensation can be a mapping Y=f(X)=F2^{-1}[F1(X)], or an approximation of the mapping using a plane, hyperplane, surface, or manifold. In some embodiments, for N=2 (bivariate distribution), [y_1,y_2]=f(x_1,x_2)=F2^{-1}[F1(x_1,x_2)]. y_1 can be obtained by y_1 = f1(x_1) = F2'^{-1}[F1'(x_1)], where F1' is the univariate cumulative distribution of x_1 and F2' is the univariate cumulative distribution of T_1. Then, y_2 = f2(x_2) = F2''^{-1}[F1''(x_2)], where F1'' is the univariate cumulative distribution of x_2 and F2'' is the univariate cumulative distribution of T_2. In other words, X_ and X_2 can be processed / treated independently to give Y_1 and Y_2. In some embodiments, y_1 is calculated as y_1=F1(x_1)=F2'^{-1}[F1'(x_1)], and then y_2 is calculated by y_2=f3(x_1,x_2)=F2'''^{-1}[F1(x_1,x_2)], where F2''' is the univariate cumulative distribution of T_2 given T_1=y_1=F1(x_1)=F2'^{-1}[F1'(x_1)]. In some embodiments, y_2 is calculated as y_2=f2(x_2)=F2''^{-1}[F1''(x_2)], and then y_1 is calculated / obtained by y_1=f4(x_1,x_2)=F2''''^{-1}[F1(x_1,x_2)], where F2'''' is the univariate cumulative distribution of T_1 given T_2=y_2=f2(x_2)=F2''^{-1}[F1''(x_2)].In some embodiments, for an N-variate distribution, the first component y_{i1} may be calculated first based on x_{i1}, where i1 is the component index of the first component. Then, given T_{i1}=y_{i1}, the second component y_{i2} may be calculated based on x_{i2}. Then, given T_{i1}=y_{i1} and T_{i2}=y_{i2}, the third component y_{i3} may be calculated based on x_{i3}, and so on. And given T_{i1}=y_{i1}, T_{i2}=y_{i2},...,T_{i(N-1)}=y_{i(N-1)}, the Nth component y_{iN} may be calculated based on x_{iN}.

[0410] In some embodiments, the target behavior (e.g., a histogram of MS) ​​may not be fully specified by the user. Instead, the user may specify that the number of classes (e.g., MS) is N, and that the target values ​​(e.g., target MS values) are representative of each of the N classes (e.g., y_1, y_2, ..., y_N). To estimate the mapping for compensation (e.g., compensation / mapping of MS), (1) perform classification and classify (e.g., MS) into N classes, (2) calculate N-1 thresholds T_1, T_2, ..., T_{N-1}, calculate N decision regions (e.g., MS, C_1, C_2, ..., C_N), each of which is the centroid of its respective decision region (e.g., MS), (3) calculate N centroids, (4) construct N control points based on the N centroids and N representative target values, the control points being (C_1, y_1), (C_2, y_2), ..., (C_N, y_N), (5) the 0% point MS and the 100% point MS, and (6) using the above method, the compensation is the mapping y=f(x) connecting the control points using some monotonic line.

[0411] In some embodiments, compensation (or associated mapping / function) may be computed (e.g., based on artificial intelligence (AI), machine learning, deep learning, neural networks (e.g., CNN, RNN, feedforward network, attention-based network, Transformer network, or any combination)). Supervised learning, unsupervised learning, and semi-supervised learning may be applied to learn the compensation / mapping / function. The input to the compensation may be any (or any feature / size / phase / function / statistic / behavior / histogram) of CI, IV, MI / MS / STI, analytics, TSCI, TSMI, TSA. The output of the compensation may be another (i.e., "compensated") CI, IV, MI / MS / STI, analytics, TSCI, TSMI, TSA (or the corresponding feature / size / phase / function / statistic / behavior / histogram). The training target (i.e., training criterion) may be another CI, IV, MI / MS / STI, analysis, TSCI, TSMI, TSA (or corresponding feature / magnitude / phase / function / statistic / behavior / histogram), which may be obtained in several pre-training / pre-calibration stages / phases. Compensation / mapping / functions can be trained using an objective / cost / loss function (e.g., similarity / norm / score / difference / distance / variance / proportion / variance / variance / deviation / TRRS / correlation / covariance / autocorrelation / cross-correlation / inner product / mean square / absolute error) between the training target (e.g., target behavior) and the output (e.g., output behavior). The objective / cost / loss function may be optimized / minimized / maximized under / below certain constraints to find the best / optimized compensation / mapping / function. The objective / cost / loss function may be parameterized, and optimization / maximization / minimization may be with respect to a set of parameters.

[0412] In some embodiments, for the purpose of exploration, compensation may be computed by performing a search (e.g., exhaustive search, fast search, reduced search). Several candidate mappings may be determined and provisionally applied to obtain / calculate their respective compensated behaviors. Distances or similarity / dissimilarity scores may be computed between the target behavior and each compensated behavior (e.g., "behavioral distance", e.g., histogram distance, vector norm, city block distance, Euclidean distance, crossing distance, Bhattacharya distance (B-distance), Kullback-Leibler distance (KL-distance)). The candidate mapping that gives the best match (e.g., minimum distance or maximum similarity score) may be selected as the compensation. Multistage search may be applied. For example, several candidate first mappings (e.g., mappings applied to CI) may be determined. Then, for each candidate first mapping, several candidate second mappings (e.g., mappings applied to MS or analysis values) may be determined. Both candidate first and candidate second mappings may provisionally apply to obtain / calculate their respective compensated behaviors. The distance / similarity scores between the target behavior and each compensated behavior may be calculated such that the candidate first mapping and the candidate second mapping, which together give the best match (e.g., minimum distance or maximum similarity score), can be selected as the respective compensations. In some embodiments, inference and search may be applied together. For example, several candidate first mappings (e.g., to CI) may be determined and provisionally applied. Then, for each candidate first mapping, a second mapping (e.g., to MS) may be estimated. Both the candidate first mappings and the respective estimated second mappings may be provisionally applied to obtain / calculate the respective compensated behaviors. The distance / similarity scores between the target behavior and each compensated behavior may be calculated such that the candidate first mapping and the respective estimated second mapping, which together give the best match (e.g., minimum distance or maximum similarity score), can be selected as the respective compensations.

