Methods, Apparatus, and Systems for Wireless Monitoring, Sensing, and Localization

Abstract

To provide a method, a device, and a system for radio monitoring, sensing, and localization.SOLUTION: A radio monitoring method includes: transmitting a radio signal through a venue's radio multipath channel; receiving the radio signal through the radio multipath channel and obtaining TSCI (time series channel information) of the radio multipath channel based on the received radio signal; classifying a sliding time window by analyzing CI (channel information) included in the TSCI within the sliding time window, and monitoring the motion of an object on the basis of TSCI and sliding time window classification, and on the basis of MI (motion information) by calculating the MI on the sliding time window.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present teachings relate generally to wireless monitoring, sensing, and localization. More specifically, the present teachings relate to improving the accuracy and efficiency of wireless monitoring based on motion information computation and time window classification, wireless sensing using multiple groups of wireless devices, and wireless monitoring and localization of objects within a venue based on correlation scores. [Background technology]

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

[0003] In addition, wireless sensing has received increasing attention in recent years due to the ubiquitous deployment of wireless devices. Furthermore, human activity affects the propagation of wireless signals; therefore, understanding and analyzing how wireless signals respond to human activity can reveal a wealth of information about the activity. As more bandwidth becomes available in new generations of wireless systems, wireless sensing will enable many smartphone IoT applications that are only imagined today in the near future. This is because increased bandwidth allows for the viewing of more multipaths in rich scattering environments, such as indoor or urban areas, which can be treated as hundreds of virtual antennas / sensors. Because there may 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 indispensable, from map navigation to social networking. The Global Positioning System (GPS) has reshaped human life for decades and is considered a significant technological milestone in modern society. However, while GPS satisfies users' needs for location services in outdoor scenarios, it cannot provide reliable location data in indoor conditions due to signal blockage. Therefore, indoor location technology has become a hot topic in academic research. Indoor location systems can be implemented either actively or passively. Active indoor location requires a specialized device attached to or carried by a human and locates a target by continuously monitoring signals from the device. On the other hand, passive indoor location systems typically rely on the perception of sensors placed in the environment. Therefore, the target does not need to carry a device. Such designs benefit multiple applications where continuous user cooperation is impossible or inconvenient, including intruder detection, fall detection, and daily activity monitoring.

[0005] Existing passive indoor location solutions operate at various resolutions, ranging from centimeter / decimeter levels to room-level or zone-level resolutions, corresponding to different applications. The former approach aims to provide fine-grained indoor location information, enabling applications such as indoor tracking, while the latter focuses on obtaining coarser location information that can provide behavioral 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 infrared (PIR) sensors, among others. PIR sensors can detect humans entering a room in response to their presence, but they cannot continuously detect humans during low-level activities such as reading or napping, let alone in blind spots. Cameras are widely deployed to monitor rooms for security purposes, but this only works in line-of-sight conditions, incurs additional hardware costs, and risks privacy violations. Therefore, there remains a great need for robust, low-cost, and privacy-friendly solutions.

[0006] Among many approaches to passive indoor localization, WiFi-based approaches have received the greatest research focus. This is due to two factors: superior sensing capabilities and negligible cost. A single WiFi access point has more coverage and fewer blind spots than other sensors, thanks to ubiquitous indoor propagation and its ability to penetrate walls. Furthermore, the sensitivity of WiFi multipath propagation profiles to changes in the physical environment is useful for recording information linked to human motion. WiFi signals can "see" human motion at multiple scales in indoor environments, from sizable body movements to chest movements. Furthermore, the availability of channel state information (CSI) from commercially available WiFi chipsets allows these approaches to incur negligible additional costs and reuse existing WiFi infrastructure. Most WiFi-based localization approaches using CSI rely on dedicated placement and calibration to infer geometric relationships, which requires a high level of setup effort. Other efforts using CSI fingerprinting require tedious training and cannot generalize well to various environments. As a result, both of these approaches are not scalable to real-world scenarios. Summary of the Invention

[0007] The present teachings relate to systems, methods and devices 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 within a venue based on correlation scores.

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

[0009] In another embodiment, a method performed by a wireless monitoring system is described. The 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 motion of an object within the venue; receiving the wireless signal by 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 the object; obtaining, using a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory, time-series channel information (TSCI) of the wireless multipath channel based on the received wireless signal; performing sliding time window classification by analyzing the channel information (CI) included in the TSCI within a sliding time window; computing motion information (MI) for the sliding time window based on the TSCI and the sliding time window classification; and monitoring the motion of the object based on the MI.

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

[0011] In yet another embodiment, a method performed by a set of heterogeneous wireless devices in a venue for wireless sensing is described, the method including: communicatively coupling a specific device included in the set to a first wireless channel based on a first protocol using a first radio of the specific device; communicatively coupling a specific device included in the set to a second device through a second wireless channel based on a second protocol using a second radio of the specific device; and performing pairwise subtasks by the specific device and the second device based on wireless signals communicated between the specific device and the second device through the second wireless channel using the second radio of the specific device; and detecting a pairwise subtask by the specific device based on a wireless signal extracted from the wireless signal. The method includes: obtaining pairwise sensing analysis values ​​calculated based on time-series channel information (TSCI) of the second wireless channel obtained by the specific device, where each channel information (CI) includes at least one of channel state information (CSI), channel impulse response (CIR), or channel frequency response (CFR); calculating a composite sensing analysis value by the specific device based on the pairwise sensing analysis values; transmitting the composite sensing analysis value by the specific device to the first device over the first wireless channel using a first radio of the specific device; and performing a wireless sensing task based on the composite sensing analysis value.

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

[0013] In another embodiment, a method for correlation-based wireless monitoring is described, the method including: forming at least two device pairs, each including a first wireless device and a second wireless device, of a plurality of first wireless devices and a plurality of second wireless devices in a venue, the venue including a plurality of objects performing individual motion; for each device pair, a first wireless device of the device pair transmitting an individual wireless signal; and a second wireless device of the device pair receiving the individual wireless signal through an individual wireless multipath channel of the venue, the received wireless signal being different from the transmitted wireless signal due to the individual wireless multipath channel and the motion of the plurality of objects in the venue; obtaining individual time-series channel information (TSCI) of the individual wireless multipath channels based on the received wireless signals; and calculating individual motion information (MI) based on the TSCI. performing individual sensing tasks based on the individual MIs and the individual TSCIs; detecting and monitoring motion of a first object in the first sensing task based on a first MI calculated based on a first TSCI associated with the first device pair; detecting and monitoring motion of a second object in the second sensing task based on a second MI calculated based on a second TSCI associated with the second device pair; calculating a correlation score based at least in part 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 includes a memory having a set of instructions stored therein 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 the second MI. Motion of a first object is detected and monitored in a first sensing task associated with a first device pair based on the first MI calculated based on the first TSCI obtained from first wireless signals communicated between the first device pair in a venue, the venue including multiple objects each performing an individual motion. Motion of a second object is detected and monitored in a second sensing task associated with a second device pair based on the second MI calculated based on second TSCI obtained from second wireless signals communicated between the second device pair in the venue, each device pair including a first wireless device and a second wireless device. For each device pair, a first wireless device of the device pair is configured to transmit a respective wireless signal. A second wireless device of the device pair includes: receiving the respective wireless signals through respective wireless multipath channels of the venue, where the received wireless signals differ from the transmitted wireless signals due to the respective wireless multipath channels and motion of multiple objects within the venue; obtaining respective TSCIs for the respective wireless multipath channels based on the received wireless signals; computing respective MIs based on the TSCIs; and performing respective sensing tasks based on the respective MIs and the respective TSCIs. 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 the present teachings for wireless monitoring, sensing, and localization. Additional novel features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following drawings and the accompanying drawings, or may be learned by the making or operation of the embodiments. The novel features of the present teachings may be realized and attained by practice or use of various aspects of the methods, instrumentalities, and combinations that are described in the detailed examples discussed below. [Brief explanation of the drawings]

[0016] The methods, systems, and / or devices described herein will be further explained with reference to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, and like reference numerals represent like structure throughout the several views of the drawings.

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

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

[0019] [Figure 3] FIG. 3 illustrates a flowchart of an exemplary method for precision wireless monitoring, according to some embodiments of the present disclosure.

[0020] [Figure 4] , [Figure 5] , [Figure 6] , [Figure 7] , [Figure 8]4-8 are flowcharts illustrating detailed operations for precision wireless monitoring according to some embodiments of the present disclosure.

[0021] [Figure 9] , [Figure 10] , [Figure 11] , [Figure 12] , [Figure 13] , [Figure 14] , [Figure 15] , [Figure 16] , [Figure 17] 9-17 are flowcharts illustrating detailed operations for compensated wireless monitoring according to some embodiments of the present disclosure.

[0022] [Figure 18] FIG. 18 illustrates an example scenario for wireless sensing in a venue, according to some embodiments of the present disclosure.

[0023] [Figure 19] FIG. 19 illustrates an example floor plan and arrangement of a wireless device for wireless sensing, according to some embodiments of the present disclosure.

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

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

[0026] [Figure 22]FIG. 22 illustrates an exemplary system for performing wireless sensing in a venue using multiple device groups with a whole-home deployment, according to some embodiments of the present disclosure.

[0027] [Figure 23] FIG. 23 illustrates a flowchart of 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] FIG. 24 illustrates an example floor plan for wireless sensing displaying sensing motion statistics and analysis values, according to some embodiments of the present disclosure.

[0029] [Figure 25] FIG. 25 illustrates a flowchart of an exemplary method of a wireless sensing and presentation system according to some embodiments of the present disclosure.

[0030] [Figure 26] FIG. 26 illustrates a flowchart of an exemplary method for performing a sensing-by-proxy procedure, according to some embodiments of the present disclosure.

[0031] [Figure 27A] FIG. 27A illustrates an exemplary environment for motion monitoring and localization according to some embodiments of the present disclosure.

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

[0033] [Figure 28A] FIG. 28A illustrates another exemplary environment for motion monitoring and localization, according to some embodiments of the present disclosure.

[0034] [Figure 28B] FIG. 28B illustrates different activity captured by another wireless system, according to some embodiments of the present disclosure.

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

[0036] [Figure 30] FIG. 30 illustrates example features extracted from a CSI time series by a wireless system in accordance with some embodiments of the present disclosure.

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

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

[0039] [Figure 33A] , [Figure 33B] 33A-33B show example locations of transmitters and receivers in different setup environments, according to some embodiments of the present disclosure.

[0040] [Figure 34]FIG. 34 illustrates a flowchart of an exemplary method for wireless monitoring and localization, according to some embodiments of the present disclosure.

[0041] [Figure 35] FIG. 35 shows a flowchart illustrating detailed operations for wireless monitoring and localization according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0042] The symbol " / " disclosed herein means "and / or." For example, "A / B" means "A and / or B." In some embodiments, a method / device / system / software for a wireless monitoring system is disclosed. Time-series channel information (CI) of a wireless multipath channel is obtained using a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory. The time-series CI (TSCI) may be extracted from a wireless signal transmitted through the channel from a Type 1 heterogeneous wireless device (e.g., a wireless transmitter (TX), a "Bot" device) to a Type 2 heterogeneous wireless device (e.g., a wireless receiver (RX), an "Origin" device) in a venue. 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 may be computed / monitored based on the TSCI. Tasks may be performed based on the characteristics / STI / MI. The task-related presentation can be generated in a user interface (UI) on the user's device.

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

[0044] A wireless multipath channel may include a communication channel, an analog frequency channel (e.g., with a carrier frequency near 700 / 800 / 900 MHz or 1.8 / 1.8 / 1.9 / 2.4 / 3 / 5 / 6 / 27 / 60 / 70+ GHz), a coded channel (e.g., in CDMA), and / or a channel of a wireless / cellular network / system (e.g., WLAN, WiFi, mesh, 4G / LTE / 5G / 6G / 7G / 8G, Bluetooth, Zigbee, UWB, RFID, microwave). It may include multiple channels, which may be contiguous (e.g., adjacent / overlapping bands) or non-contiguous (e.g., non-overlapping bands, 2.4 GHz / 5 GHz). While a channel is used to transmit wireless signals and perform sensing measurements, data (e.g., TSCI / features / components / characteristics / STI / MI / analytical values / task outputs, auxiliary / non-sensing data / network traffic) may also be communicated / transmitted over the channel.

[0045] The wireless signal may include a sequence of probe signals, which may be EM radiation, radio frequency (RF), optical, band-limited, or baseband signals, licensed or unlicensed ISM band signals, or wireless, mobile, cellular, optical, network, mesh, downlink, uplink, unicast, multicast, or broadcast signals, which may comply with standards / protocols (e.g., WLAN, WWAN, WPAN, WBAN, international, national, industrial, de facto, IEEE, 802.11, 15, 16, WiFi, 802.11n, ac, ax, be, bf, 3G, 4G, LTE, 5G, 6G, 7G, 8G, 3GPP, Bluetooth, BLE, Zigbee, NFC, RFID, UWB, WiMax). The 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 / poll / announcement / extension / query / acknowledgement frames / packets / signals, and / or null-data-frame (NDP), RTS, CTS, QoS, CF-Poll, CF-Ack, block-acknowledgement, reference, training, or synchronization. It may include line-of-sight (LOS) or non-LOS components (or paths / links). Data may be embedded. The probe signal may be replaced (or embedded) by a data signal. Each frame / packet / signal may include a preamble / header / payload. It may include a training sequence, a short (STF) / long (LTF) training field, L-STF / L-LTF / L-SIG / HE-STF / HE-LTF / HE-SIG-A / HE-SIG-B, and a channel estimation field (CEF). It may be used to transfer power wirelessly from a Type 1 device to a Type 2 device. The sounding rate of the signal may be adjusted to control the amount of power transferred. The probe signal may be transmitted in bursts.

[0046] TSCI may be extracted / obtained (e.g., by an IC / chip) from wireless signals at layers of a Type-2 device (e.g., layers of the OSI reference model, PHY / MAC / Data Link / Logical Link Control / Network / Transport / Session / Presentation / Application layer, TCP / IP / Internet / Link layer). It may be extracted from received wireless / derived signals. It may include wireless sensing measurements obtained with communication protocols (e.g., wireless / cellular communication standards / networks, 4G / LTE / 5G / 6G / 7G / 8G, WiFi, IEEE802.11 / 11bf / 15 / 16). Each CI may be extracted from a probe / sounding signal and associated with a timestamp. TSCI may be associated with a CI / sampling / sounding frequency / period start time / stop time / duration / amount. Motion detection / sensing signals may be recognized / identified based on the probe signal. TSCI may be stored / retrieved / accessed / pre-processed / processed / post-processed / conditioned / analyzed / monitored. TSCI / features / components / characteristics / STI / MI / analysis values / task results may be communicated to an edge / cloud server / Type 1 / Type 2 / hub / data aggregator / another device / system / network.

[0047] A Type 1 / Type 2 device 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. It may be an access point (AP) / base station / mesh / router / repeater / hub / radio station / client / terminal / "Origin Satellite" / "Tracker Bot" and / or an Internet of Things (IoT) / appliance / wearable / accessory / peripheral / funny / amenity / gadget / vehicle / module / wireless-enabled / unicast / multicast / broadcast / node / hub / target / sensor / portable / mobile / cellular / communication / motion detection / source / destination / standards-compliant device. It may include additional attributes such as auxiliary functions / network connectivity / generic / brand / model / appearance / form / shape / color / material / specifications, etc. The above (e.g., components / device types / additional attributes) may 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 / co-located. They may be the same device. Type 1 / Type 2 / portable / nearby / other devices, sensing / measurement sessions / links between them, and / or objects / representations / motions / characteristics / STI / MI / tasks may be associated with an identity / identification / identifier (ID), such as a UUID, associated / unassociated STA ID (ASID / USID / AID / UID). Type 2 devices may passively observe / monitor / receive wireless signals from Type 1 devices without establishing a connection (e.g., association / authentication / handshake) with the Type 1 device or requesting services from the Type 1 device. Type 1 / Type 2 devices may move along with the tracked object / other object.