[0413] In some embodiments, two or more MS / MI / STI / analyses may be calculated for the same task or for different tasks (e.g., a first for motion detection, a second for respiration detection, a third for fall detection, etc.) for different compensation for different MS / MI / STI / analyses. In some embodiments, the MI may be an MS / MI / STI calculated based on TSCI (or a compensated TSCI).

[0414] The following numbered sections provide examples of compensated wireless monitoring.

[0415] Item C1. Time / device / system / software of a compensated wireless monitoring system, wherein a Type 1 heterogeneous wireless device of a wireless sensing system transmits a wireless signal through a venue's wireless multipath channel, the wireless multipath channel being affected by the motion of an object in the venue, and a Type 2 heterogeneous wireless device of the wireless sensing system receives a wireless signal through the wireless multipath channel, the received signal being different from the transmitted wireless signal due to the venue's wireless multipath channel and the motion of an object. The process includes: using a processor, memory, and a set of instructions to obtain a time series of channel information (CI) of a radio multipath channel based on a received radio signal; calculating time series motion information (MI) based on the time series CI (TSCI); calculating time series analysis values ​​based on the time series MI (TSMI); and calculating compensated time series analysis values ​​by applying compensation to the calculation of time series analysis values ​​(TSA), wherein the compensation includes monotonic mapping; and monitoring motion based on the compensated time series analysis values.

[0416] Item C2. The method / device / system / software of Item C1, wherein monotonic mapping includes at least one of univariate mapping, bivariate mapping, multivariate mapping, monotonically increasing mapping, monotonically decreasing mapping, monotonically non-increasing mapping, linear mapping, nonlinear mapping, affine mapping, piecewise linear mapping, concave mapping, convex mapping, quadratic mapping, cubic mapping, polynomial mapping, exponential mapping, logarithmic mapping, fitted mapping, regression mapping, spline mapping, function, inverse function, function, function of another mapping, function of mapping, multimapping, parametric mapping, nonparametric mapping, time-varying mapping, or time-invariant mapping.

[0417] Section C3. A method / device / system / software for a compensated wireless monitoring system as described in Section C1 (or 2), wherein the compensation further comprises at least one of the following: a first compensation tailor made for a pair of general type 1 devices and general type 2 devices; a second compensation tailor made for a type 1 device; a third compensation tailor made for a type 2 device; and a fourth compensation tailor made for a pair of type 1 devices and type 2 devices.

[0418] Section C4. A method / device / system / software for a compensatory wireless monitoring system in Section C3 (or 1 or 2), further comprising monotonic mapping comprising at least one of: a first monotonic mapping tailor made for a pair of general type 1 devices and general type 2 devices; a second monotonic mapping tailor made for a type 1 device; a third monotonic mapping tailor made for a type 2 device; and a fourth monotonic mapping tailor made for a pair of type 1 devices and type 2 devices.

[0419] In some embodiments, monotonic mapping may be applied to CI, median (IV), MI, and / or analytical values.

[0420] Section C5. A method / device / system / software for a compensated radio monitoring system in Section C1 (or 2 or 3 or 4), wherein compensation or monotonic mapping is applied to at least one of the following: CI, a number of CIs, a number of CIs in a sliding time window, a number of CIs in a TSCI, a TSCI, another TSCI acquired based on another radio signal received by a Type 2 device, another TSCI acquired based on yet another radio signal transmitted by a Type 1 device, a feature of a CI, the magnitude of a CI, the phase of a CI, the components of a CI, a number of components of a CI, the magnitude of the components of a CI, or the phase of the components of a CI, and is applied to at least one of calculating a time series of compensated MIs based on all compensated CIs, compensated features of CIs, and compensated components of CIs, and calculating analytical values ​​of the compensated time series based on the time series of compensated MIs.

[0421] In some embodiments, a monotonic mapping may be applied to an intermediate value (IV) between TSCI and TSMI. IV may be calculated based on TSCI, and TSMI may be calculated based on IV.

[0422] Section C6. A method / device / system / software for a compensated wireless monitoring system in Section C1 (or 2 or 3 or 4), wherein compensation or monotonic mapping is applied to at least one of the following: intermediate values ​​(IVs) calculated based on TSCI, a number of IVs, a number of IVs in a sliding time window, features of IVs, magnitude of IVs, phase of IVs, components of IVs, a number of components of IVs, magnitude of components of IVs, or phase of components of IVs; and at least one of the following: calculating a time series of compensated MIs based on the compensated features of all compensated IVs, the compensated features of IVs, and the compensated components of IVs; and calculating an analytical value of a compensated time series based on the time series of compensated MIs.

[0423] In some embodiments, a monotonic mapping may be applied to an intermediate value (IV) between TSMI and TSA. IV may be calculated based on TSMI, and TSA may be calculated based on IV.