[0049] A Type 1 (TX) device may function temporarily / sporadically / continuously / repeatedly / interchangeably / alternatively / simultaneously as a Type 2 (RX) device, and vice versa. A Type 1 device may be a Type 2 device. A device may function temporarily / sporadically / continuously / repeatedly / simultaneously as a Type 1 / Type 2 device. There may be multiple wireless nodes, each a Type 1 / Type 2 device. TSCI may be obtained between two nodes when they exchange / communicate wireless signals. Object characteristics / STI / MI may be monitored based on TSCI individually or multiple TSCIs 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 / automated guided vehicle / AGV), or passively monitored with a Type 1 / Type 2 device that does not move with the object (e.g., both devices are fixed).

[0051] Tasks can be performed using a reference / trained / initial database / profile / baseline that is trained / collected / processed / computed / sent / stored during the training phase. The database can be retrained / updated / reset.

[0052] A presentation may include UI / GUI / text / message / form / webpage / visual / image / video / graphic / animation / graphical / symbol / emoji / sign / color / shade / sound / music / speech / audio / mechanical / gesture / vibration / haptic presentation. Time series of characteristics / STI / MI / task results / other quantities may be displayed / presented in a presentation. Any computation may be performed / shared by a processor (or logic unit / chip / IC), Type 1 / Type 2, user, nearby, another device, local, edge, cloud server, hub, data, signal analysis subsystem, sensing initiator / responder, SBP initiator / responder, AP, or non-AP. A presentation may include monthly / weekly / daily / simplified / detailed / cross-sectional / small / large / form factor / color-coded / comparison / summary / web display, animation, audio announcement, repetitive motion, or another presentation related to the cyclical / recurring characteristics of the representation.

[0053] Multiple Type 1 (or Type 2) devices may interact with a Type 2 (or Type 1) device. Multiple Type 1 (or Type 2) devices may be synchronous / asynchronous and / or may use the same / different channel / 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 wireless signals transmitted (or received) by them may be sporadic / temporary / continuous / repeated / synchronous / simultaneous. They may operate independently / collaboratively. Their data (e.g., TSCI / features / characteristics / STI / MI / intermediate task results) may be processed / monitored / analyzed independently or jointly / collaboratively.

[0054] Any device may act based on some state / internal state / system state. Devices may communicate directly or through another / nearby / portable device / server / hub device / cloud server. Devices / systems 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 performed in the order shown or in another order. Steps may be performed in parallel / iteratively / repeatedly. Users may include humans / adults / elderly / males / females / young people / children / babies / pets / animals / living things / machines / computer modules / software. Steps / actions / processing may differ for different devices based on location / orientation / direction / role / roles / user-related characteristics / settings / available resources / bandwidth / power / network connection / hardware / software / processors / coprocessors / memory / battery life / antennas / directional antennas / power settings / device parameters / characteristics / conditions / situations / states. Any / all devices may be controlled / coordinated by a processor (e.g., Type 1 / Type 2 / nearby / portable / associated with another device / server / designated source), including devices physically within / on / attached to a common device.

[0055] A Type 1 (or Type 2) device may be capable of wirelessly coupling with multiple Type 2 (or Type 1) devices. The Type 1 (or Type 2) device may be caused / controlled to switch / establish a wireless coupling (e.g., association / authentication) from a Type 2 (or Type 1) device to another Type 2 (or another Type 1) device. The switching may be controlled by a server / hub device / processor / Type 1 device / Type 2 device. The wireless channels before and after the switching may be different. A second wireless signal may be transmitted between the Type 1 (or Type 2) device and a second Type 2 (or second Type 1) device through the second channel. A second TSCI of the second channel may be extracted / obtained from the second signal. The first / second signal, the first / second channel, the first / second Type 1 device, and / or the first / second Type 2 device may be identical / similar / co-located.

[0056] A Type 1 device may transmit / broadcast a wireless signal to multiple Type 2 devices without establishing a connection (association / authentication) with each individual Type 2 device. It may transmit to a specific / common MAC address, which may 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 wireless signal. The specific MAC address may be associated with a venue, which may be recorded in an association table of an association server (e.g., a hub device). The venue may be identified by the Type 1 device / Type 2 device based on the wireless signal received at the 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 a venue such that the Type 1 and Type 2 devices are unaware of each other. During setup, the Type 1 device may be instructed / 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 wireless signals (e.g., a sequence of probe signals) to a specific MAC address. When powered on, the Type 2 device may scan for probe signals according to a table of MAC addresses (e.g., stored in a designated source, server, hub device, cloud server) that may be used to broadcast at different locations (e.g., different MAC addresses used for different venues such as a house / office / enclosure / floor / multi-store building / store / airport / mall / stadium / hall / station / subway / lot / area / zone / region / district / city / country / continent, etc.). When the Type 2 device detects a wireless signal transmitted to a specific MAC address, it can use the table to identify the venue.

[0058] A channel may be selected from a set of candidate / selectable / acceptable channels. Candidate channels may be associated with different frequency bands / bandwidths / carrier frequencies / modulations / wireless standards / coding / encryption / payload characteristics / networks / IDs / SSIDs / characteristics / settings / parameters. A particular MAC address / selected channel may be changed / adjusted / modified over time (e.g., according to a time table / rule / policy / mode / condition / situation / change). The selection / change may be based on availability / collisions / traffic patterns / co-channel / inter-channel interference / available bandwidth / random selection / pre-selection list / plan. This may be done by a server (e.g., a hub device). They may be communicated (e.g., to / from a Type 1 / Type 2 / hub / another device / local / edge / cloud server).

[0059] A wireless connection (e.g., association / authentication) may be established (e.g., using a signal handshake) between the Type 1 device and a nearby / portable / another device. 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 / another device. The nearby / portable / another 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 transmit / broadcast a wireless signal to multiple Type 2 devices without establishing a connection with the Type 2 device. The second handshake signal may be a response / acknowledgement (e.g., ACK) to the first handshake signal. The second handshake signal may include information of the venue / Type 1 device. The nearby / portable / another device may be a dummy device having a purpose (e.g., primary purpose, secondary purpose) of establishing a wireless connection with the Type 1 device to receive the first signal or to transmit the second signal. 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 the Type 1 device to broadcast a signal to multiple Type 2 devices without establishing connections with the multiple Type 2 devices by sending a third handshake signal to the Type 1 device, which may respond to the third signal by sending a fourth handshake signal to the other device.

[0061] A nearby / portable / other device may be used to trigger multiple Type 1 devices to broadcast. It may have multiple RF circuits to trigger multiple transmitters in parallel. The triggering may be sequential / partially sequential / partially / fully parallel. Parallel triggering may be achieved by using additional (one or more) devices to perform similar triggering in parallel with the nearby / portable / other device. After establishing a connection with the Type 1 device, the nearby / portable / other device may suspend / stop communication with the Type 1 device. It may enter an inactive / hibernate / sleep / standby / low power / off / power down mode. The suspended communication may be resumed. The nearby / portable / other device may have a specific MAC address, and the Type 1 device may send a signal to the specific MAC address.

[0062] A (first) wireless signal may be transmitted by a first antenna of a Type 1 device over a first channel at a first venue to several first Type 2 devices. A second wireless signal may be transmitted by a second antenna of a Type 1 device over a second channel at a second venue to several second Type 2 devices. The first and second signals may be transmitted at first and second (sounding) rates, respectively, possibly individually to first and second MAC addresses. Several first and second channels / signals / rates / MAC addresses / antennas / Type 2 devices may be the same / different / synchronous / asynchronous. The first and second venues may have the same / different size / shape / multipath characteristics. The immediate areas around the first and second venues / first and second antennas may overlap. The first / second channels / signals can be WiFi+LTE (one WiFi and one LTE), or WiFi+WiFi, or WiFi(2.4GHz)+WiFi(5GHz), or WiFi(5GHz, channel=a1, BW=a2)+WiFi(5GHz / channel=b1, BW=b2). Some of the first / second items (e.g., channel / signal / rate / MAC address / antenna / Type 1 / Type 2 device) can be changed / adjusted / modified over time (e.g., based on a time table / rule / policy / mode / condition / situation / another change).

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

[0064] A database of available / candidate Type 1 (or Type 2) devices may be initialized / maintained / updated by a Type 2 (or Type 1) device. A Type 2 device may receive wireless 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 / conditions / task requirements / training task results / MAC address / identity / identifier / past signal sources / history / user instructions / other considerations.

[0065] An undesirable / bad / problematic / unsatisfactory / unacceptable / unacceptable / faulty / severe / undesirable / insufficient / insufficient / poor / unsuitable condition may occur when (1) the timing between adjacent probe signals in the received wireless signal becomes irregular and deviates from the agreed-upon sounding rate (e.g., time perturbations beyond an acceptable range) and / or (2) the processed / signal strength of the received signal is too weak (e.g., below a third threshold or below a fourth threshold for a significant percentage of the time), where processing includes any low-pass / band-pass / high-pass / median / moving / weighted average / linear / nonlinear / smoothing filtering. Any thresholds / percentages / parameters may vary over time. Such conditions may occur when Type 1 / Type 2 devices become increasingly distant or when the channel becomes 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. The changes may be according to time tables / rules / policies / modes / conditions (e.g., undesirable conditions) / other changes. For example, the sounding rate may be normally 100Hz, but may be changed to 1000Hz in harsh conditions or to 1Hz in low power / standby conditions.

[0067] Settings can vary based on task requirements (e.g., 100Hz normal, 1000Hz instantaneously for 20 seconds). Within a task, instantaneous systems can be adaptively / dynamically associated with classes / states / conditions (e.g., low / normal / high priority / emergency / critical / normal / privileged / non-subscription / subscription / paid / non-paid). Settings (e.g., sounding rate) can be adjusted accordingly. Changes can be controlled by the server / hub / Type 1 / Type 2 device. Scheduled changes can be made according to a timetable. Changes can be made immediately if an emergency is detected or gradually if an ongoing condition is detected.

[0068] Characteristics / STIs / MIs may be monitored / analyzed individually based on TSCIs associated with a particular 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 particular Type 2 device and any Type 1 device, jointly based on any TSCIs associated with a particular 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 may be applied (e.g., to CIs / CI features / characteristics / STIs / MIs) and / or trained / retrained / updated. In the training stage, training may be performed based on multiple training TSCIs of several training wireless multipath channels or characteristics / STIs / MIs calculated from the training TSCIs. The training TSCIs are obtained from training wireless 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 TSCIs / current TSCIs. There can be multiple classes (e.g., groupings / categories / events / motions / representations / activities / objects / locations) attached to venue / region / zone / place / environment / house / office / building / warehouse / facility objects / representations / motions / actions / processes / events / manufacturing / assembly line / maintenance / repair / navigation / objects / emotions / minds / states / stages / gestures / gates / actions / motions / presence / motions / routines / activities / history / events.

[0070] Classifiers can include linear / nonlinear / binary / multi-class / Bayesian classifier / Fisher linear discriminant / logistic regression / Markov chain / Monte Carlo / deep / neural networks / perceptron / 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 any of the following: subspace projection / principal component analysis (PCA) / independent component analysis (ICA) / vector quantization / singular value decomposition (SVD) / eigendecomposition / eigenvalue / time / frequency / orthogonal / non-orthogonal decomposition, processing / pre-processing / post-processing. Each CI may contain multiple components (e.g., vectors / combinations of complex values). Each component may be pre-processed to give magnitude / phase or functions thereof.

[0072] Features can include: feature extraction / projection output, amplitude / amplitude / phase / energy / power / intensity / intensity, presence / absence / proximity / likelihood / histogram, time / period / duration / frequency / component / decomposition / projection / band, local / global / maximum / minimum / zero crossing, repeating / periodic / typical / habitual / transient / atypical / sudden / mutually exclusive / evolutionary / transitional / changing / temporal / associated / correlated features / patterns / trends / profiles / events / trends / slope / behavior, causality / short term / long term / correlation / statistics / frequency / period / duration, movement / movement / location / map / coordinates / height / speed / acceleration / angle / rotation / size / volume, suspicious / hazard / alarm event / warning / certainty / approach / collision Bump, tracking / breathing / heart rate / gait / behavior / event / statistics / hourly / daily / weekly / monthly / yearly parameters / statistics / analysis, health / disease / medical statistics / analysis, state / condition / situation / disease / biometric early / immediate / concurrent / delayed indication / suggestion / sign / indicator / verification / detection / symptom, baby / patient / machine / equipment / temperature / vehicle / parking lot / road / lift / elevator / space / street / fluid flow / house / room / office / home / building / warehouse / storage / system / ventilation / fan / pipe / duct / person / human / car / boat / truck / plane / drone / downtown / crowd / impulsive event / cyclostationary / environment / vibration / material / surface / 3D / 2D / local / global, and / or another measurable quantity / variable. Features may include monotonic functions of features or sliding aggregates of 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] A current event / motion / expression / object at a current time may be classified by applying a classifier to current TSCI / characteristics / STI / MI obtained from current wireless signals received by a Type 2 device at a venue from a Type 1 device during the operational phase. If there are multiple Type 1 / Type 2 devices, some / all (or their locations / antenna locations) may be permutations of the corresponding training Type 1 / Type 2 devices (or locations / antenna locations). The Type 1 / Type 2 devices / signals / channels / venues / objects / motions may be the same as / different from the corresponding training entities. The classifier may be applied in a sliding window. The current TSCI / characteristics / STI / MI may be augmented by training TSCI / characteristics / STI / MI (or fragments / extractions) to bootstrap the classification / classifier.

[0075] A first section / segment (having a first duration / start / end time) of a first TSCI (associated with a first Type 1-Type 2 device pair) may be aligned with a second section / segment (having a second duration / start / end time) of a second TSCI (associated with a second Type 1-Type 2 device pair) (e.g., using dynamic time warping / DTW / matched filtering, possibly 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 pre-processed. Some similarity scores (per component / item / link / segment) may be computed. The similarity scores may include any of the mismatch / distance / similarity score / cost. A component-wise similarity score may be calculated between a component of a first item (CI / feature / characteristic / STI / MI) in the first section and a corresponding component of a corresponding mapped item (second item) in the second section. An item-wise similarity score may be calculated between the first and second items (e.g., based on a set of corresponding component-wise similarity scores). The overall value may include a sum / weighted sum, a weighted average / robust / trimmed mean / arithmetic / geometric / harmonic mean, or a median / mode. A link-wise similarity score may be calculated between the first and second items associated with a link (TX-RX antenna pair) of the first and second Type 1-Type 2 device pair (e.g., based on a set of corresponding item-wise similarity scores). A segment-wise similarity score may be calculated between the first and second segments (e.g., based on a set of corresponding link-wise similarity scores). The first and second segments may be slid.

[0076] In DTW, the first / second segment, the first / second item, another first (or second) item of the first (or second) segment, or any function of the corresponding timestamp / duration / difference / difference may satisfy the constraint. The time difference between the first item and the second item may be constrained (e.g., upper / lower bound). The first (or second) section may be the entire first (or second) TSCI. The duration / start / end time of the first / second may be the same / different.

[0077] In one example, the first / second Type 1-Type 2 device pair may be the same, and the first / second TSCI may be the same or different. If different, the first / second TSCI may include a current / reference, current / current, or reference / reference TSCI pair. In the case of "current / reference," the first TSCI may be a current TSCI acquired during the operational phase, and the second TSCI may be a reference TSCI acquired during the training phase. In the case of "reference / reference," the first / second TSCI 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 TSCI may be two TSCIs acquired during the operational phase (e.g., associated with 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] The aligned first / second segments (or portions thereof) can be represented as first / second vectors. The portions may include all items (for "segment-wise"), or all items associated with the TX-RX link (for "link-wise"), or items (for "item-wise"), or components of items (for "component-wise"). The similarity score may include any combination / aggregation / function of the following: dot product / correlation / autocorrelation / correlation index / covariance / discrimination score / distance / Euclidean / absolute / L_k / weighted distance (between the first / second vectors). The similarity score may be normalized by the vector length. Parameters derived from the similarity score may be modeled using statistical distributions. The scale / location / other parameters of the statistical distribution may be estimated.

[0079] Recall that there may be multiple sliding segments. A classifier may be applied to the sliding first / second segment pair to obtain a provisional classification result. It may associate the current event with a particular class based on one segment pair / provisional classification result or multiple segment pairs / provisional classification results (e.g., the period for which the similarity score is dominant (e.g., largest / smallest / dominant / unmatched / most important / superior), or is sufficiently significant (e.g., higher / lower than some threshold) among all candidate classes for N consecutive times, or for a high / low sufficient percentage, or the most / lowest frequent).