[0424] Section C7. A method / device / system / software for a compensatory wireless monitoring system in Section C1 (or 2 or 3 or 4), wherein compensation or monotonic mapping is applied to at least one of the following: intermediate values ​​(IVs) calculated based on TSMI, a number of IVs, a number of IVs in a sliding time window, features of an IV, magnitude of an IV, phase of an IV, components of an IV, a number of components of an IV, magnitude of a component of an IV, magnitude of a component of an IV, phase of a component of an IV, or phase of a component of an IV.

[0425] In some embodiments, monotonic mapping can be applied to MI.

[0426] Section C8. A method / device / system / software for a compensated wireless monitoring system in Section C1 (or 2 or 3 or 4), further comprising monitoring motion based on all compensated MIs, compensated features of MIs, and compensated components of MIs, wherein compensation or monotonic mapping is applied to at least one of MIs, a number of MIs, a number of MIs in a sliding time window, a number of MIs in a TSMI, features of a TSMI, magnitude of MIs, magnitude of MIs, phase of MIs, a number of components of MIs, magnitude of components of MIs, or phase of components of MIs.

[0427] In some embodiments, monotonic mapping can be applied to TSA.

[0428] Section C9. Methods / devices / systems / software for compensatory wireless monitoring systems in Section C1 (or 2 or 3 or 4), wherein compensation or monotonic mapping applies to at least one of the following: analysis, several analyses, several analyses in a sliding time window, several analyses in a TSA, TSA, features of the analysis, magnitude of the analysis, phase of the analysis, components of the analysis, number of components of the analysis, magnitude of the components of the analysis, or phase of the components of the analysis.

[0429] In some embodiments, the monotonic mapping can be modified over time.

[0430] Section C10. A method / device / system / software for a compensatory wireless monitoring system in Section C1, further comprising modifying compensation or monotonic mapping based on at least one of the following: changes in target behavior, changes in wireless signals, changes in wireless signal bandwidth, specifications, system settings, user input, circumstances, events, time tables, strategies, and plans.

[0431] In some embodiments, compensation may be computed / learned / derived / trained during a calibration phase (past, present, real-time, or future).

[0432] Section C11. A method / device / system / software for a compensatory wireless monitoring system as described in Section C1(to C10), comprising, in a calibration stage, transmitting a calibration radio signal from a calibration type 1 heterogeneous wireless device through a calibration radio multipath channel of a calibration venue, wherein the calibration radio multipath channel is affected by the calibration operation of a calibration object in the calibration venue; receiving a calibration radio signal through the calibration radio multipath channel, wherein the received calibration signal differs from the transmitted calibration radio signal due to the calibration radio multipath channel of the calibration venue and the calibration operation of the calibration object; and further comprising obtaining a time series of calibration CIs of the received calibration radio multipath channel using a calibration processor, calibration memory, and a set of calibration instructions; and determining compensation based on the time series of calibration CIs.

[0433] In some embodiments, the monotonic mapping is determined by comparing the "calibration behavior" with the "target behavior." In some embodiments, after compensation for the monotonic mapping is applied, the objective is to find a compensated or monotonic mapping such that the compensated calibration behavior resembles the target behavior.

[0434] The methods / devices / systems / software for compensatory wireless monitoring systems described in Sections C12 and C11 further include, in the calibration phase, calculating a time series of calibration MI based on a time series of calibration CI, calculating a time series of calibration analysis based on a time series of calibration MI, comparing the behavior associated with the time series of calibration CI, calibration MI, or calibration analysis with the target behavior, and calculating a monotonic mapping based on the comparison between the calibration behavior and the target behavior.

[0435] Section C13. A method / device / software in Section C12, wherein the behavior includes at least one of the following: Statistical behavior, temporal behavior, frequency behavior, transformation domain behavior, projection domain behavior, magnitude behavior, phase behavior, component behavior, histogram, probability distribution, cumulative histogram, characteristic function, moment generation function, cumulative distribution, univariate distribution, univariate histogram, bivariate distribution, bivariate histogram, multivariate distribution, multivariate histogram, restricted distribution, restricted histogram, conditional distribution, conditional histogram, histogram of CI, MI or analytics between two timestamps, histogram of observations related to CI, MI or analytics between two timestamps, histogram of observations related to CI, MI or analytics restricted to between two timestamps, histogram of CI, MI or analytics between two characteristic points of CI, MI or analytics, CI, MI or analytics between two characteristic points of CI, MI or analytics Histogram of observed values ​​related to nalistics, histogram of observed values ​​related to the analysis of processed CI, MI or CI between two characteristic points of the analysis, histogram of observed values ​​related to the analysis of CI, MI or CI between two characteristic points of the analysis, histogram of observed values ​​related to the analysis of CI, MI or CI between two characteristic points with conditions on two characteristic points, statistics, conditional statistics, trimmed statistics, first-order statistics, second-order statistics, higher-order statistics, scatter plot, mean, weighted mean, conditional mean, restricted mean, arithmetic mean, trimmed mean, geometric mean, harmonic mean, mode, median, percentile, maximum value, minimum value, zero crossing, range, magnitude, phase, magnitude of components, phase of components, variance, standard deviation, variance, dispersion, kurtosis, entropy, information, moment, central moment, correlation, correlation coefficient, covariance, autocorrelation function (ACF), autocovariance function, cross-correlation, or cross-covariance, Here, the observable elements related to an item consist of the item, the size of the item, the phase of the item, the characteristics of the item, the components of the item, the size of the components of the item, the phase of the components of the item, the characteristics of the components of the item, or at least one of the above functions. Here, any characteristic point is either a local maximum, a local minimum, or a zero intersection.