[0080] Channel information (CI) may include any of the following: signal strength / phase / phase / timestamp, spectral power measurements, 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 wireless signal propagation, input to output conversion, stable behavior of environmental profile, state profile, wireless channel measurements / received signal strength indicator (RSSI) / channel state information (CSI) / channel impulse response (CIR) / channel frequency response (CFR) / frequency component characteristics (e.g., subcarriers) / channel characteristics / channel filter response, auxiliary information, data / meta / user / account / access / security / session / status / monitoring / device / network / household / neighborhood / environment / real-time / sensor / storage / encrypted / compressed / protected data, identity / identifier / identification information.

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

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

[0083] The CI / TSCI may be pre-processed / processed / post-processed / stored / retrieved / sent / received. Some modem / radio condition parameters may be held constant. The modem parameters may be applied to the radio subsystem and may represent radio conditions. The motion detection signal (e.g., a baseband signal, then decoded / demodulated packets) may be obtained by processing (e.g., downconverting) a radio signal (e.g., an RF / WiFi / LTE / 5G / 6G signal) by the radio subsystem using the radio conditions represented by the stored modem parameters. The modem parameters / radio conditions may be updated (e.g., using previous modem parameters / radio conditions). Both the previous / updated modem parameters / radio conditions may be applied in the radio subsystem (e.g., to process the signal / decode the data). In the disclosed system, both may be obtained / compared / analyzed / processed / monitored.

[0084] Each CI may include N1 CI components (CICs) (e.g., time / frequency domain components, decomposed components), each with a corresponding CIC index. Each CIC may include a real / imaginary / complex quantity, magnitude / phase / Boolean / flag, and / or some combination / subset. Each CI may include a vector / matrix / set / collection of CICs. The CICs of TSCIs associated with a particular CIC index may form a CIC time series. The TSCI may be divided into N1 time series of CICs (TSCICs), each associated with a respective CIC index. Characteristics / STI / MI may be monitored based on the TSCICs. Some TSCICs may be selected for further processing based on some criteria / cost function / signal quality metric (e.g., SNR, interference level).

[0085] Multi-component characteristics / STI / MI of multiple TSCICs (e.g., two components with indexes 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 with k corresponding CIC indices. When k=1, it is a single-component characteristic that can constitute / form a one-dimensional (1D) function because the CIC index spans all possible values. When k=2, a two-component characteristic can constitute / form a 2D function. In special cases, it can 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., weighted average / total value). Properties / STI / MI of objects / motions / expressions can be monitored based on any multi-component characteristic / total characteristic.

[0086] Characteristics / STI / MI may include: instantaneous / short-term / repeating / short-term / repeating / periodic / repeating / periodic / repeating / periodic / repeating / periodic / repeating / temporal / chronological / orthogonal / transformed / deterministic / probabilistic / primary / characteristic / significant / indicative / typical / prototypical / sustained / anomalous / sudden / abnormal / atypical / hazard / alarm / evolving / transient / transient quantity / characteristic / feature / information, cause and effect indicators / autocorrelation / covariance, autocorrelation function (ACF), spectrum / spectrography system / power spectral density, time / frequency function / transform / projection, initial / final / time / change / trend / trend / behavior / activity / history / profile / event, place / location / localization / spatial coordinates / change on map / route / navigation / tracking, line / rotation / horizontal / vertical / position / distance / displacement / height / velocity / acceleration / change / angular velocity, direction / orientation, size / length / width / height / orientation / area / volume / capacity, deformation / transformation, object / direction of motion / angle / shape / form / reduction / Magnification, behavior / activity / movement, occurrence, fall / accident / security / event, period / frequency / rate / cycle / rhythm / number / amount, timing / duration / interval, start / start / end / present / past / next time / amount / 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 / air velocity / inspiration / exhalation time / ratio, walking / tools / machines / complex movements, signals / movement characteristics nature / information / features / statistics / parameters / magnitude / phase / degree / dynamics / anomalies / variations / detection / estimation / recognition / identification / display, function slope / derivatives / higher order derivatives / features / mapping / transformation of other properties, discrepancy / distance / similarity score / cost / metric, Euclidean / statistical / weighted distance, L1 / L2 / Lk norm, dot product / cross product, tag, quantity inspected, quantity consumed / unconsumed, state / physical / health / well-being / emotional / mental state, output response, any configuration / combination, and / or any related property / information / combination.

[0087] Test quantities may be calculated. Characteristics / STI / MI may be calculated / monitored based on CI / TSCI / features / similarity scores / test quantities. Static (or dynamic) segments / profiles may be analyzed to analyze CI / TSCI / features / features / functions / test quantities / features / STI / MI (e.g., target motion / motion presence / detection / estimation / recognition / identification information) to calculate / analyze / obtain / acquire / mark / present / display / highlight / store / communicate identification information. Test quantities may be based on CI / TSCI / features / feature functions / features / STI / MI. Test quantities may be processed / examined / analyzed / compared.

[0088] Test quantities consist of any / all functions of the following: data / vector / matrix / structure, characteristic / STI / MI, CI information (CII, e.g., CI / CIC / feature / magnitude / phase), directional information (DI, e.g., directional CII), dominant / representative / characteristic / indicative / key / typical / exemplary / salient / common / shared / typical / prototypical / average / regular / consistent / normal / non-normal / non-representative data / vector / matrix / structure, similarity / discrepancy / distance score / cost / metric, auto / cross-correlation / covariance, sum / average / weighted / trim / arithmetic mean / geometric mean / harmonic mean, variance / deviation / absolute / squared variance / mean / median / sum / standard deviation / differential / slope / variation / total / absolute / squared variance / spread / variance / dispersibility, divergence / skewness / kurtosis / range / interquartile range / coefficient of variation / variance / L denominator / interquartile coefficient of variation / mean absolute value / squared difference / Gini coefficient / relative mean difference / entropy / maximum (max) / minimum (min) / median / percentile / quartile, variance-to-mean ratio, max-to-min ratio, variability / regularity / similarity measures, transient events / behavior, statistics / mode / likelihood / histogram / probability distribution function (pdf) / moment generating function / expectation function / value, behavior, repetition / periodicity / pseudoperiodicity, impulsivity / suddenness / occurrence / reproducibility, temporal profile / characteristics, time / minimum time / duration / period / frequency / trend / history, start / start / end time / quantity / count, movement classification / type, change, temporal / frequency / periodic change, etc.

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

[0090] Objects include people / pets / animals / plants / users, baby / man / experts / staff / personnel / personnel / personnel / nurses / technicians / servicemen / patients / students / travellers / inmates / tracked objects, vehicles / cars / drones / robots / wagons / transporting machinery / movable objects / items / goods / parts / machines / lifts / elevators, goods / luggage / people / items / packaging / luggage / equipment / workflow / assembly line / warehouse / factory / stores / supermarkets / distribution / logistics / transportation / manufacturing / retail / wholesale / business centres / facility / cleaning tools in hubs, phones / computers / laptops / tablets / dongles / plug-ins / companions / tools / peripherals / accessories devices, wearables, furniture, 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, lights, fixtures, stationary objects, televisions, cameras, audio, video, surveillance equipment, parts, tickets, parking passes, passes, airline tickets, credit cards, plastic cards, access cards, fixed, mutable, intangible objects, mass, solids, liquids, gases, fluids, smoke, fire, flames, signs, electromagnetic (EM) sources, media, and / or another object.

[0091] An object may have multiple parts, each with different motion (e.g., change of position / location / orientation). The object may be a person walking forward. While walking, his left / right hands may move in different directions with different instantaneous motions / velocities / accelerations.

[0092] The object may / may not be communicatively coupled to several networks such as WiFi, MiFi, 4G / LTE / 5G / 6G / 7G / 8G, Bluetooth / NFC / BLE / WiMax / Zigbee / mesh / adhoc networks, etc. AC-powered machinery that moves during installation, cleaning, maintenance, and renewal is bulky. It may be placed on / in a moving platform such as an elevator / conveyor / lift / pad / belt / robot / drone / forklift / car / boat / vehicle, etc. Type 1 / Type 2 devices can be attached to / moved on the object. The Type 1 / Type 2 device 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 the Type 1 / Type 2 / portable / another device may be attached to / not moved with the object and may have a 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) with a nearby device for network access. The nearby device may be an object / phone / AP / IoT / device / appliance / peripheral / amenity / furniture / vehicle / gadget / wearable / network / computing device. The nearby device may be connected to some server (e.g., a cloud server via a network / Internet). It may be portable / mobile or not, and may or may not move with the object. Type 1 / Type 2 / Portable / Proximity / Other devices can be battery / solar / DC / AC / other powered, swappable / non-swappable, rechargeable / non-rechargeable, or wirelessly charged.

[0093] Type 1 / Type 2 / Portable / Proximity / Other devices may include any of the following: Computer / Laptop / Tablet / Pad / Phone / Printer / Monitor / Battery / Antenna, Peripherals / Accessories / Sockets / Plugs / Chargers / Switches / Adapters / Dongles, Internet of Things (IoT), TV / Soundbar / HiFi / Speaker / Set-top Box / Remote Control / Panel / Gaming Console, AP / Cable / Broadband / Router / Repeater / Extender, Appliance / Utility / Fan / Refrigerator / Washer / Dryer / Microwave / Oven / Stove / Range / Light / Lamp / Pipe / Faucet / Lighting / Air Conditioner / Heater / Smoke Detector, Wearable Items / Watches / Eyeglasses / Goggles / Buttons / Bracelets / Chains / Jewelry / Rings / Belts / Clothing / Fabric / Shirts / Pants / Dresses / Gloves / Handwear / Shoes / Footwear / Hats / Headwear / Bags / Wallets / Purses / Makeup / Cosmetics / Decorative Items / Books / Magazines / Paper / Stationery / Signs / Posters / Displays / Printed Materials, Furniture / Fixtures / Table / Desk / Chair / Sofa / Bed / Cabinet / Shelf / Rack / Storage / Box / Bucket / Basket / Packaging / Car / Tile / Plate / Brick / Block / Mat / Panel / Curtain / Cushion / Pad / Carpet / Material / Building Material / Glass, Amenities / Sensor / Clock / Pot / Dishes / Container / Bottle / Can / Cookware / Plate / Cup / Bowl / Toy / Ball / Tool / Pen / Racket / Key / Bell / Camera / Microphone / Painting / Frame / Mirror / Coffee Maker / Door / Window, Food / Pill / Medicine, Implantable / Implantable / Gadget / Equipment / Apparatus / Instrument / Machine / Controller / Machine Tool, Garage Opener, Key / Plastic / Payment / Credit Card / Ticket, Solar Panel, Key Tracker, Fire Extinguisher, Trash Can / Dust Bin, WiFi Enabled Device, Smart Device / Machine / Machinery / System / House / Office / Building / Warehouse / Facility / Vehicle / Automobile / Bicycle / Motorcycle / Boat / Watercraft / Airplane / Cart / Wagon, House / Vehicle / Office / Factory / Building / Manufacturing / Production / Computing / Security / Other Devices.

[0094] One / two / more Type 1 / Type 2 / portable / proximity / another device / server may determine the initial characteristics / STI / MI of the object and / or share intermediate information. One of the Type 1 / Type 2 devices may move with the object (e.g., "Tracker Bot"). The other Type 1 / Type 2 device may not move with the object (e.g., "Origin Satellite", "Origin Register"). Both may have known characteristics / STI / MI. The initial STI / MI may be calculated based on the known STI / MI.

[0095] A venue can be a space such as: sensing area, room / house / home / office / workplace / building / facility / warehouse / factory / store / vehicle / real estate, indoor / outdoor / closed / semi-closed / open / semi-open / closed / air / floating / underground space / area / structure / enclosure, wood / glass / metal / material / structure / frame / beam / panel / column / wall / floor / door / ceiling / window / cavity / gap / opening / reflective / refractive medium / fluid / structural material / space / region with fixed / adjustable layout / shape, human body / animal / plant body / cavity / organ / bone / blood / blood vessel / air pipe / wind pipe / tooth / soft / hard / non-hard tissue, manufacturing / repair / maintenance / mining / parking / storage / transportation / ship / logistics / sports / recreation / amusement / public / entertainment / government / community / elderly / seniors / space facility / terminal / hub, logistics center / store, machinery / engine / equipment / assembly line / workflow, city / rural / suburban / metropolitan area, stairs / escalator / elevator / hallway / passage / tunnel / cave / cavern / waterway / duct / pipe / tube / lift / well / passage / roof / basement / sinkhole / alley / road / path / highway / sewer / ventilation system / network, car / truck / bus / van / container / ship / boat / submersible / train / tram / plane / mobile home, stadium / city / playground / park / field / track / court / gym / hall / market / supermarket / square / plaza / construction site / hotel / museum / school / hospital / university / garage / mall / airport / train station / bus stop / terminal / hub / platform, valley / forest / tree / terrain / landscape / garden / park / patio / land, and / or gas / oil / water pipe / line. A venue may consist of the interior or exterior of a building / facility. A building / facility may be one-story or multi-story, with some underground.

[0096] Events can be monitored based on the TSCI. Events can be object / motion / gesture / walk-related, such as falls, rotations / hesitations / poses, impacts, shocks (e.g., hitting a punching bag / door / bed / chair / table / desk / cabinet / box / another person / animal / bird / flying / ball / bowling / tennis / football / volleyball / soccer / baseball / basketball), two-body actions (e.g., balloon liberator / catching fish / moldable clay / paper / typing computer), movement in a garage, smartphone / person walking in a venue, autonomous / mobile object / machine motion (e.g., vacuum cleaner / utility / self-driving vehicle / car / vehicle / drone), etc.

[0097] Tasks may include: (a) Sensing task, i.e., monitoring / sensing / detection / recognition / estimation / verification / identification / authentication / classification / location / guidance / navigation / tracking / counting of / in any of the following: objects / vehicles / machines / tools / humans / babies / elderly / patients / intruders / pet presence / proximity / activity / daytime activity / health status / breathing / vital signs / heart rate / health status / sleep / sleep stages / gait / location / distance / speed / acceleration / navigation / tracking / motion / safety / hazard / fall / intrusion / security / life threat / movement / motion / decline / pattern / cyclical / repeating / periodic / steady / regular / transient / sudden / suspicious movement / irregularity / trend / change / breathing / human biometric information / environmental information / gate / gesture / room / area / zone / street; (b) Computational tasks, i.e., any of the following: signal processing / pre-processing / post-processing / conditioning / noising / calibration / analysis / feature extraction / transformation / mapping / supervised / unsupervised / semi-supervised / discrimination / machine / deep learning / learning / clustering / learning / PCA / eigendecomposition / frequency / time / function decomposition / neural network / map-based / model-based processing / correction / shape estimation / analysis computation; (c) IoT tasks, i.e., any of the following: smart tasks for venues / users / objects / humans / pets / houses / homes / offices / workplaces / buildings / facilities / warehouses / factories / stores / vehicles / properties / structures / assembly lines / IoT / devices / systems, energy / power management / transfer, wireless power transfer, interaction / collaboration with users / objects / intruders / humans / animals (presence / motion / gesture / walk / activity / behavior / voice / command / instruction / query / music / sound / image / video / location / movement / hazard / threat detection / recognition / monitoring / analysis / response / execution / synthesis, dialogue generation / retrieval / playback / display / rendering / composition / exchange / response / presentation / experience / media / multimedia / representation / sound / voice / music / image / video / animation / web page) page / text / message / notification / attention / inquiry / warning, user / intruder / object input / motion / gesture / location / activity detection / recognition / monitoring / interpretation / analysis / recording / storage), activation / control / setting (on / off / control / lock / unlock / open / close / adjust / setting) of devices / systems (e.g., vehicles / drones / electrical / machines / air conditioning / heating / lighting / ventilation / learning / entertainment / IoT / security / siren / access systems / devices / doors / windows / garages / lifts / elevators / escalators / speakers / TVs / lighting / peripherals / accessories / wearables / furniture / home appliances / amenities / gadgets / alarms / cameras / games / coffee / cooking / heaters / fans / housekeeping / household / office machines / devices / robots / vacuum cleaners / assembly lines), (d) Various tasks, namely, any of the following: transmission of data / parameters / analysis / derived data / coding / encryption / storage / analysis, upgrade / management / configuration / adjustment / broadcast / synchronization / network / encryption / communication / protection / compression / storage / database / archive / query / cloud computing / presentation / augmented / virtual reality / other processing / tasks. The tasks may be performed by some of the following: Type 1 / Type 2 / Nearby / Portable / Another device and / or Hub / Local / Edge / Cloud server.