[0436] In some embodiments, possible methods for obtaining a monotonic mapping include (1a) searching during the calibration phase, (1b) subtracting during the calibration phase, and (2) subtracting from a user or another device (e.g., a server) or system configuration / description.

[0437] Section C14. A method / device / system / software for a compensatory wireless monitoring system in Section C12, further comprising: (1) being calculated based on exploration or inference in a calibration stage; (2) being obtained from a specification, setting, user input or another device; (3) being calculated based on supervised learning, unsupervised learning or semi-supervised learning; and (4) being obtained by artificial intelligence, machine learning, deep learning or a neural network.

[0438] In some embodiments, possible methods for obtaining a monotonic mapping include (1a) exploring during the calibration phase. In some embodiments, trial compensation is performed for each candidate monotonic mapping. After compensation, a similarity score is calculated between the target behavior and the calibration behavior obtained after compensation.

[0439] Section C15. A method / device / system / software for a compensated wireless monitoring system as described in Section C14, further comprising: determining a number of candidate monotonic mappings in the calibration stage; applying each compensation, which includes the candidate monotonic mapping, to the calculation of the TSA for each candidate monotonic mapping; calculating each similarity score between the target behavior and the resulting behavior related to the time series of the calibration CI, calibration MI, or calibration analysis after the application of each compensation; and selecting a monotonic mapping as the candidate monotonic mapping with the highest similarity score.

[0440] In some embodiments, possible methods for obtaining a monotonic mapping include (1b) subtraction during the calibration stage.

[0441] Item C16. A method / device / system / software of a compensated wireless monitoring system in item C14, further comprising, in a calibration phase, estimating a monotonic mapping based on a comparison between a target behavior and a behavior related to the time series of calibration CI, calibration MI, or calibration analysis.

[0442] In some embodiments, N control points can be obtained and a monotonic mapping can be estimated based on the N control points.

[0443] Item C17. A method / device / software of a compensated wireless monitoring system in item C16, comprising, in a calibration phase, determining the behavior of a univariate observable X based on the time series of calibration CI, the time series of calibration MI, or the time series of calibration analysis, the behavior including N scalar values {x_1, x_2,... x_N} where x_1 < x_2 <... < x_N, and determining the target behavior of the univariate observable X, the target behavior including N scalar values {y_1, y_2,... y_N} where y_1 < y_2 <... < y_N, and defining N control points of the monotonic mapping by mapping the N scalar values of x to the N scalar values of y, the control points being (x_1, y_1), (x_2,, y_2),...,(x_N, y_N), and further comprising estimating a monotonic mapping based on the N control points.

[0444] In some embodiments, the N control points can be connected in a line (continuous).

[0445] Item C18. A method / device / system / software of a compensated wireless monitoring system in item C17, further comprising, in a calibration phase, estimating the monotonic mapping as a monotonic line connecting the N control points.

[0446] In some embodiments, there are possible lines for connecting control points.

[0447] Section C19. A method / device / system / software for a compensatory wireless monitoring system in Section C18, wherein the monotonic line includes at least one of the following: linear map, quadratic map, cubic map, affine map, polynomial map, exponential map, logarithmic map, convex map, concave map, spline map, piecewise linear map, piecewise quadratic map, piecewise cubic map, monotonically increasing map, and monotonically non-decreasing map.

[0448] In some embodiments, curve fitting may be used instead (some control points may not lie on the fitted curve).

[0449] Section C20. A method / device / system / software for a compensatory wireless monitoring system in Section C17, further comprising estimating a monotonic mapping as a curve that fits N control points according to a conformance criterion.

[0450] In some embodiments, the curve fitting may be least-squares fitting.

[0451] Section C21. Method / device / system / software for a compensated wireless monitoring system in Section C20, further comprising fitting a curve to N control points according to a least-squares fitting criterion.

[0452] In some embodiments, curve fitting can be achieved by regression.

[0453] Section C22. A method / device / system / software for a compensated wireless monitoring system in Section C17, further comprising the fact that a monotonic mapping is estimated by applying regression, linear regression, robust linear regression, orthogonal regression, Deming regression, long-axis regression, separable regression, polynomial regression, or regression dilution to N control points.

[0454] In some embodiments, there are possible choices of control points.

[0455] Section C23. A method / device / system / software for a compensatory wireless monitoring system in Section C17, wherein N control points include at least one of the following: (Mean of X, Mean of Y), (Weighted mean of X, Weighted mean of Y), (Trivalent mean of X, Trivalent mean of Y), (Arithmetic mean of X, Arithmetic mean of Y), (Geometric mean of X, Geometric mean of Y), (Harmonic mean of X, Harmonic mean of Y), (Median of X, Median of Y), (Mode of X, Mode of Y), (Percentile of X, Percentile of Y), (0th percentile of X, 0th percentile of Y), (1st percentile of X, 1st percentile of Y), (5th percentile of X, 5th percentile of Y), (10th percentile of X, 10th percentile of Y), (10th percentile of X, 10th percentile of Y), (20th percentile of X) (20th percentile of Y), (30th percentile of X, 30th percentile of Y), (40th percentile of X, 40th percentile of Y), (50th percentile of X, 50th percentile of Y), (60th percentile of X, 60th percentile of Y), (70th percentile of X, 70th percentile of Y), (80th percentile of X, 80th percentile of Y), (90th percentile of X, 90th percentile of Y), (95th percentile of X, 95th percentile of Y), (99th percentile of X, 99th percentile of Y), or (100th percentile of X, 100th percentile of Y)

[0456] In some embodiments, monotonic mapping can be directly calculated based on cumulative distribution functions F_X and F_Y.

[0457] Section C24. A method / device / system / software for a compensatory wireless monitoring system as described in Section C16, further comprising: determining the behavior of a univariate observable X related to a CI, MI, or analysis value based on a time series of calibration CI, a time series of calibration MI, or a time series of calibration analysis, wherein the behavior includes a cumulative univariate distribution F_X of X; determining the target behavior of a target observable Y, wherein the target behavior includes a cumulative univariate target distribution F_Y of Y; and estimating a monotonic mapping based on the cumulative univariate distribution F_X and the cumulative univariate target distribution F_Y.

[0458] In some embodiments, monotonic mapping can be directly calculated based on the formulas for cumulative distribution functions F_X and F_Y.

[0459] Section C25. A method / device / system / software for a compensated wireless monitoring system as described in Section C24, further comprising estimating a monotonic mapping as F_Y^{-1}[F_X(X)], where F_Y^{-1} is the reciprocal of the function F_Y.

[0460] In some embodiments, the formulas may be complex, so approximations may be used instead.

[0461] Section C26. A method / device / system / software for a compensated wireless monitoring system in Section C24, further comprising estimating a monotonic mapping as an approximation of F_Y⁻¹(F_X(X)), where F_Y⁻¹ is the reciprocal of the function F_Y, and the approximation includes at least one of a linear map, quadratic map, cubic map, affine map, polynomial map, exponential map, logarithmic map, convex map, concave map, spline map, piecewise linear map, piecewise quadratic map, or piecewise cubic map.

[0462] In some embodiments, for a multivariate observable X, each X (and Y) is a multivariate tuple. N control points can be obtained.

[0463] A method / device / system / software for a compensatory wireless monitoring system as described in Section C27.C16, further comprising: determining the behavior of a multivariate observable X related to a CI, MI, or analysis value based on a time series of calibration CI, a time series of calibration MI, or a time series of calibration analysis, wherein the behavior includes N multivariate tuples {X_1, X_2, ..., X_N}; determining the target behavior of a multivariate target observable Y, wherein the target behavior includes N multivariate tuples {Y_1, Y_2, ..., Y_N}; defining N control points of a monotonic mapping by mapping N multivariate tuples of X to N multivariate tuples of Y, wherein the control points are determined to be (X_1, Y_1), (X_2, Y_2), ..., (X_N, Y_N); and estimating the monotonic mapping based on the N control points.

[0464] In some embodiments, a hyperplane or manifold may be used to connect several control points (piecewise).

[0465] Section C28. A method / device / system / software for a compensatory wireless monitoring system as described in Section C27, further comprising, in the calibration stage, estimating a monotonic mapping as a combination of multiple piecewise hyperplanes connecting some of the N control points.

[0466] In some embodiments, a hyperplane or manifold may be used to fit / approximate the control points (e.g., piecewise).

[0467] Section C29. A method / device / system / software for a compensatory wireless monitoring system as described in Section C27, further comprising, in the calibration stage, estimating a monotonic mapping as a manifold or hyperplane that fits N control points according to a fitting criterion.

[0468] In some embodiments, monotonic mapping can be estimated based on an equation.

[0469] Section C30. The methods / devices / systems / software for compensated wireless monitoring systems in Section C27 further include estimating a monotonic mapping based on the cumulative distribution function of X and the cumulative distribution function of Y during the calibration phase.

[0470] In some embodiments, in the case of multi-stage search, the search can be combined with inference to find the best combination.

[0471] Section C31. A method / device / system / software for a compensated wireless monitoring system in Section C14, further comprising: determining a monotonic mapping including a first monotonic mapping and a second monotonic mapping in the calibration stage; determining and retrieving a plurality of candidate first monotonic mappings; estimating a second monotonic mapping for each candidate first monotonic mapping; applying each compensation, including the candidate first monotonic mapping and the respective estimated second monotonic mapping, to the calculation of the TSA; calculating each similarity score between the target behavior and the resulting behavior related to the time series of the calibration CI, calibration MI, or calibration analysis after the respective compensation has been applied; and selecting a monotonic mapping as the candidate first monotonic mapping and the respective estimated second monotonic mapping having the highest similarity score.

[0472] Section C32. A method / device / system / software for a compensated wireless monitoring system as described in Section C12, further comprising obtaining target behavior from the specification, system settings, user input, server, cloud server, remote server, local server, sensing server, or another device.

[0473] Section C33. Methods / devices / systems / software for compensatory wireless monitoring systems as described in Section C12, further comprising modifying target behavior based on the description, system settings, user input, circumstances, events, time tables, strategies, and plans.

[0474] A method / device / system / software for a compensatory radio monitoring system as described in Section C34.C11, wherein the first bandwidth of the calibration radio signal comprises a second bandwidth of the radio signal, and the first bandwidth of the calibration radio signal includes the second bandwidth of the radio signal.

[0475] Section C35. A method / device / system / software for a compensatory wireless monitoring system as described in Section C11, wherein the calibration object is similar to an object in a first way, and the calibration motion of the calibration object is similar to the motion of the object in a second way.