[0098] Tasks may also include: detect / recognize / monitor / locate / interpret / analyze / record / store user / visitor / intruder / object / pet, interact / engage / talk / converse / exchange with user / object / visitor / intruder / human / baby / pet, detect / locate / recognize / monitor / analyze / interpret / learn / train / respond / perform / synthesize / generate / record / store / summarize health / health status / daily life / activities / behaviors / patterns / exercise / diet Intake / Toilet / Work / Play / Rest / Sleep / Relax / Danger / Routine / Limit / Habit / Tendency / Normal / Abnormal / Regularity / Irregularity / Change / Presence / Action / Gesture / Walk / Facial Expression / Emotion / State / Voice / Command / Instruction / Question / Inquiry / Music / Sound / Location / Movement / Fall / Threat / Discomfort / Illness / Environment / , Generate / Search / Play / Display / Render / Dialogue Synthesis / Exchange / Response / Presentation / Report / Experience / Media / Multimedia / Representation / Sound / Voice / Music / Image / Video / Animation / Web Page / Text / Message / Notification / Reminder / Inquiry / Warning, User / Intruder / Object Input / Movement / Gesture / Location / Activity Detection / Recognize / Monitor / Interpret / Analyze / Record / Store), Detect / Check / Monitor / Location / Manage / Control / Adjust / Configure / Lock / Unlock / Alarm / Disarm / Open / Close / Whole / Partial / Activate / Activate Turning on / off (e.g., vehicle / robot / drone / electrical / machine / air conditioning / heating / ventilation / HVAC / lighting / cleaning / entertainment / IoT / security / siren / access system / device / item / component, door / window / garage / lift / elevator / escalator / speaker / TV / lighting / peripherals / accessories / wearable / furniture / appliance / amenity / gadget / alarm / camera / game / coffee / cooking / heater / fan / housekeeping / home / office machine / device / vacuum cleaner / assembly line / window / garage / door / blind / curtain / panel / solar panel / sunshade), detecting / monitoring / locating where a user / pet is doing something (e.g., sitting on the couch / sleeping / sleeping in the bedroom / running on a treadmill / cooking / watching TV / eating in the kitchen / dining room / going up / down stairs / outside / inside / using the toilet),Do something (e.g. do something automatically upon detection (generate a message / response / alert / clarification / notification / report), do something automatically for the user upon detection of their presence, turn on / off / alarm / control / adjust / dimming lights / music / radio / TV / HiFi / STB / computer / speaker / smart device / air conditioning / ventilation / heating system / curtains / light shades, turn on / off / preheat / control a coffee maker / kettle / cooker / oven / microwave / another cooking appliance, check / manage temperature / settings / weather forecast / phone call / 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 a user arrives home in their car, the tasks may be to automatically detect the user / car's approach, open the garage door upon detection, turn on the driveway / garage light as the user approaches the garage, and / or turn on the air conditioner / heater / fan. When a user enters the house, 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 depending on the mood / current / impending event (e.g., romantic lighting / music because the user is having dinner with his / her girlfriend soon), microwave a hot meal prepared in the morning, check the weather forecast for tomorrow / news, user's interests, check the calendar / to-do list, answer the phone / messaging system / check email, verbally report using a dialogue system / speech synthesis, and / or play TV / entertainment. Using audio tools, using visual tools such as entertainment systems / computers / notebooks / displays / lights / colors / brightness / pattern symbols, 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 systems / tools, or any combination) to prepare a user for someone's birthday / phone call. A task might be to preemptively turn on an air conditioning / heating / ventilation system and / or preemptively adjust the temperature setting on a smart thermostat. When a user moves from the entrance to the living room, the task might be to 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 / states (e.g., adjust the lighting, select / play music to create a romantic atmosphere).

[0100] If a user wakes up in the morning, the tasks may be to detect the user's motion in the bedroom, open the blinds / curtains / window, turn off the alarm clock, adjust the night-to-day temperature profile, turn on the bedroom light, turn on the toilet light as the user approaches the toilet, check the radio / streaming channel, play the morning news, turn on the coffee machine, turn on the preheated water, and / or turn off the security system. If a user walks from the bedroom to the kitchen, the tasks may be to turn on the kitchen / hallway light, turn off the bedroom / toilet light, move music / messages / reminders from bedroom to bedroom, turn on the kitchen TV, change the TV to the morning news channel, lower the kitchen blinds, open the kitchen window, unlock the back door so the user can check the back yard, and / or adjust the kitchen temperature setting.

[0101] When the user leaves home for work, the task is to detect when the user has left the house, play a farewell / welcome message, open / close the garage door, turn on / off the garage / driveway lights, close / lock all windows / doors (in case the user has forgotten), turn off appliances (stove / microwave / oven, etc.), turn on / arm the security system, adjust the lighting / AC / heating / ventilation systems to an "away" profile to save energy, and / or send alerts / reports / updates to the user's smartphone.

[0102] Motion may include: no motion, motion sequence, stationary / non-moving motion, movement / position / location change, daily / weekly / monthly / yearly / repeated / activity / behavior / routine, transient / time varying / fall / repeated / periodic / quasi-periodic motion / breathing / heartbeat, deterministic / non-deterministic / stochastic / chaotic / random motion, complex / complex motion, non / quasi / cyclo / stationary random motion, change in electromagnetic properties, human / animal / plant / body / machine / vehicle / drone motion, Air / wind / weather / water / fluid / ground / earthquake motion, human-machine interaction, normal / abnormal / danger / warning / suspicious motion, impending / rain / fire / flood / tsunami / explosion / collision, head / face / eyes / mouth / tongue / neck / fingers / hands / arms / shoulders / upper / lower / body / chest / abdomen / waist / legs / feet / joints / knees / elbows / skin / subcutaneous / subcutaneous tissue / blood vessels / organs / heart / lungs / stomach / intestines / bowels / eating / breathing / speaking / singing / dancing / coordination motion, facial / eye / mouth expressions, and / or hands / arms / gestures / walking / UI / keystrokes / typing strokes.

[0103] A Type 1 / Type 2 device may include a heterogeneous IC, a low-noise amplifier (LNA), a power amplifier, a transmit / receive switch, a media access controller, a baseband radio, and / or a 2.4 / 3.65 / 4.9 / 5 / 6 / sub-7 / over-7 / 28 / 60 / 76 GHz / other radio. The heterogeneous IC may include a processor, memory, software, firmware, and instructions. It may support broadband / wireless / mobile / mesh / cellular networks, WLAN / WAN / MAN, standards / IEEE / 3GPP / 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 general-purpose / special-purpose / embedded / multi-core processors, microprocessors / microcontrollers, multi / parallel / CISC / RISC processors, CPUs / GPUs / DSPs / ASICs / FPGAs, and / or logic circuits. The memory may include non-volatile, RAM / ROM / EPROM / EEPROM, hard disks / SSDs, flash memory, CD- / DVD-ROMs, magnetic / optical / organic / storage systems / networks, network / cloud / edge / local / external / internal storage, and / or any non-transitory storage medium. The set of instructions may comprise machine-executable code in hardware / ICs / software / firmware and may be embedded / preloaded / loaded at boot-up / on-the-fly / on-demand / pre-installed / installed / downloaded.

[0105] Processing / pre-processing / post-processing may be applied to data (e.g., TSCI / features / characteristics / STI / MI / test quantities / intermediate / data / analysis) and may have multiple steps. Steps / processing / pre-processing / post-processing may include any of the following: operand / LOS / non-LOS / single-link / multi-link / component / item / quantity arithmetic functions, magnitude / norm / phase / feature / energy / time axis / similarity / distance / characteristic score / measure calculation / extraction / correction / cleaning, linear / non-linear / 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 / noising / smoothing / conditioning / spectral analysis / mean subtraction / removal, linear / nonlinear / inverse / frequency / time transform, Fourier transform (FT) / DTFT / DFT / FFT / wavelet / Laplace / Hilbert / Hadamard / trigonometric / sine / cosine / DCT / power of two / sparse / fast / frequency transform, zero / cyclic / padding, graph-based transformation / processing, decomposition / orthogonal / non-orthogonal / complete projection / eigendecomposition / SVD / PCA / ICA / compressed sensing, grouping / folding / sorting / comparison / soft / hard / threshold / clipping, first / second / higher order differentiation / integration / convolution / multiplication / division / addition / subtraction, local / global / maximization / minimization, recursion / iteration / constraint / batch processing, least mean square / absolute error / deviation, cost function optimization, neural network / detection / recognition / Classification / identification / estimation / labeling / association / tagging / mapping / remapping / learning / clustering / machine / supervised / unsupervised / semi-supervised learning / network, vector / quantization / encryption / compression / matching pursuit / scrambling / coding / storing / retrieval / send / receive / time domain / frequency domain / normalization / scaling / representation / combining / segmentation / tracking / monitoring / shape / silhouette / motion / activity / analysis, pdf / histogram estimation / importance / Monte Carlo sampling, error detection / protection / correction, do nothing, time-varying / adaptive processing, conditioning / weighting / averaging / selected components / over 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. Operations may be applied individually or jointly. Acceleration using GPU / DSP / coprocessor / multi-core / multi-processing can also be applied.

[0106] Functions may include: property / characteristic / magnitude / phase / energy, scalar / vector / discrete / continuous / polynomial / exponential / logarithmic / trigonometric / transcendental / logical / piecewise / linear / algebraic / nonlinear / circular / piecewise linear / real / complex / vector-valued / reciprocal / absolute value / index / limit / floor / circular / sign / composite / slide / shift function, differentiation / integration, function of functions, one-to-one / one-to-many / many-to-one / many-to-many functions, mean / mode / median / percentile / max / min / range / statistics / histogram, local / global max / min / 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 / squared / cubed / root / power, thresholding / clipping / rounding / truncation / quantization / approximation, time functions operated on (e.g. filtering), sine / cosine / tangent / bitangent / elliptic / parabolic / hyperbolic / game / zeta function, stochastic / probabilistic / random / ergodic / stationary / deterministic / periodic / repeated functions, inverse transform / frequency / discrete time / Laplace / Hilbert / sine / cosine / triangle / wavelet / integer / power of two / sparse transform Transformation, orthogonal / non-orthogonal / eigenprojection / decomposition / eigenvalues / singular values / PCA / ICA / SVD / compressed sensing / neural network / feature extraction / function of moving window of nearby items in time series / filtering function / convolution / short-time / discrete transform / Fourier / cosine / sine / Hadamard / wavelet / sparse transform / matching pursuit / approximation, graph-based processing / transform / graph signal processing, classification / identification / class / group / category / labeling, processing / preprocessing / postprocessing, machine / learning / detection / estimation / feature extraction / learning network / feature extraction / noise removal / signal enhancement / Encoding / Encryption / Mapping / Vector Quantization / Remapping / Lowpass / Highpass / Bandpass / Matched / Kalman / Particle / FIR / IIR / MA / AR / ARMA / Median / Mode / Adaptive Filtering, 1st / 2nd / Higher Order Differentiation / Integration / Zero Crossing / Smoothing, Up / Down / Random / Importance / Monte Carlo Sampling / Resampling / Transformation, Interpolation / Extrapolation, Short Term / Long Term Statistics / Auto / Cross Correlation / Moment Generating Functions / Time Average / Weighted Average, Special / Bessel / Beta / Gamma / Gaussian / Poisson / Integral Complementary Error Functions.

[0107] The sliding time window may vary in width / size over time. It may be initially small or large to allow for fast and accurate imaging, and over time, it may increase / decrease to a steady-state size comparable to the frequency / period / duration / characteristics / STI / MI of the monitored motion. The window size / time shift between adjacent windows may be constant / adaptive / dynamic / automatically / changed / adjusted / fluctuated / modified (e.g., based on battery life / power consumption / available computing power / changes in the volume of monitored objects / nature of monitored motion / user request / selection / instruction / command).

[0108] The characteristic / STI / MI may be determined based on characteristic values / points of the function and / or the function's associated arguments (time / frequency, etc.). The function may be the result of a regression. The characteristic values / points may include the local / global / constrained / significant / first / second / i-th maximum / minimum / extremum / zero-crossing (e.g., with positive / negative time / frequency / argument) of the function. A local signal-to-noise ratio (SNR) or SNR-like parameter may be calculated for each pair of adjacent local maxima (peaks) / local minima (troughs) of the function, which may be some function (e.g., linear / logarithmic / exponential / monotonic / power / polynomial) of the fraction or difference of the quantity (e.g., power / magnitude) of the local maximum relative to the quantity 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 (or less) than another threshold. The local maximum / minimum may be selected / identified / calculated using a persistence-based approach. Several significant local maxima / minima may be selected based on selection criteria (e.g., quality criteria / conditions, strongest / consistently significant peak within a range). Unselected significant peaks may be stored / monitored as "reserved" peaks for use in future selections in future sliding time windows. For example, a particular peak (e.g., at a particular 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 the previously selected peak (momentarily strong / dominant, but not persistent / consistent). Peak consistency may be measured by the trace or duration of significance. Alternatively, local maxima / minima may be selected based on a finite state machine (FSM). The decision threshold may be time-varying and adjusted adaptively / dynamically (e.g., based on backtrace timing / FSM, or data distribution / statistics).

[0109] A similarity score (SS) / component SS can be computed based on two temporally adjacent CIs / CICs, one TSCI, or two different TSCIs. Pairs may originate from the same / different (one or more) sliding windows. 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 / metric / quality index, signal quality criteria, statistical features, discriminative scores, neural networks / deep learning networks / machine learning / training / discrimination / weighted average / preprocessing / noising / signal conditioning / filtering / time correction / timing compensation / phase offset compensation / transform / component-wise operation / feature extraction / FSM, and / or other scores.

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

[0111] Local extremum search may involve constrained / minimization / maximization, statistical / dual / constrained / convex / global / local / combinatorial / infinite-dimensional / multi-objective / multimodal / non-differential / particle swarm / simulation-based optimization, linear / nonlinear / quadratic / higher-order regression, linear / nonlinear / stochastic / constrained / mechanical / mathematical / connection-free / convex / semi-defined / cone / interior / fractional / integer / sequential / quadratic programming, conjugate / gradient / subgradient / coordinate / contractive descent, Newtonian / complex / iterative / point / elliptic / quasi-Newtonian / interpolation / memory / genetic / evolutionary / pattern / gravitational search / algorithms, constraint satisfaction, calculus of variations, optimal control, space mapping, heuristics / metaheuristics, 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, calculus of variations, and / or calculus of variations. It can be associated with an objective function / loss function / cost function / utility function / fitness function / energy function.

[0112] Regression may be performed using a regression function to fit the data or a function of the data (e.g., ACF / transformed / mapped) within the regression window. The length / position of the regression window may be changed during the iterations. The regression function may be linear / quadratic / cubic / polynomial / other. The regression may minimize the mean / weighted / absolute / squared deviation, error, total / component / weighted / mean / absolute / squared / higher order / other error / cost (e.g., in a projected domain / selected axis / orthogonal axis), robust error for smaller error magnitudes (e.g., first error (e.g., squared), second error (e.g., absolute) for larger error magnitudes), 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). The regression parameters (e.g., For example, the maximum / minimum regression error of the regression function in the regression window, the time offset associated with the window position / width) may be initialized and / or updated during the iteration (e.g., based on target values / ranges / profiles, characteristics / STI / MI / test quantities, object motion / amount / count / location / status, past / current trends, location / amount / distribution of extreme values ​​in the previous window, signal carrier / subcarrier frequency / bandwidth, amount of antenna associated with the channel, noise characteristics, histogram / distribution / center / F-distribution, and / or thresholds). Upon convergence, the current time offset becomes the center / left / right (or a fixed relative position) of the regression window.