[0476] Section C36. A method / device / system / software for a compensatory wireless monitoring system in Section C11, further comprising: transmitting a reference radio signal from a reference type 1 heterogeneous radio device through a reference radio multipath channel of a reference venue in a pre-calibration stage prior to a calibration stage, wherein the reference radio multipath channel is affected by the reference motion of a reference object in the reference venue; receiving a reference radio signal through a reference type 2 heterogeneous radio device through the reference radio multipath channel, wherein the received reference radio signal is different from the transmitted reference radio signal due to the reference radio multipath channel of the reference venue and the reference motion of the reference object; acquiring a reference CI of the received reference radio multipath channel using a reference processor, reference memory, and a set of reference instructions; calculating a time-series reference MI based on the time-series reference CI; calculating a time-series reference analysis value based on the time-series reference MI; and calculating target behavior based on at least one of the time-series reference CI, time-series reference MI, or time-series reference analysis value.

[0477] Section C37. A method / device / system / software for a compensatory wireless monitoring system as described in Section C36, wherein the target behavior includes a set of basic behaviors that may occur within the compensatory wireless monitoring system.

[0478] Section C38. A method / device / system / software for a compensated wireless monitoring system as described in Section C11, further comprising: in a recalibration step following a calibration step, calculating a time series of recalibrated MI based on a recalibrated TSCI; comparing the behavior of the time series of recalibrated MI with the behavior of a recalibrated target; calculating an updated monotonic mapping based on the comparison between the behavior of the time series of recalibrated MI and the behavior of a recalibrated target; and replacing the monotonic mapping with the updated monotonic mapping in compensation for the calculation of TSA.

[0479] In some embodiments, monotonic mapping is determined by comparing the "calibration behavior" with the "target behavior".

[0480] Section C39. A method / device / system / software for a compensatory wireless monitoring system as described in Section C11 (or 12), wherein the calibration type 1 device is a reference type 1 device, the calibration type 2 device is a type 2 device, and the compensation and monotonic mapping are adjusted for the type 2 device to compensate for the calculation of any respective TSA based on any respective TSCI obtained from any respective wireless signal received by the type 2 device for motion monitoring.

[0481] Section C40. A method / device / system / software for a compensatory wireless monitoring system as described in Section C11 (or 12), wherein a calibration type 1 device is a type 1 device, a calibration type 2 device is a reference type 2 device, and compensation and monotonic mapping are adjusted to the type 1 device to compensate for the calculation of any respective TSA based on any respective TSCI obtained from any respective wireless signal transmitted by the type 1 device for motion monitoring.

[0482] Section C41. A method / device / system / software for a compensatory wireless monitoring system as described in Section C11 (or 12), wherein a calibration type 1 device is a type 1 device, a calibration type 2 device is a type 2 device, and compensation and monotonic mapping are adjusted for a pair of type 1 and type 2 devices to compensate for the calculation of TSA based on TSCI obtained from a wireless signal transmitted between a pair of devices for motion monitoring.

[0483] Section C42. A method / device / system / software for a compensatory wireless monitoring system in Section C11 (or 12), wherein the calibration type 1 device is a representative type 1 device, and the calibration type 2 device is a representative type 2 device, wherein the compensation and monotonic mapping are customized to compensate for the calculation of each TSA based on any respective TSCI obtained from any each wireless signal transmitted between any pair of devices for motion monitoring, for any pair of general-purpose type 1 devices represented by the representative type 1 device and general-purpose type 2 devices represented by the representative type 2 device, the type 1 device is a general-purpose type 1 device, and the type 2 device is a general-purpose type 2 device.

[0484] In some embodiments, the monotonic mapping is the same for Type 1 devices, Type 2 devices, and radio signals, but it may differ if different analyses are used.

[0485] Section C43. A method / device / system / software for a compensated wireless monitoring system in Section C1, comprising: calculating a second time series analysis value based on a time series of MI; and calculating a compensated second time series analysis value by applying a second compensation to the calculation of the second time series analysis value, wherein the second compensation includes a second monotonic mapping for monitoring motion based on the compensated second time series analysis value.

[0486] In some embodiments, monotonic mapping may be the same for Type 1 devices, Type 2 devices, and radio signals, even if different analyses are used (e.g., compensation applied to CI or MI).

[0487] Section C44. A method / device / system / software for a compensated wireless monitoring system as described in Section C43, wherein the second compensation and the compensation are the same, and the second monotonic mapping and the first monotonic mapping are the same.

[0488] In some embodiments, the monotonic mapping is the same for Type 1 devices and Type 2 devices and the radio signal, but it may differ if different MIs are used.

[0489] Section C45. A method / device / system / software for a compensated wireless monitoring system in Section C1, comprising: calculating a second MI of a time series based on TSCI; calculating a second analysis value of a time series based on the second MI of the time series; and calculating a compensated second analysis value of a time series by applying a second compensation to the calculation of the second analysis value of the time series, wherein the second compensation comprises a second monotonic mapping; and monitoring motion based on the compensated second analysis value of the time series.

[0490] In some embodiments, monotonic mapping can be the same for Type 1 devices, Type 2 devices, and radio signals, even if different MIs are used (e.g., compensation applied to CI).

[0491] Section C46. A method / device / system / software for a compensated wireless monitoring system as described in Section C45, wherein the second compensation and the compensation are the same, and the second monotonic mapping and the first monotonic mapping are the same.

[0492] In some embodiments, monotonic mapping may differ if the Type 1 devices are different but the Type 2 devices are the same.

[0493] Section C47. A method / apparatus / system / software for a compensated wireless monitoring system in Section 1, comprising: transmitting a second wireless signal from a second type 1 heterogeneous wireless device of a wireless sensing system through a wireless multipath channel of the wireless sensing system; receiving the second wireless signal by a type 2 heterogeneous wireless device of the system through the wireless multipath channel; the received second wireless signal being different from the transmitted second wireless signal due to the wireless multipath channel of the venue and the motion of an object; and the received second wireless A multipath channel processor, memory, and instruction set to obtain a second TSCI of a second radio signal received; calculate a second MI of a time series based on the second TSCI; calculate a second analysis value of a second compensated time series based on the second MI of the time series; and calculate a second analysis value of a compensated time series by applying a second compensation to the calculation of the second analysis value of the time series, wherein the second compensation includes a second monotonic mapping, and to monitor motion based on the second analysis value of the compensated time series.

[0494] In some embodiments, the monotonic mapping may be the same if the Type 1 devices are different but the Type 2 devices are the same.

[0495] Section C48. A method / device / system / software for a compensated wireless monitoring system as described in Section C47, wherein the second compensation and the compensation are the same, and the second monotonic mapping and the first monotonic mapping are the same.

[0496] In some embodiments, the monotonic mapping may differ if the Type 2 devices are different but the Type 1 devices are the same.

[0497] Section C49. A method / device / system / software for a compensated wireless monitoring system in Section C1, comprising transmitting a second wireless signal from a Type 1 heterogeneous wireless device of a wireless sensing system through a wireless multipath channel of a venue, and receiving the second wireless signal by a second Type 2 heterogeneous wireless device of the system through the wireless multipath channel, wherein the received second wireless signal is different from the second wireless signal transmitted due to the wireless multipath channel of the venue and the motion of an object, and the second P The process includes using a oscillator, a second memory, and a second instruction set to obtain a second TSCI of a radio multipath channel based on a second radio signal received; calculating a second MI of a time series based on the second TSCI; calculating a second analysis value of a time series based on the second MI of a time series; calculating a compensated second analysis value of a time series by applying a second compensation to the calculation of the second analysis value, wherein the second compensation includes a second monotonic mapping; and monitoring motion based on the compensated second analysis value of a time series.

[0498] In some embodiments, the monotonic mapping may be the same if the Type 2 devices are different but the Type 1 devices are the same.

[0499] Section C50. A method / device / system / software for a compensatory wireless monitoring system in Section C49, wherein the second compensation and the compensation are the same, and the second monotonic mapping and the first monotonic mapping are the same.

[0500] In some embodiments, the monotonic mapping may differ when the Type 1 and Type 2 devices are the same, but the radio signals are different (e.g., different bandwidths).

[0501] Section C51. A method / device / system / software for a compensated wireless monitoring system in Section C1, comprising: transmitting a second wireless signal from a type 1 heterogeneous wireless device of a wireless sensing system through a wireless multipath channel of a venue; receiving the second wireless signal through the wireless multipath channel by a type 2 heterogeneous wireless device of the system, wherein the received second wireless signal is different from the transmitted second wireless signal due to the wireless multipath channel of the venue and the motion of an object; obtaining a second TSCI of the wireless multipath channel based on the received second wireless signal using a processor, memory, and set of instructions; calculating a time series of a second MI based on the second TSCI; calculating a time series of a second calculation based on the time series of the second MI; and calculating a time series of a second calculation by applying a second compensation to the time series of the second calculation, wherein the second compensation comprises a second monotonic mapping; and monitoring motion based on the compensated time series of the second calculation.

[0502] In some embodiments, the monotonic mapping may be the same for Type 1 and Type 2 devices, but the radio signals may be different.

[0503] Section C52. A compensated w...

Claims

1. A method for wireless monitoring, The first wireless device transmits a wireless signal through a wireless multipath channel of the venue, wherein the wireless multipath channel is affected by the motion of objects within the venue. The wireless signal is received by a second wireless device through the wireless multipath channel, wherein the received wireless signal is different from the transmitted wireless signal due to the wireless multipath channel and the motion of the object. Using a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory, to acquire the TSCI (Time Series Channel Information) of the wireless multipath channel based on the received wireless signal, The classification of the sliding time window is performed by analyzing the CI (channel information) included in the TSCI within the sliding time window, Based on the TSCI and the classification of the sliding time window, the MI (motion information) for the sliding time window is calculated, Based on the MI, the motion of the object is monitored, Methods that include...

2. The method according to claim 1, wherein the MI is The similarity scores of two temporally adjacent CIs included in the aforementioned TSCI, The ACF (autocorrelation function) of the aforementioned TSCI, The characteristic features of the aforementioned ACF, A method that is calculated based on at least one of the following.

3. The method according to claim 1 or 2, further, If the sliding time window is classified as a first sliding window class based on the classification, the MI is calculated in a first method based only on the CI included in the TSCI within the sliding time window, If the sliding time window is classified as a second sliding window class based on the classification, the MI is calculated in a second way based on at least one CI included in the TSCI outside the sliding time window, If the sliding time window is classified as a third sliding window class based on the classification, the MI is calculated in a third way based on the first subset of the CI included in the TSCI within the sliding time window, without using the second subset of the CI included in the TSCI within the sliding time window, wherein the first subset and the second subset are relatively prime. Methods that include...

4. A method according to any one of claims 1 to 3, further, The method involves calculating the TS (test score) for each CI included in the TSCI within the sliding time window, based on a certain number of temporally adjacent CIs, wherein the TS includes at least one of the following: difference, magnitude, vector similarity, vector dissimilarity, dot product, and cross product. Classifying each CI included in the TSCI within the sliding time window based on the corresponding TS, Methods that include...

5. The method according to claim 4, further, The LTS (Linkwise Test Score) is calculated based on the sum of all TS values ​​for the CI included in the TSCI within the sliding time window, Based on the LTS, the classification of the sliding time window is performed, Methods that include...