[0113] In a presentation, information may be displayed / presented (e.g., using a venue map / environment model). Information may include: current / past / corrected / approximate / map / location / speed / 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 / speed / vital signs / breathing / heart rate / activity / emotion / sleep / status / rest information, motion statistics / MI / STI, presence or absence of moving body / person / pet / object / vital signs, gesture (e.g., hand / arm / leg / body / head / face / mouth / eye) / meaning / control (controlling a device using gestures), location-based gesture control / motion interpretation, ID / identifier (ID) (e.g., object / person / user / pet / zone / region, device / machine / vehicle / drone / car / boat / bicycle / TV / air conditioner / fan / self-guided machine / device ID / identifier). Objects / people / users / pets / zones / areas, devices / machines / vehicles / drones / cars / boats / bicycles / TVs / air conditioners / fans / self-guided machines / devices / vehicles), environment / weather information, gestures / gesture control / motion traces, earthquakes / explosions / storms / fires / temperature, collisions / shocks / vibrations, events / doors / windows / opening / closing / falls / accidents / burning / freezing / water / wind / air movement events, recurring / quasi-periodic events (e.g., running on a treadmill, jumping, skipping rope, somersaults, etc.), and / or vehicle events. Location may be in one / two / three dimensions (e.g., represented / represented as 1D / 2D / 3D rectangular / polar coordinates), relative (e.g., rt map / environment model), or relational (e.g., at / near / distance from / between two points, around a corner, upstairs, on a tabletop, on the ceiling, on the floor, on a sofa).

[0114] Information (e.g., location) may be marked / displayed with some kind of symbol. The symbol may change over time / flash / pulse, changing color / intensity / size / orientation. The symbol may be a number reflecting 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. Information may be textual or presented visually / verbally (e.g., using pre-recorded voice / speech synthesis) / mechanically (e.g., animated gadget, moving parts).

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

[0116] The map / floor plan / environment model (e.g., home / office / building / store / warehouse / facility) may be 2 / 3 / higher dimensional. It may change / evolve over time (e.g., rotate / zoom / move / jump on the screen). Walls / windows / doors / entrances / exits / restricted areas may be marked. It may contain multiple layers (overlays). It may include maintenance maps / models including water pipes / gas pipes / cables / air ducts / crawl spaces / ceiling / underground layouts.

[0117] A venue may be divided / subdivided / zoned / grouped into multiple zones / regions / sectors / sections / territories / districts / districts / neighborhoods / areas / stretches / expanses such as bedroom / living / dining / resting / storage / utility / warehouse / meeting / work / hallway / kitchen / foyer / garage / ground floor / second floor / office / reception room / area / region etc. It may be presented in a map / floor plan / model with presentation properties (e.g. brightness / intensity / luminance / color / chrominance / texture / animation / flash / rate).

[0118] An example of the disclosed system / apparatus / 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: the kitchen, dining room, and living room, with the dining room in the middle. A is in the dining room, B is in the kitchen, and C is in the living room, dividing the first floor into three zones (dining room, living room, kitchen). When motion is detected by the AB pair and / or AC pair, the system analyzes the TSCI / features / characteristics / STI / MI and associates the motion with one of the three zones.

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

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

[0121] On a typical day, Mom wakes up at 7 AM, spends 20 minutes cooking breakfast in the kitchen, and 30 minutes eating breakfast in the dining room. She then does her daily exercise in the living room, and then sits on the couch in the living room and watches her favorite TV show. The motion detection system allows Stephen to see the timing of motion in three areas of the house. If the motion agrees with the daily routine, Stephen knows that Mom should be generally fine. However, if the motion pattern is abnormal (e.g., no motion until 10 AM or too much time in the kitchen / stillness), Stephen suspects something is wrong and calls Mom to check on her. Stephen can even get someone (e.g., family member / neighbor / paid staff / friend / social worker / service provider) to check on Mom.

[0122] One day, Stephen feels like relocating the device. He simply unplugs it from its AC power plug and plugs it into another AC power plug. He's happy that the motion detection system is plug-and-play, and repositioning doesn't affect the system's operation. It works as soon as he powers it on.

[0123] Stephen then decides to install a similar setup (i.e., one Type 2 and two Type 1 devices) on the second floor to monitor the bedrooms on the second floor. Again, the system is very easy to set up; he simply plugs the Type 2 and Type 1 devices into AC power plugs on the second floor. No special installation is required. He can monitor motion on both the ground and second floors using the same mobile app. Each Type 2 device on the ground and second floors can interact with all Type 1 devices on the ground and second floors. Stephen has more than twice the capacity of the combined system.

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

[0125] In some embodiments, to perform a wireless sensing task, motion statistics (MS) or motion information (MI) may be computed within a sliding time window based on all CIs (e.g., CSI / CIR / CFR) within the sliding time window. The task may be to monitor the motion of an object within a venue based on the MS / MI. The MS / MI may be STIs or characteristics of an object or object motion. In some cases, a CI may be abnormal (e.g., due to interference, noise), thereby causing abnormal behavior of the MS / MI and disrupting / disrupting the wireless sensing task. Such abnormal CIs (and abnormal MS / MI) may be considered outliers. In some embodiments, a scheme is disclosed for detecting / suppressing / remove / exclude outliers or the influence of outliers within a sliding time window. The system may classify the sliding time window as either "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 normal way, and tasks may be performed based on the MS / MI. For a "highly abnormal" sliding time window (or, for example, a highly unreliable / irregular time window with lots of abnormal / outlier CIs), the MS / MI for the current window may not be calculated, and tasks may be performed in an alternative way (e.g., using a replacement MS / MI calculated from adjacent MS / MIs) without the MS / MI for the current time window. For a "moderately abnormal" sliding time window (or, for example, a moderately reliable / irregular time window having an acceptable amount of good / normal / reliable CIs in a particular sequence (e.g., at least N1 consecutive ones) at a particular position (e.g., at the beginning / end / middle) within the sliding time window), an alternative MS / MI (e.g., a simplified / reduced / alternative MS / MI) may be computed based on some good / normal / reliable CIs within the sliding time window so that tasks can be performed based on the alternative MS / MI (which may replace the MS / MI). The alternative MS / MI may be an MS / MI computed without the abnormal CIs (i.e., with the abnormal CIs removed or excluded). Not all good / normal / reliable CIs may be included in the computation of the alternative MS / MI.

[0127] In some embodiments, a test score (TS) may be calculated. A characteristic value may be calculated for a 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 may be two, three, or more. Some or all of the M temporally adjacent CIs may be contiguous. Each TS may be a scalar. Each TS may be associated with a distinct time. The test score may include / be any of the following, or may include / be a measure or score of: similarity, dissimilarity, difference, distance, norm, distinction, ratio, proportion, variance, variation, divergence, spread, deviation, TRRS, correlation, covariance, autocorrelation, cross-correlation, dot 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 a Rx antenna of a Type 2 device. There may be one or more links for a Type 1 / Type 2 device pair, with two or more associated TSCIs.

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

[0129] In some embodiments, some components / associated CTSs may be "selected" (e.g., based on the K largest (magnitude) CTSs, or CTS magnitude > threshold, or a function of individual CTSs relative to other CTS functions), and TS(t) may be a composite value of only the selected CTSs / quantities (e.g., function, magnitude, phase, magnitude squared) for the selected CTSs. If TS is a weighted quantity (e.g., weighted sum, weighted average, weighted product) of CTSs, the CTSs of selected components may have a greater weight than the CTSs of non-selected components. Weights may be calculated for each component (e.g., based on the CTS of the component relative to the CTSs of other components). TS may be a weighted quantity of CTSs, where each CTS is weighted by an individual weight.

[0130] In some embodiments, a link-wise test score (LTS) may be calculated based on the CIs within a sliding time window of the TSCI. For example, the LTS may be a first composite 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 TSCIs within the sliding time window. For a link, the sliding time window may be classified as "normal," "moderately abnormal," or "severely abnormal" based on two or more TSs associated with the LTS or TSCI. A link-wise MS / MI (or an alternative link-wise MS / MI) may / may not be calculated based on the CIs within the sliding time window of the TSCI. Any overall value may include any of the following: average, weighted average, 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, a device pairwise test score (TTS) may be calculated for 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 aggregate value of two or more associated LTSs, each LTS being associated with a separate link. The TTS may also be an “overall” test score based on all CIs included in all of the 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 being associated with a separate link). A 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. A test score (TS) associated with time t, TS(t), may be a difference score, such as a CI difference (CID) between two or more temporally adjacent CIs. In a time window with N CIs within each TSCI, the system may compute (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, feature f(.) is / includes magnitude / magnitude squared / norm / absolute value / power of it or a monotonic function) may be computed.

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

[0134] In some embodiments, the TS can be any CID or any CIDF or any combination. A large TS may suggest / indicate abrupt change, anomaly, or high probability / likelihood of anomaly. The LTS can be an aggregate value of the N-1 CIDs (e.g., arithmetic / geometric / harmonic / trimmed / weighted average or sum, median / mode / maximum / percentile / minimum, variance / variation / 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. The variance / variation / divergence may reflect fluctuation behavior.

[0135] To perform CI-level abnormality classification, for a CI at time t, if the associated TS(t) satisfies a first condition (e.g., greater than or less than a threshold), it may be classified / calculated / determined as "abnormal" / outlier / atypical / uncommon / unusual / unrepresentative / fault / irregular / strange / irregular / deviation / divergence / eccentricity / exceptional / idiosyncratic / reverse / perverse / corrupted. 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) may 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 may be that TS(t) is smaller than a threshold. For an "abnormal" CI, an "abnormality score" / AS (e.g., a real number between 0 and 1, or between -1 and +1) may be calculated (e.g., based on TS, or based on all / selected associated CTSs, or based on a certain number of adjacent / temporally adjacent TSs or associated CTSs). For a run of consecutive abnormal CIs, a run-wise abnormality score / RAS may be calculated as the combined value (sum, weighted sum, product, weighted product, mean, weighted mean / median / mode, (weighted) arithmetic / geometric / harmonic mean) of the ASs of each abnormal CI in the run.

[0136] To classify the abnormality at the link level, 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". When it is "abnormal", if 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 the level of abnormality 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 in the time window (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 abnormality score / AS / run-wise AS / RAS. Any threshold can be predetermined or calculated adaptively based on the relevant 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 / concatenated) "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., the 90th percentile) of TS(t) in the sliding time window of link k is smaller than a certain threshold.

[0140] In some embodiments, any threshold may be predetermined or adaptively calculated based on the associated abnormality score / AS / run-wise AS / RAS. In some embodiments, MS / MI may be calculated differently according to the classification of “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 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 the first condition is met, or the distribution characteristics of the abnormal CI in link k meet a condition). In some embodiments, LTS(k) may be a score / measure of a window-wide total value, maximum value, minimum value, 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 (a Type 1 device with M1 antennas and a Type 2 device with M2 antennas, such that M=M1*M2), and thus includes M links and M associated TSCIs, the sliding time window of the device pair can be determined / classified / calculated as “anomalous” if a third condition is met (the TTS is greater than or less than a threshold, or the majority / minority / percentage / sufficient amount / minimum amount / maximum amount of the LTS is greater than / less than another threshold or meets a second condition, or the majority / minority / percentage / sufficient amount / minimum amount / maximum amount of the TS within the slicing time window is greater than / less than yet another threshold or meets a first threshold, or the distribution characteristics of the anomalous links meet a certain condition, or the distribution characteristics of the anomalous CIs meet another condition). Otherwise, the sliding time window of the device pair can be classified as “normal.”

[0142] In some embodiments, if the current time window is “normal,” MS / MI may be computed for the current sliding time window based on all CIs on link k (or two or more links) within the time window. This may be the usual way to compute MS / MI. If the current time window is “abnormal,” especially if it is “severely abnormal,” MS / MI may / may not be computed based on the CIs in the current time window. If already computed, the MS / MI may be discarded (or may not be used). An alternative / replacement / alternative / auxiliary / fallback / standby / fill-in / stand-in / proxy MS / MI may be used. The alternative MS / MI may be a predicted / estimated / replacement MS / MI value computed based on several neighboring MS / MIs (e.g., temporally adjacent, concurrent, past, or future, or spatially adjacent, such as other TSCIs of the same TX / RX device pair, or TSCIs of “adjacent” TX / RX device pairs). The alternative MS / MI may be an aggregate value, such as the mean / median / mode / weighted average / trimmed mean, or a zeroth / first / second / higher-order predictor / estimator, or another aggregate value of multiple adjacent MS / MIs. If the current time window is "moderately abnormal," a reduced / partial / simplified / limited / tilted / biased / partially / distorted / colored / one-sided MS / MI may be computed based on some (or all) remaining / available "normal" CIs within the current time window. For example, a reduced MS / MI may be computed 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 the first or last run of normal CIs (having a sufficient run length, e.g., run length > T2) within the time window.

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

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

[0145] 1 illustrates an exemplary block diagram of a first wireless device (e.g., a bot 100) of a wireless sensing or monitoring system in accordance with one embodiment of the present teachings. The bot 100 is an example of a device that may be configured to implement various methods described herein. As shown in FIG. 1, the bot 100 includes a housing 140 that includes a processor 102, a memory 104, a transceiver 110 including 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, processor 102 controls the overall operation of 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, discrete hardware components, dedicated hardware finite state machine, or any other suitable circuit, device and / or structure capable of performing arithmetic or other manipulation of data.

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

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

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

[0150] The synchronization controller 106 in this example may be configured to control the operation of the bot 100 to be synchronized 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 synchronize with the origin that receives the wireless signal transmitted by the bot 100. In another embodiment, the synchronization controller 106 may control the bot 100 to transmit a wireless signal asynchronously with the other bots. In another embodiment, the bot 100 and the other bots may each transmit a wireless signal individually and asynchronously.

[0151] Carrier configurator 120 is an optional component within bot 100 for configuring transmission resources (e.g., time and carrier) for transmitting the wireless signal generated by wireless signal generator 122. In one embodiment, each CI in the time series of CIs has one or more components that each correspond to a carrier or subcarrier of the transmission of the wireless signal. Motion detection can be based on motion detection with respect to any one or any combination of the components.

[0152] Power module 108 may include a power source, such as one or more batteries, and a power regulator for providing regulated power to each of the aforementioned modules of Figure 1. In some embodiments, if bot 100 is coupled to a dedicated external power source (e.g., a wall outlet), 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, for example, a power bus, a control signal bus, and / or a status signal bus. It will be appreciated that the modules of the bot 100 may be operably coupled to one another using any suitable technology and medium.

[0154] 1, one skilled in the art will understand that one or more of the modules may be combined or implemented commonly. For example, processor 102 may implement the functionality described above with respect to processor 102 as well as the functionality described above with respect to wireless signal generator 122. Conversely, each of the modules shown in FIG. 1 may be implemented using multiple separate components or elements.

[0155] 2 illustrates an exemplary block diagram of a second wireless device (e.g., origin 200) of a wireless sensing or monitoring system in accordance with one embodiment of the present teachings. Origin 200 is an example of a device that may be configured to implement various methods described herein. Origin 200 in this example may function as an origin, a receiver, a Type 2 device, or an AP in the system disclosed herein. As shown in FIG. 2 , origin 200 includes a housing 240 that includes a processor 202, a memory 204, a transceiver 210 including 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, processor 202, memory 204, transceiver 210, and power module 208 operate similarly to processor 102, memory 104, transceiver 110, and power module 108 in bot 100. Antenna 250 or multi-antenna array 250 is typically mounted in housing 240 and electrically coupled to transceiver 210.

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

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

[0159] The synchronization controller 206 in this example may be configured to control the operation of the origin 200 to be synchronous 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 synchronize with a bot transmitting a wireless signal. In another embodiment, the synchronization controller 206 may control the origin 200 to receive a wireless signal asynchronously with another origin. In another embodiment, the origin 200 and the other origins may each receive a wireless signal separately and asynchronously. In one embodiment, the optional motion detector 222 or a motion detector external to the origin 200 is configured to asynchronously calculate separate heterogeneous motion information related to the motion of an object based on separate time series of 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, for example, a power bus, a control signal bus, and / or a status signal bus. It will be appreciated that the modules of the origin 200 may be operably coupled to one another using any suitable technology and medium.

[0161] 2, one skilled in the art will understand that one or more of the modules may be combined or implemented commonly. For example, processor 202 may implement the functionality described above with respect to processor 202 as well as the functionality described above with respect to channel information extractor 220. Conversely, each of the modules shown in FIG. 2 may be implemented using multiple separate components or elements.

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

[0163] 3 shows a flowchart of an exemplary method 300 for precision wireless monitoring according to some embodiments of the present disclosure. In various embodiments, the method 300 may be performed by the systems disclosed above. In operation 302, a wireless signal is transmitted from a first wireless device through a wireless multipath channel of a venue, where the wireless multipath channel is affected by the motion of an object within the venue. In operation 304, the wireless signal is received by a second wireless device through the wireless multipath channel, where the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion of the object. In operation 306, time series of channel information (TSCI) of the wireless multipath channel is obtained based on the received wireless signal. In operation 308, a sliding time window classification is performed by analyzing the channel information (CI) included 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 sliding time window classification. In operation 312, the motion of the object is monitored based on the MI.

[0164] 4 shows a flowchart of an example method 400 for performing sliding time window classification according to some embodiments of the present disclosure. In various embodiments, the 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 MI / MS / STI for the current sliding time window using different methods based on the at least three classes. In operation 404, if the current sliding time window is window class 1 (e.g., "NORMAL"), the system may calculate MI / MS / STI using a first method based only on CIs included in TSCIs in the current sliding time window. In operation 406, if the current sliding time window is window class 2 (e.g., "SEVERELY ABNORMAL"), the system may calculate MI / MS / STI using a second method based on at least one CI included in TSCIs outside the current sliding time window. In operation 408, if the current sliding time window is of window class 3 (e.g., "moderately abnormal"), the system may calculate MI / MS / STI in a third manner based on a first subset of CIs included in TSCIs within the current sliding time window without using a second subset of CIs included in TSCIs within the sliding time window, where the first subset and the second subset are disjoint.

[0165] 5 shows a flowchart of an exemplary method 500 for computing a test score (TS) and performing item-wise classification for each CI, according to some embodiments of the present disclosure. In various embodiments, the 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 in the sliding time window based on one of sub-operations 512, 514. In suboperation 512, the system may calculate a TS for the CS based on the temporally adjacent CIs. In suboperation 514, the system may calculate a component test score (CTS) for each component of the CI, and then calculate a TS for the CI as an overall value of the CTSs of the CIs. 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, overall value, difference, absolute / squared difference, variance / variability / variability, deviation / standard deviation, spread, dispersion, 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 difference between a linear combination of CI(t) and CI(t+k) for k=+-1 / 2 / 3 / ...., difference between a linear combination of F(CI(t)) and F(CI(t+k)), dot product between a linear combination of CI(t) and CI(t+k) for k=+-1 / 2 / 3 / ...., dot product between a linear combination of F(CI(t)) and F(CI(t+k)), dissimilarity score between a linear combination of CI(t) and CI(t+k) for k=+-1 / 2 / 3 / ...., or 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 included in the TSCI within the sliding time window based on the individual TS.

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

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

[0168] 8 shows a flowchart of an example method 800 for computing MI / MS / STIs according to some 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 compute at least one tentative MI / MS / STI for a sliding time window. Each tentative MI / MS / STI is based on a separate selection run of first-class CIs included in the TSCIs within the sliding time window. This operation may include sub-operations 812 and 814. In suboperation 812, the system may calculate a particular interim MI / MS / STI based on a composite run of CIs included in the TSCI formed by combining a leading (selected) run in the sliding time window with a trailing run in the previous sliding time window. In suboperation 814, the system may calculate a particular interim MI / MS / STI based on a composite run of CIs included in the TSCI formed by combining a trailing (selected) run in the sliding time window with a leading 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, 824. In suboperation 822, the system may calculate the MI / MS / STI as a weighted aggregate of all tentative MI / MS / STIs, each of which is weighted by an individual weight calculated based on the run length of the individual selection run. In suboperation 824, the system may calculate the MI / MS / STI as a aggregate of all tentative MIs and at least one adjacent MI, each of which is associated with one of a past or future adjacent sliding time window of a CI included in the TSCI, or an adjacent sliding time window of a CI included in another TSCI.

[0169] 9 shows a flowchart of an example method 900 for performing compensated wireless monitoring according to some embodiments of the present disclosure. In various embodiments, the method 900 may be performed by one or more systems such as those disclosed above. In operation 910, a wireless signal is transmitted from a type 1 heterogeneous device to a type 2 heterogeneous device over a wireless channel that is affected by the motion of objects in the venue. In operation 920, a time series channel information (TSCI) of the wireless channel is obtained based on the received wireless signal using a processor, memory, and set of instructions. In operation 930, the system may calculate time series of motion information (TSMI) based on the TSCI, and may calculate time series analysis (TSA) based on the TSMI. In operation 940, the system may compute a compensated analysis value of the time series by applying a compensation to the calculation of the TSA, where the compensation includes a monotonic mapping. In one embodiment, the system may modify the compensation / monotonic mapping in operation 950 based on changes in target behavior, wireless signals, bandwidth, bands, specifications, settings, user input, conditions, events, time tables, strategies, and plans, apply the modified compensation, and then monitor object motion based on the compensated time series analysis in operation 960. In another embodiment, the system may perform operation 940 and then directly monitor object motion based on the compensated time series analysis in operation 960.

[0170] 10 shows a flowchart of an exemplary method 1000 for computing an analysis value of a compensated time series according to some embodiments of the present disclosure. In various embodiments, the method 1000 may be performed by a system such as those disclosed above. In operation 1010, the system may apply compensation to the TSA operation. The compensation may include a monotonic mapping. In some embodiments, the monotonic mapping may include 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 / regression / 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 generic 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 CI / IV / MI / MS / STI / analytes, features / magnitudes / phases / components of CI / IV / MI / MS / STI / analytes, TSCI / TSMI / TSA. In operation 1022, the system may compute a compensated CI based on the compensation / monotonic mapping applied to the CI. In operation 1024, the system may compute a 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 analysis value based on the compensation / monotonic mapping applied to the CI / IV / MI / MS / STI, the compensated CI / IV / MI / MS / STI, the compensated features / phases / magnitudes / components of the CI / IV / MI / MS / STI, or the analysis value.

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

[0172] 12 shows a flowchart of an example method 1200 for determining a monotonic mapping according to some embodiments of the present disclosure. In various embodiments, the 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 an individual compensation that includes the candidate monotonic mapping to the calculation of the TSA, and (b) calculate an individual similarity score between the target behavior and the resulting behavior associated with the calibration CI / MI / MS / STI / analysis value after the individual compensation has been applied. 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 networks. In operation 1240, the system may select the monotonic mapping as the candidate monotonic mapping with the highest similarity score.

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

[0174] 14 illustrates a flowchart of an example method 1400 for estimating a monotonic mapping, in accordance with some embodiments of the present disclosure. In various embodiments, the 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 a univariate observable X associated with the CI / MI / MS / STI / analyte based on the calibration CI / MI / MS / STI / analyte, where the behavior includes a 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 (eg, based on performing one of suboperations 1432, 1414). In sub-operation 1432, the system may estimate the monotonic mapping as F_Y^{-1}[F_X(X)], where F_Y^{-1} is the inverse of the function F_Y. In sub-operation 1434, the system may estimate the monotonic mapping as an approximation of F_Y^{-1}[F_X(X)], where F_Y^{-1} is the inverse of the function F_Y. In some embodiments, the approximation includes a linear / affine / quadratic / cubic / polynomial / exponential / logarithmic / convex / concave / spline map, a piecewise linear / quadratic / cubic / polynomial map, or a monotonically increasing / non-decreasing map.

[0175] 15 shows a flowchart of an example method 1500 for selecting a monotonic mapping according to some embodiments of the present disclosure. In various embodiments, the method 1500 may be performed by a system such as those disclosed above. In operation 1510, during a calibration stage, the system may determine a monotonic mapping to include a first and a second monotonic mapping. In operation 1520, the system may determine and search several candidate first monotonic mappings. In operation 1530, for each candidate first monotonic mapping, the system may (a) estimate an individual second monotonic mapping, (b) apply an individual compensation including the candidate first monotonic mapping and the individual estimated second monotonic mapping to the calculation of the TSA, and (c) calculate an individual similarity score between the target behavior and the resulting behavior associated with the calibration CI / MI / MS / STI / analysis value after the individual compensation has been applied. In operation 1540, the system may select the monotonic mapping as the candidate first monotonic mapping and the respective estimated second monotonic mapping with the largest similarity score.

[0176] 16 illustrates a flowchart of an exemplary method 1600 for computing a target behavior, according to some embodiments of the present disclosure. In various embodiments, the method 1600 may be performed by a system such as those disclosed above. In operation 1610, in a pre-calibration stage prior to the calibration stage, a reference wireless signal is transmitted from a reference type 1 device to a reference type 2 device over a reference wireless channel that is affected by a reference motion of a reference object in a reference venue. In operation 1620, a reference processor / memory / instructions is used to obtain a time series of reference CIs for the reference wireless channel based on the received reference wireless signal. In operation 1630, the system may calculate a time series of reference MI / MS / STI based on the time series of reference CI, and may calculate a time series of reference analysis values ​​based on the time series of 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 of reference CI, a time series of reference MI, or a time series of reference analysis values.

[0177] 17 illustrates a flowchart of an example method 1700 for updating a monotonic mapping, in accordance with some embodiments of the present disclosure. In various embodiments, the method 1700 may be performed by a system such as those disclosed above. In operation 1710, in a recalibration phase after the calibration phase, the system may obtain a recalibration TSCI. In operation 1720, the system may calculate a time series of recalibrated MI / MS / STI based on the time series of recalibrated CI, and may calculate a time series of recalibrated analysis values ​​based on the time series of recalibrated MI / MS / STI. In operation 1730, the system may compare the behavior of the recalibrated CI / MI / MS / STI / analyte values ​​of the time series to the target behavior. In operation 1740, the system may compute an updated monotonic mapping based on the comparison. In operation 1750, the system may replace the monotonic mapping with an updated monotonic mapping in compensation for the operation of the TSA.

[0178] The order of operations in any one of the figures herein may be varied according to various embodiments of the present teachings.

[0179] Although motion statistics (MS) or motion information (MI) may be calculated in a sliding time window to perform wireless sensing and monitoring, one objective of the present teachings is to provide precise wireless monitoring by detecting, suppressing, removing, and / or eliminating outliers or the influence of outliers in 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. If "normal," the MS / MI may be calculated in the usual manner. If "severely abnormal," the MS / MI may be calculated based on neighboring CIs or MS / MIs rather than based on CIs within the sliding time window. If "moderately abnormal," the MS / MI may be calculated based on good / reliable (or "normal") CIs within the sliding time window. Bad / 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, the method including: transmitting a wireless signal from a first wireless device through a wireless multipath channel of a venue, the wireless multipath channel being affected by motion of an object within the venue; receiving the wireless signal by 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 the object; using a processor, a memory communicatively 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 sliding time window classification by analyzing CI (Channel Information) included in the TSCI within the sliding time window; computing MI (Motion Information) for the sliding time window based on the TSCI and the sliding time window classification; and monitoring the motion of the object based on the MI.

[0182] Item 2. The method described in Item 1, wherein the 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 feature points of the ACF.

[0183] Item 3. The method of item 1, further comprising: if the sliding time window is classified as a first sliding window class based on the classification, calculating the MI using a first method based only on CIs included in TSCIs within the sliding time window; if the sliding time window is classified as a second sliding window class based on the classification, calculating the MI using a second method based on at least one CI included in TSCIs outside the sliding time window; and if the sliding time window is classified as a third sliding window class based on the classification, calculating the MI using a third method based on a first subset of CIs included in TSCIs within the sliding time window without using a second subset of CIs included in TSCIs within the sliding time window, wherein the first subset and the second subset are disjoint.

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

[0185] Item 5. The method of item 4, further comprising: calculating a link-wise test score (LTS) based on the overall values ​​for all TSs for CIs included in the TSCI within the sliding time window; and performing classification of the sliding time window based on the LTS.

[0186] Clause 6. The method of clause 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] Clause 7. The method of clause 6, wherein 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 is classified as a second CI class if the corresponding TS is greater than a fourth threshold.

[0188] Section 8. The method described in Section 7, wherein if all CIs included in the TSCIs within the 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 included in the TSCIs 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, and the TSCIs within the sliding time window include at least one first-class CI and at least one second-class CI.

[0189] Item 9. The method of item 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, each run including individual run lengths of consecutive CIs of the same individual CI class within the sliding time window, each run length being one of a number, amount, or count greater than zero; and classifying the sliding time window based on the runs and individual run lengths of the first class CIs and the second class CIs.

[0190] Item 10. The method of item 9, wherein if at least one selected run of the first class CI is selected, the sliding time window is classified as a third sliding window class, and if no selected run of the first class CI is selected, the sliding time window is classified as a second sliding window class.

[0191] Item 11. The method of item 10, further comprising: selecting at least one selected run of the first-class CI based on the run length of each run of the first-class CI and a TS associated with the run; and calculating an MI based on the at least one selected run of the first-class CI, wherein the at least one selected run is at least one run of the first-class CI that has the longest run length among all runs of the first-class CI within the sliding time window.

[0192] Clause 12. The method of clause 11, wherein if the individual run length of a leading run of consecutive first-class CIs, including the CI that is the first in the sliding time window, is greater than a first individual threshold, a first selected run is selected as the leading run; if the individual run length of a trailing run of consecutive first-class CIs, including the CI that is the last in the sliding time window, is greater than a second individual threshold, a second selected run is selected as the leading run; and if the individual run length of the leading run or the trailing run is greater than a third individual threshold, any selected run that is not the leading run or the trailing run is selected.

[0193] Item 13. The method of 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 at least one selection run is selected such that, for each selection run, all associated TSs are less than a threshold.

[0194] Clause 14. The method of clause 13, further comprising: constructing a first subset by including all of at least one selected run of a first-class CI included in a TSCI within the sliding time window when the sliding time window is classified as a third sliding window class; constructing a second subset by including all of a second-class CI included in a TSCI within the sliding time window; and calculating an MI as a combined value 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 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, or an adjacent sliding time window of a CI included in another TSCI, and the adjacent MI is calculated based on at least one CI included in a TSCI outside the sliding time window.

[0195] Clause 15. The method of clause 14, further comprising: calculating at least one interim MI for a sliding time window, each interim MI being calculated based on an individual selection run of first-class CIs included in the TSCI within the sliding time window; and calculating the MI as a total value of the at least one interim MI.

[0196] Item 16. The method of item 15, further comprising: determining the selected run as a leading run of CIs included in the TSCI within the sliding time window; combining the leading run of CIs with a trailing run of CIs included in the TSCI within a previous sliding time window to form a combined run of CIs included in the TSCI; and calculating a first interim MI based on the combined run of CIs.

[0197] Item 17. The method of item 15, further comprising: determining the selected run as a trailing run of CIs included in the TSCI within the sliding time window; combining the trailing run of CIs with a leading run of CIs included in the TSCI within a next sliding time window to form a combined run of CIs included in the TSCI; and calculating a second interim MI based on the combined run of CIs.

[0198] Clause 18. The method of clause 15, further comprising: for each interim MI calculated based on an individual selection run, calculating an individual calculated weight based on the run length of the individual selection run; and calculating the MI as a weighted total value of at least one interim MI, wherein each interim MI is weighted by an individual calculated weight.

[0199] Clause 19. The method of clause 15, further comprising calculating the MI as a combined value of at least one interim MI and at least one adjacent MI, each adjacent MI being associated with one of 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.

[0200] Clause 20. A wireless monitoring system comprising: a first wireless device configured to transmit a wireless signal through a wireless multipath channel of a venue, the wireless multipath channel being affected by motion of an object within the venue; a second wireless device configured to receive the wireless signal 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 the object; and a processor configured to: obtain TSCI (Time Series Channel Information) of the wireless multipath channel based on the received wireless signal; perform a sliding time window classification by analyzing CI (Channel Information) included in the TSCI within the sliding time window using the processor, a memory communicatively 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 sliding time window classification; and monitor the motion of the object based on the MI.

[0201] The following numbered sections provide examples for precision 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 the wireless sensing system through a wireless multipath channel of a venue, the wireless multipath channel being affected by motion of an object within the venue; receiving the wireless signal by a Type 2 heterogeneous wireless device of the system through the wireless multipath channel, the received wireless signal being different from the transmitted wireless signal due to the wireless multipath channel of the venue and periodic vital sign motion of the object; using a processor, memory, and instruction set, obtaining a time series of 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) for 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, some characteristic steps used to calculate MS / MI in wireless sensing are whether it is "normal," "abnormal," "moderately abnormal," or "severely abnormal."