6. The method according to claim 5, If the LTS is smaller than the first threshold, the sliding time window is classified as either the first sliding window class or the second sliding window class. A method in which, if the LTS is greater than a second threshold, the sliding time window is classified as a third sliding window class.

7. A method according to any one of claims 4 to 6, A method in which each CI included in the TSCI within the sliding time window is classified as a first CI class if the corresponding TS is less than a third threshold, and as a second CI class if the corresponding TS is greater than a fourth threshold.

8. The method according to claim 7, If all CIs included in the TSCI within the sliding time window are first-class CIs classified as a first CI class, then the sliding time window is classified as a first sliding window class. A method wherein, if all CIs included in the TSCI within the sliding time window are second-class CIs classified as a second CI class, the sliding time window is classified as a second sliding window class.

9. The method according to claim 7 or 8, Identifying at least one run of a first class CI and at least one run of a second class CI within the sliding time window, wherein each run comprises individual run lengths of consecutive CIs of the same individual CI class within the sliding time window, and each run length is one of a number, quantity, or count greater than zero. Classifying the sliding time window based on the runs of the first class CI and the second class CI and the individual run lengths, Methods that include...

10. The method according to claim 8 or 9, If at least one selection run of the first class CI is selected, the sliding time window is classified as a third sliding window class. If no selection run for the first class CI is selected, the sliding time window is classified as a second sliding window class, in this method.

11. The method according to claim 10, further, Selecting at least one optional run of the first class CI based on the run length of each run of the first class CI and the TS associated with that run, The calculation of the MI based on the at least one selected run of the first class CI includes, The method wherein the at least one selected run is at least one run of the first class CI having the longest run length among all runs of the first class CI within the sliding time window.

12. A method according to claim 10 or 11, If the individual run lengths of the leading runs of consecutive first class CIs, including the first CI within the sliding time window, are greater than a first individual threshold, then the first selected run is selected as the leading run. If the individual run lengths of the trailing runs of consecutive first-class CIs, including the last CI within the sliding time window, are greater than the second individual threshold, then the second selected run is selected as the leading run. A method in which, if the individual run length of the leading run or trailing run is greater than a third individual threshold, any optional run other than the leading run or trailing run is selected.

13. A method according to any one of claims 10 to 12, The at least one selection run is selected such that the quantity of the at least one selection run is less than or equal to a predetermined number, and A method in which at least one selection run is selected such that, for each selection run, all relevant TS values ​​are below a threshold.

14. A method according to any one of claims 10 to 13, further, When the sliding time window is classified as the third sliding window class, Constructing the first subset by including all of the at least one selected run of the first class CI included in the TSCI within the sliding time window, Constructing the second subset by including all second-class CIs included in the TSCI within the sliding time window, When the sliding time window is classified as the second sliding window class, This includes calculating the aforementioned MI as the sum of at least one adjacent MI, Each adjacent MI is associated with one of the following: the past adjacent sliding time window of a CI included in the TSCI, the future adjacent sliding time window of a CI included in the TSCI, or the adjacent sliding time window of a CI included in another TSCI. A method in which the adjacent MI is calculated based on at least one CI included in the TSCI outside the sliding time window.

15. A method according to any one of claims 10 to 14, further, The method involves calculating at least one provisional MI for the sliding time window, each provisional MI being calculated based on individual selection runs of the first class CI included in the TSCI within the sliding time window. The aforementioned MI is calculated as the sum of the at least one provisional MI, Methods that include...

16. A method according to any one of claims 10 to 15, further, The selected run is determined as the first run of the CI included in the TSCI within the sliding time window, The aforementioned leading run of the CI is combined with the trailing run of the CI included in the TSCI within the previous sliding time window to form a combined run of the CI included in the TSCI, Based on the aforementioned composite run of CI, a first provisional MI is calculated, Methods that include...

17. A method according to any one of claims 10 to 16, further, The selected run is determined as the last run of the CI included in the TSCI within the sliding time window, The aforementioned trailing run of the CI is combined with the leading run of the CI included in the TSCI within the next sliding time window to form a combined run of the CI included in the TSCI, Based on the aforementioned composite run of CI, a second provisional MI is calculated, Methods that include...

18. A method according to any one of claims 10 to 17, further, For each provisional MI calculated based on individual selection runs, the individual calculated weights are calculated based on the run length of the individual selection runs. The MI is calculated as a weighted sum of the at least one provisional MI, wherein each provisional MI is weighted by the individually calculated weights. Methods that include...

19. A method according to any one of claims 1 to 15, further, A method comprising calculating the MI as a sum of at least one provisional MI and at least one adjacent MI, wherein each adjacent MI is associated with one of the following: a past adjacent sliding time window of a CI included in the TSCI, a future adjacent sliding time window of a CI included in the TSCI, and an adjacent sliding time window of a CI included in another TSCI.

20. A wireless monitoring system, A first wireless device configured to transmit wireless signals through a wireless multipath channel of a venue, wherein the wireless multipath channel is affected by the motion of objects within the venue, and the first wireless device and A second wireless device configured to receive the wireless signal through the wireless multipath channel, wherein the received wireless signal is different from the transmitted wireless signal due to the wireless multipath channel and the motion of the object, A processor is provided, and the processor is Based on the received wireless signal, the TSCI (Time Series Channel Information) of the wireless multipath channel is obtained, The classification of the sliding time window is performed by analyzing the CI (channel information) included in the TSCI within the sliding time window, Based on the TSCI and the classification of the sliding time window, the MI (motion information) for the sliding time window is calculated, Based on the MI, the motion of the object is monitored, A wireless monitoring system configured to perform the following actions.

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