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

[0205] Section A3. The method / device / system / software of the wireless monitoring system of section A1, comprising calculating the MI based on the autocorrelation function (ACF) of the TSCI.

[0206] Section A4. The method / device / system / software of the wireless monitoring system of section A3, including calculating MI based on feature points of the ACF.

[0207] In some embodiments, each sliding time window may be classified as one of a "moderately abnormal," "severely abnormal," or "normal" sliding window class. In some embodiments, the first sliding window class = "normal," the second sliding window class = "moderately abnormal," in some embodiments, the third sliding window class = "moderately abnormal," in some embodiments, the third sliding window class = "moderately abnormal," in some embodiments, if the (current) sliding time window is classified as the "moderately abnormal" sliding window class (third class), then in some embodiments, each CI within the current sliding time window may be classified into 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 each temporal neighborhood of the CI. Each CI may be classified based on its TS (e.g., "normal" if TS < threshold T1).

[0208] In some embodiments, the second subset of term A5 may be a collection of all abnormal CIs. The first subset may be a collection of all normal CIs (which may include one or more runs of normal CIs, each run including a run length of consecutive normal CIs with a run length ≥ 1). The first subset may also be a subset of the collection of normal CIs (e.g., only the normal CIs in the longest run or a selected run, or only the leading run of normal CIs, or only the trailing 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 a plurality of mutually prime runs of normal CIs. Some runs may be "satisfactory" (e.g., sufficient run length, or all TS<T2<T1). Some runs may not be satisfactory. A plurality of tentatively reduced MIs may be calculated, each based on an individual mutually prime run of (satisfactory) normal CIs. The reduced MI can be calculated as the combined value of the plurality of tentatively reduced MIs. The combined value may be a weighted quantity (e.g., sum, weighted average, weighted product, weighted median, etc.). The weight of each tentatively reduced MI can be adaptively calculated based on the run length of the normal CI run used to calculate the tentatively 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 the combined 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, tentatively 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, magnitude, square, phase), decomposition into a plurality of CI components (CIC) of the CI, decomposition into a plurality of time series CIC (TSCIC) 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, second sliding window class="severely abnormal": If the (current) sliding time window is severely abnormal, 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., a past MI, a recent past MI, a future MI, a next MI, or an MI calculated based on another TSCI) or a set thereof. In some embodiments, any past MI may be associated with a respective 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 respective 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, first sliding window class="normal": In some embodiments, if everything is normal, the MI for each sliding time window can be calculated based only on the CIs of TSCIs within the sliding time window, without using the CIs of TSCIs outside the sliding time window.

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

[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 a corresponding test score (TS). In some embodiments, for the second (item-wise) classification, each CI may be classified into a "normal" or "abnormal" CI class.

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

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

[0215] Paragraph A6c. The method / device / system / software of the wireless monitoring system of paragraph A6b, including computing a CTS for a component of a CI based on each component of a number of respective temporally adjacent CIs.

[0216] Section A6d. The method / device / system / software of the wireless monitoring system of section A6b, comprising calculating the TS for each CI as the aggregate value of a selected subset of N1 CTSs associated with the CI, the selected subset being selected based on criteria.

[0217] In some embodiments, TS at time t, i.e., TS(t), may be a function of temporally neighboring CI(t i), 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) may also be 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) can also be TS(t)=CI(t)-d*pastCI(t)-(1-d)*future(t), where 0 <d<1である。

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

[0219] In some embodiments, TS may include difference (i.e., subtraction or vector subtraction). In some embodiments, for a vector X (an N-tuple), F(X) may be a vector (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, magnitude squared, 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). . where for any vector X, F(X) is a vector of the same size as 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 each scalars, and CI(t) is the CI at time t.

[0221] In some embodiments, TS may include vector dissimilarities / similarities, dot products, cross products.

[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)]. . where 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_1 is a scalar, AxB is the dot 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, a link-wise TS (LTS) for a sliding time window may be computed based on all TSs within the sliding time window. Classification of the sliding time window may be based on the LTS.

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

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

[0226] Clause A11. The method / device / system / software of the wireless monitoring system of clause A10, comprising calculating the LTS as an aggregate value of a plurality of TSs, wherein the aggregate value comprises at least one of a sum, a weighted sum, an average, a weighted average, a geometric mean, a weighted geometric mean, a weighted geometric mean, a harmonic mean, a weighted harmonic mean, a weighted harmonic mean, an arithmetic mean, a weighted average, a trimmed mean, a median, a weighted median, a mode, a histogram, a statistic, a percentile, a maximum, a minimum, a variance, a variation, a divergence, a spread, a range, a deviation, or a characteristic value.

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

[0228] Clause A12. The method / device / system / software of the wireless monitoring system of clause A10, comprising: classifying the sliding time window as a first sliding window class if the LTS is greater than a first threshold; and classifying the sliding time window as a class comprising a second sliding window class and a third sliding window class if the LTS is less than a second threshold.

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

[0230] The method / device / software of the wireless monitoring system of paragraph A13.A1, wherein the LTS includes calculating at least one of the following: score, correlation, covariance, cross-correlation, norm, difference, absolute difference, squared difference, variance, standard deviation, spread, dispersion, 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, or change of the TSCI over a sliding time window; and performing classification of the sliding time window based on the 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 the CI has TS>T4, it 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. When T3<T4, and when T3<TS<T4, the CI can be classified as "moderately abnormal". Alternatively, if the CI has 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 "moderately abnormal" CIs or "mildly abnormal" CIs. In some embodiments, one or more reduced MIs (or preliminarily reduced MIs) can be calculated based on the first subset. Basically, "normal" CIs may be more reliable than "mildly abnormal" CIs, which in turn may 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, when an MI includes a weighted quantity (e.g., a weighted average, weighted sum, weighted geometric mean, etc.) of multiple reduced MIs, a more reliable reduced MI (e.g., the first reduced MI or the third reduced MI) may have a greater weight than an unreliable reduced MI (e.g., the second reduced MI or the fourth reduced MI). The weight for a 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 computing the reduced MI. In some embodiments, when an MI includes a weighted quantity of reduced MIs and several “neighboring MIs” from a “neighboring sliding time window” and / or “neighboring TSCIs,” the weight of a 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 computing the reduced MI.

[0232] Section A14. The method / device / system / software of the wireless monitoring system of section A6, including, in a second (item-wise) classification, classifying each CI as a first CI class if the respective TS is less than a third threshold, and as a second CI class if the respective TS is greater than a fourth threshold.

[0233] In some embodiments, the first CI class = "normal" and the second CI class = "abnormal". In some embodiments, not all "normal" CIs are used in calculating the MI. The first subset may, for example, include only some (e.g., all or not all) of the "normal" CIs. The MI may be calculated based on the longest run of "normal" CIs. In other words, the second, third, fourth, etc. longest run of "normal" CIs may or may not be used in calculating the MI. In some embodiments, the first subset may include all of the "normal" CIs. In some embodiments, the first sliding window class = "normal," in some embodiments, the second sliding window class = "severely abnormal," and in some embodiments, the third sliding window class = "moderately abnormal." 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 percentage.

[0234] Section A15. The method / device / system / software of the wireless monitoring system of section A14, including classifying the sliding time window as a first sliding window class if all CIs of the TSCIs within the sliding time window are first class CIs, and classifying the sliding time window as a second sliding window class if all CIs of the TSCIs within the sliding time window are classified as a second CI class, wherein any first class CI is a CI classified as the first CI class and any second class CI is a CI classified as the second CI class.

[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), then there is at least one run of the first-class CI and at least one run of the second-class CI, each run having a run length of consecutive CIs of the same class. In some embodiments, the classification of the sliding time window may be based on the run length and multiple TSs associated with each run (particularly runs of first-class CIs).

[0236] Section A16. The method / device / system / software of the wireless monitoring system of section A15, wherein there is at least one first class CI and at least one second class CI of TSCI within a sliding time window, and includes identifying at least one run of the first class CI and at least one run of the second class CI within the sliding time window, each run having a respective run length of consecutive CIs of the same class within the sliding time window, any run length being greater than zero, by one of number, amount, or count; and classifying the sliding time window based on the runs of the first class CI and the second class CI and their respective run lengths.

[0237] In some embodiments, several "selected" runs of "normal" CIs may be selected (or chosen or identified). The "selected" runs must meet certain criteria / requirements / conditions. If at least one selected run can be found (and used to calculate 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. The method / device / system / software of the wireless monitoring system of section A16, including classifying the sliding time window as a third sliding window class if at least one selected run of the first class CI is selected, and classifying the sliding time window as a second sliding window class if no selected runs of the first class CI are selected.

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

[0240] Item A18. The method / device / system / software of the wireless monitoring system of item A17, including selecting at least one selected run of the first class CI based on a run length of each run of the first class CI and a plurality of TSs associated with the run, and calculating an MI based on the at least one selected run of the first class CI.

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

[0242] Item A19. The method / device / system / software of the wireless monitoring system of item A18, wherein the particular selected run is the run of the first class CI that has the longest run length among all runs of the first class CI within the sliding time window.

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

[0244] Item A20. The method / device / system / software of the wireless monitoring system of Item A19, 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 the sliding time window.

[0245] In some embodiments, one "selected" run may be a leading run with sufficient run length. A run with a very short run length may not be sufficient to calculate MI with good accuracy. In some embodiments, the run length requirements for the leading or trailing run may be less stringent than for other runs. In other words, the run length threshold for the leading or trailing run (e.g., in term A21 or term A22) may be smaller (i.e., less stringent) than the other run length thresholds (e.g., in term A23).

[0246] Item A21. The method / device / system / software of the wireless monitoring system of Item A18, wherein a particular selected run is selected as the leading run of consecutive first-class CIs that includes the very first CI within the sliding time window if the run length of each of the leading runs is greater than a respective threshold.

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

[0248] Item A22. The method / device / system / software of the wireless monitoring system of Item A18, wherein a particular selected run is selected as the tail run of consecutive first class CIs including the most recent CI within the sliding time window if the run length of each of the tail runs is greater than a respective threshold.

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

[0250] Item A23. The method / device / system / software of the wireless monitoring system of item A18, wherein any selected run that is not a leading run or a trailing run is selected if the run length of each of the runs is greater than a respective threshold.

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

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

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

[0254] Paragraph A25. The method / device / system / software of the wireless monitoring system of paragraph A24, wherein at least one selection run is selected such that the count of the at least one selection run is a predetermined number.

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

[0256] Item A26. The method / device / system / software of the wireless monitoring system of item A18, wherein at least one selection run is selected such that for each selection run, all associated TSs satisfy the condition.

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

[0258] Paragraph A27. The method / device / system / software of the wireless monitoring system of paragraph 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. A provisional 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 provisional reduced MS / MIs.

[0260] Item A28. The method / device / system / software of the wireless monitoring system of item A18, comprising: calculating at least one interim MI for a sliding time window, each interim MI being calculated based on a respective selected run of first class CIs of TSCI within the sliding time window; and calculating the MI as a combined value of the at least one interim MI.

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

[0262] Paragraph A29. The method / device / system / software of the wireless monitoring system of paragraph A28, comprising: computing a specific interim MI based on a selected run that is a leading run of CIs of the TSCI within a sliding time window; combining a trailing run of CIs of the TSCI with a leading run of CIs within a previous sliding time window to form a composite run of CIs of the TSCI; and computing a specific interim MI based on the composite run of CIs.

[0263] In some embodiments, the trailing run of the (current) sliding time window may be combined with the leading run of CIs in the next sliding time window to form a combined run of CIs. In some embodiments, a provisional reduced MS / MI may be calculated based on the combined runs of CIs.

[0264] Item A30. The method / device / system / software of the wireless monitoring system of item A28, comprising: calculating a specific interim MI based on a selected run that is a trailing run of CIs of the TSCI in a sliding time window; combining the trailing run of CIs with a leading run of CIs of the TSCI in a next sliding time window to form a combined run of CIs of the TSCI; and calculating a specific interim MI based on the combined run of CIs.

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

[0266] Item A31. The method / device / system / software of the wireless monitoring system of item A28, comprising: for each interim MI calculated based on a respective selection run, calculating a respective calculated weight based on the run length of the respective selection run; and calculating the MI as a weighted total value of the at least one interim MI, with each interim MI weighted by the respective calculated weight.

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

[0268] Section A32. The method / device / system / software of the wireless monitoring system of section A28, comprising calculating the MI as a combined value of at least one interim MI and at least one neighboring MI, each neighboring MI being associated with one of a past neighboring sliding time window of a CI of the TSCI, a future neighboring sliding time window of a CI of the TSCI, or a neighboring sliding time window of a 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 computed using a first subset of CIs of the TSCIs within the sliding time window without using a second subset. The first subset may include all "normal" (first class) CIs of the TSCIs within the sliding time window. The second subset may include all "abnormal" (second class) CIs of the TSCIs within the sliding time window.

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

[0271] In some embodiments, the first subset may include only at least one select run of "normal" (first class) CIs of the TSCI within the sliding time window. In some embodiments, the second subset may include all "abnormal" (second class) CIs of the TSCI within the sliding time window.

[0272] Section A34. The method / device / system / software of the wireless monitoring system of section A18, comprising, when the sliding time window is classified as a third sliding window class, constructing a first subset by including all of at least one selected run of first class CIs of the TSCI within the sliding time window, and constructing a second subset by including all second class CIs of the TSCI within the sliding time window.

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

[0274] Section A35. The method / device / system / software of the wireless monitoring system of section A17, when the sliding time window is classified as a second sliding window class, includes calculating the MI as a set of at least one adjacent MI, each adjacent MI being associated with one of a past adjacent sliding time window of a CI of the TSCI, a future adjacent sliding time window of a CI of the 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 the TSCI outside the sliding time window.

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

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

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

[0278] In some embodiments, there are more than one radio signal. More than one CI can be obtained. A composite CI can be calculated based on the two or more CIs.

[0279] Clause B2. The system of clause B1, further comprising: at least one receiver configured to receive respective wireless signals transmitted from respective transmitters through respective wireless channels; determining at least one CI based on the at least one received wireless signal; comparing one of the at least one CI with a respective previous CI obtained based on a respective previously received wireless signal; calculating a similarity between the CI and each previous CI; determining a composite CI based on the at least one wireless signal and the at least one CI; and comparing the composite CI with a previous composite CI; wherein the previous composite CI is determined based on the at least one previously received wireless signal and the at least one previous CI, and wherein the at least one The previous CI is determined based on at least one previously received radio signal; and calculating a second similarity between the composite CI and the previous composite CI, wherein the second similarity is calculated based 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, a CI-based autocorrelation function, a second autocorrelation function based on the composite CIs, a CI-based cosine function, 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 may be obtained.

[0281] Clause B3. The system of clause B1, wherein the wireless signals include a sequence of periodically transmitted probe signals, and the sequence of CIs is determined based on the received wireless signals, one CI for each probe signal.

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

[0283] Section B4. The system of section B1, wherein the one or more data processors are further configured to compare the CI with a plurality of other previous CIs, determine the CI to be an outlier CI if the similarity between the CI and the plurality of other previous CIs is less than a threshold, and discard the outlier CI.

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

[0285] Clause B5. The system of clause B1, wherein the one or more data processors are further configured to replace the CI with another CI obtained based on another received radio signal transmitted from the respective transmitters to the respective receivers, and calculate a similarity based on the another CI instead of the CI, wherein the similarity is between the another CI and the previous CI.

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

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

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

[0289] Section B7. The system of section B6, wherein the one or more data processors are further configured to replace the 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 another CI instead of the particular CI.

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

[0291] Section B8. The system of section B1, wherein the one or more data processors are further configured to compare the CI with a plurality of reference CIs in the database, determine the CI to be new when the similarity between the CI and the plurality of reference CIs is less than a threshold, and update the database by replacing the particular reference CI in the database with the CI.

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

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

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

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

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

[0297] Clause B11. The system of clause B2, wherein each respective wireless signal comprises a respective quantity of subcarriers, a respective frequency band, a respective bandwidth, each CI comprising two or more CI components, each CI component associated with a subcarrier.

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

[0299] Clause B12. The system of clause B2, wherein each respective wireless signal is communicated between a respective transmitter and a respective receiver in accordance with a wireless signaling protocol or standard, the wireless signaling protocol or standard comprising 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, a wireless data transmission standard or protocol, a wireless data communication standard or protocol, a wireless network standard or protocol, a cellular network standard or protocol, a wireless local area network (WLAN) standard, OFDM, OFDMA, or CDMA.

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

[0301] Clause B13. The system of clause B12, wherein each wireless signal comprises one or more frames having a format that conforms to a wireless signaling standard or protocol.

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

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

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

[0305] Clause B15. The system of clause B13, wherein the CI is determined based on a long preamble of one of the frames.

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

[0307] Clause B16. The system of clause B14, wherein the CI is determined based on parameters and control signals passed between the transmitter and receiver according to a wireless signaling standard or protocol.

[0308] Clause B17. The system of clause B1, wherein the one or more data processors are further configured to determine CI based on at least one of discretization, derivation, "exact-enough" determination, and desired accuracy of system performance.

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

[0310] Clause B18. The system of clause B103, wherein the CI is determined from the received wireless signal based on a preamble of the wireless signal.

[0311] Clause B19. The system of clause B103, wherein the CI is processed to mitigate variations in the CI due to at least one of imperfect transmission by each transmitter, imperfect reception by each receiver, imperfect timing, imperfect frequency synchronization, channel frequency offset (CFO), or sampling frequency offset (SFO).

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

[0313] Paragraph B20. The system of paragraph B103, wherein the CI determined based on each received wireless signal is obtained and processed locally at a device that receives each received wireless signal.

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

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

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

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

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

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

[0320] Paragraph B24. The system of paragraph B1, further including the same device comprising one of the transmitters and one of the receivers.

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

[0322] Clause B25. The system of clause B1, wherein the one or more data processors are further configured to enable a user to provide user-provided information and to compute at least one of the computed items based on the user-provided information and the similarity score.

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

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

[0325] Paragraph B28. The system of paragraph B25, wherein the user-provided information includes a location or arrangement identifier.

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

[0327] Clause B29. The system of clause B1, wherein one or more data processors are 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 wireless channel is the same wireless channel associated with the same wireless network.

[0328] In some embodiments, multicast, broadcast, unicast, tracker bots may be carried by and travel with each mobile object.

[0329] Clause B30. The system of clause B29, wherein the one or more data processors are further configured such that the 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 the at least one receiver is a respective terminal device to be carried by a respective user to identify the location of the respective user.

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

[0331] Clause B31. The system of clause B29, wherein the one or more data processors are further configured such that the at least one receiver is the same receiver and each of the at least one transmitter is a respective terminal device to be carried by a respective user to identify the location of the respective user.

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

[0333] In some embodiments, a confidence level may be calculated (eg, to qualify the Tx / Rx / CI / system).

[0334] Clause B33. The system of clause B1, wherein the one or more data processors are further configured to calculate a confidence associated with at least one of the calculated items.

[0335] In some embodiments, confidence may be computed in the form of "accuracy" (eg, for a sensing task).

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

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

[0338] Paragraph B35. The system of paragraph B1, wherein the one or more data processors are further configured to calculate the similarity based on the amplitude of the CI.

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

[0340] Clause B36. The system of clause B1, wherein the one or more data processors are further configured to compute the similarity based on the topology of the CIs.

[0341] In some embodiments, it can be determined that the amounts of CIs that combine to form composite CIs.

[0342] Clause B37. The system of clause B2, wherein the one or more data processors are further configured to determine the number of individual CIs to be included in the composite channel response to achieve a desired accuracy.

[0343] In some embodiments, "preferred" (eg, default) amounts of CIs are combined to form a composite CI.

[0344] Clause B38. The system of clause B2, wherein several individual CIs are preferably included in the composite channel response.

[0345] Paragraph B38b. The system of paragraph B2, wherein several individual CIs are associated with each transmitter and each receiver, is preferred.

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

[0347] Paragraph B38d. The system of paragraph B38b, wherein the number of individual CIs and respective receivers is preferred.

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

[0349] Clause B39. The system of clause B1, wherein each CI is determined for a pairing of a transmit antenna at the transmitter and a receive antenna at the receiver.

[0350] In some embodiments, feature values ​​(eg, magnitude, phase, spatio-temporal information (STI), motion information (MI)) may be computed based on the CI.

[0351] Clause B40. The system of clause B1, wherein the one or more data processors are further configured to determine a feature value based on a mathematical function performed on the CI and the previous CI, and to calculate at least one of the calculated items based on the feature value.

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

[0353] Paragraph B41. The system of paragraph B40, wherein the one or more data processors are further configured to determine feature values ​​for the CIs based on a dimensionality reduction of the CIs.

[0354] In some embodiments, a feature value may be calculated based on the similarity score.

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

[0356] In some embodiments, there may be multiple feature values, which may be normalized.

[0357] Clause B43. The system of clause B1, wherein the one or more data processors are further configured to determine a set of feature values ​​based on a mathematical function performed on the CI and a set of previous 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, the Tx and Rx are housed in their own devices.

[0359] Paragraph B44. The system of paragraph B1, wherein the transmitter is contained within the first device and the receiver is contained within the second device.

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

[0361] Paragraph B45. The system of paragraph 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 (eg, sensing receiver) can be an AP or a non-AP.

[0363] Paragraph B46. The system of paragraph 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, the Rx (eg, sensing receiver) may be unassociated (eg, in 802.11bf).

[0365] Paragraph B47. The system of paragraph B1, wherein the receiver is not associated with a network router and a wireless network access point.

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

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

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

[0369] Clause B49. The system of clause B48, wherein the receiver is part of a first device and one of the one or more data processors is part of a second device, and the 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 in Item B49 that is reported in a report generated by the system.

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

[0373] Paragraph B51. The system of paragraph B1, wherein 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) triggered by a trigger frame (TF) in trigger-based (TB) sensing in 802.11bf or an NDP announcement frame (NDPA).

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

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

[0377] Paragraph B54. The system of paragraph B53, wherein the user interface is configured to display a map and allow a user to indicate a location associated with the CI on the map.

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

[0379] Paragraph B56. The system of paragraph B1, wherein the one or more data processors are configured to obtain the prediction and compute at least one of the computed items based on the CI and the prediction.

[0380] Paragraph B57. The system of paragraph B56, wherein the one or more data processors are configured to compare the CI to a prediction and compute at least one of the computed items based on the comparison.

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

[0382] Paragraph B59. The system of paragraph B57, wherein the prediction includes coarse position data received by the one or more data processors.

[0383] Clause B60. The system of clause B57, wherein the prediction comprises coarse position data calculated by one or more data processors based on a mapping, the receivers are pre-mapped to specific locations based on the mapping, and a more precise location of the transmitter is calculated based on the mapping.

[0384] Clause B61. The system of clause B60, wherein the 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 a fine position of the transmitter based on the coarse position using a time-reversal positioning system.

[0385] Clause B62. The system of clause B1, comprising: a storage device that stores a three-dimensional rendering of the venue; and a 3D electromagnetic simulator configured to generate a plurality of channel paths based on data derived from the three-dimensional rendering of the venue.

[0386] Clause B63. The system of clause B1, including a storage device that stores a three-dimensional model of the venue constructed using at least one of a photograph, an image, a video, a ranging technique such as laser ranging, optical coherence tomography, or echo location, and 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] Clause B64. A system comprising: a receiver configured to receive wireless signals transmitted from a transmitter over a wireless channel; and one or more data processors configured to determine channel information (CI) based on the received wireless signals, the CI including at least one of a channel response, a channel impulse response (CIR), a channel frequency response (CFR), a channel state information (CSI), a received signal strength (RSS), or an RSS indicator (RSSI); running a 3D electromagnetic simulator to predict multiple channel paths between the transmitter and the receiver; determining predicted CIs based on the predicted multiple channel paths between the transmitter and the receiver; (i) training the 3D electromagnetic simulator based on a comparison of the CIs to the predicted CIs to generate a trained 3D electromagnetic simulator, or (ii) calibrating the 3D electromagnetic simulator based on a comparison of the CIs to the predicted CIs to generate a calibrated 3D electromagnetic simulator; and using the trained or calibrated 3D electromagnetic simulator to generate a database of location-specific estimated CIs within a venue and establish a mapping between locations within the venue and corresponding CIs.

[0388] Paragraph B65. The system of paragraph B64, including a storage device that stores a three-dimensional rendering of the venue, wherein the 3D electromagnetic simulator is configured to predict a plurality of channel paths based on data derived from the three-dimensional rendering of the venue.

[0389] Clause B66. The system of clause B64, including a storage device that stores a three-dimensional model of the venue constructed using at least one of a photograph, an image, a video, a ranging technique such as laser ranging, optical coherence tomography, or echo location, and 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] Clause B67. The system of clause B64, further comprising: a storage device that stores parameters including reflection coefficients of different objects in the venue, the parameters having a plurality of values ​​to account for one or more environmental factors including at least one of temperature, humidity, or smog; and the 3D electromagnetic simulator configured to predict a plurality of channel paths based on data derived from the parameters.

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

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

[0393] The motion of objects within a venue can be monitored based on TSCI obtained based on wireless signals transmitted from Type 1 devices to Type 2 devices. 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 possible different choices for Type 1 devices. There can also be many possible different choices for Type 2 devices.

[0394] Different selection of Type 1 device (or Type 2 device or both) may cause device-dependent behavior of CI / TSCI / MS / MI / STI / analysis (e.g., shifts / mappings / distortions / systematic variations that may be linear / affine / nonlinear / monotonic / time-invariant / time-varying / device-dependent / stochastic) (e.g., large motion may cause the first MS / MI / STI to be about 0.9 and the second MS to be about 0.75, or NIL / no motion may cause the first MS / MI / STI to be about 0.1 and the second MS to be about 0.3). Device-dependence of behavior is undesirable. In some embodiments, device-dependent compensation is disclosed to "undo" or compensate for device-dependent behavior, making it independent and similar to some baseline / desired / standard behavior. The undesirable behavior / mapping may resemble / include / modeled as any of the following: a shift, a linear / affine / piecewise linear / non-linear mapping, a monotonic mapping, a monotonically increasing (or non-decreasing) mapping, a monotonically decreasing (or non-increasing) mapping, etc.

[0395] For example, a selection of Type 1 or Type 2 devices may include smartphone speakers, thermostats, TVs, sound bars, DVD players, streaming devices, lamps, light bulbs, plugs, doorbells, cameras, routers, etc. A selection of Type 1 or Type 2 devices 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), or specific models of specific brands (e.g., Echo Even for IEEE 802.11a / b / c / d), different devices may still differ (e.g., due to variations / defects / wear / deterioration / depreciation in circuit components / parts / amplifiers / antennas / chips / wiring / manufacturing / assembly / production / firmware / software). In particular, there may be different antenna counts and / or antenna types (e.g., with different gains, types, sizes, directional characteristics, radiation patterns). All of these may cause undesirable behavior of CI / TSCI / MS / MI / STI / analysis. Also, different bands (e.g., 2.4GHz, 5GHz, 6GHz) or different bandwidths (e.g., 20 / 40 / 80 / 100 / 120 / 140 / 160 / 180 / 200 / 220 / 240 / 260 / 280 / 290 / 300 / 320 / 340 / 360 / 400 / 420 / 440 / 460 / 480 / 500 / 520 / 540 / 560 / 600 / 700 / 820 / 900 / 1024 / 1124 / 1280 / 1400 / 1600 / 1800 / 1800 / 1800 / 1800 / 1900 / 2100 / 2200 / 2300 / 2400 / 2500 / 3600 / 480 / 560 / 600 / 700 / 820 / 1900 / 2400 / 3600 / 400 / 560 / 600 / 700 / 820 / 1900 / 25 ... Undesirable behavior can occur due to different frequencies (160 / 320 / 640 / 1280 MHz), different amounts of antennas (e.g., an echo has three antennas, but an echo dot has two antennas) or different antenna choices (e.g., dipole, microstrip, slot antenna, waveguide, spiral antenna, omnidirectional antenna, directional antenna, etc.). Undesirable behavior can result from different placement of devices / device antennas, different transmit power of radio signals, different amplifier sets, different digital / analog gains, different placement of objects, or environmental factors such as mechanical / electrical / electromagnetic interference.

[0396] In some embodiments, compensation may be applied to compensate for undesired behavior. In some embodiments, MS / MI / STI / analysis may be compensated based on the compensation to obtain / give / provide a compensated MS / MI / STI / analysis. One or more separate / respective compensations may be applied. After compensation, all compensated MS / MI / STI / analysis for any selection of Type 1 / Type 2 device may have a similar "target behavior" and take similar values ​​in response to the same motion of the object, regardless of the selection of Type 1 / Type 2 device. The similar target behavior may be a "reference" (or "representative" or "generic") behavior associated with a reference / representative / generic Type 1 / Type 2 device and 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, the user-defined / designed / specified / preferred behavior may be programmable / adjustable. It may be specified by the user in the system's configuration. It may be specified once and remain unchanged, or it may change over time. In particular, it may change adaptively based on a strategy / timetable / user intervention / user real-time adjustment, or perhaps in response to events / situations as specified in the system's 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, with a test setup and procedure, for a test task) to compute / obtain / train its respective compensation or respective compensation sequence. Any compensation may be pre-processing / processing / post-processing applied before / after computation of the MS / MI / STI / analysis. In the case of CI compensation, compensation may be applied to a CI, a feature of a CI, a group / sliding window of CIs, a TSCI, a number of TSCIs, a number of TSCIs associated with a particular antenna of a Type 1 device, or a number of TSCIs associated with a particular antenna of a Type 2 device. For intermediate value compensation, compensation may be applied to the intermediate value (or a number thereof), which may be computed based on the CI / CI feature / sliding window of the CI / TSCI, the compensated CI / CI feature / sliding window of the CI / TSCI, the MS / MI / STI, and / or the compensated MS / MI / STI. For MS / MI / STI compensation, compensation may be applied to the MS / MI / STI / analysis (or a number of them), which may be calculated based on the CI / CI features / sliding window of the CI / TSCI, the compensated CI / CI features / sliding window of the CI / TSCI, the intermediate value, and / or the compensated intermediate value. For analytical compensation, compensation may be applied to the analysis, which may be calculated based on the TSCI, the compensated CI / CI features / sliding window of the CI / TSCI, the MS / MI / STI, the compensated MS / MI / STI, the intermediate value, and the compensated intermediate value. In some embodiments, each compensation sequence may include any of the compensations described above. The compensation may be adaptive, time-varying, or time-invariant. The compensation may be different for different frequency bands (e.g., 2.4 GHz, 5 GHz, 6 GHz, 24 GHz, 60 GHz, 77 GHz) or bandwidths (e.g., 20 MHz, 40 MHz, 80 MHz, 160 MHz, 320 MHz, 640 MHz, 1280 MHz) of the radio signal.

[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, monotonic increasing / decreasing mapping, monotonic non-decreasing / non-increasing mapping, piecewise linear mapping, linear mapping, DE adjustment, affine mapping, composite mapping, mapping of mappings. Mappings may be time-varying (e.g., adaptively modified) or time-invariant. Mappings may be spatially varying or spatially independent. Mappings may / may not vary across frequency bands / bandwidths. Mappings may be the same or different for different sensing tasks (e.g., motion detection, respiration detection / estimation, fall detection, presence detection, gait / gesture detection / estimation, etc.).

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

[0401] In some embodiments, compensation may be computed based on training CI / TSCI / MI / MS / STI / analysis / median behavior. The behavior may include any of the following: MS / CI / STI / analysis domain behavior, time / frequency transform / analysis domain behavior, magnitude / phase / component behavior, histogram, constrained histogram, conditional histogram, histogram between two timestamps, two time / frequency / transform / projection domain stamps (i.e., range within the time / transform / projection domain), histogram between local maxima and minima, histogram between local maxima and minima above a threshold, local maxima and minima after filtering / processing (e.g., low-pass / high-pass / median filtering), statistics, trimmed statistics, first / second-order statistics, weighted average, arithmetic mean, geometric mean, harmonic mean, median, mode, maximum, minimum, zero crossing, percentile, range, variance, magnitude, phase, etc. The statistical behavior may be compared to a target behavior (e.g., target statistical behavior, target time / frequency / transform / projection domain behavior, target CI / TSCI / MS / MI / STI / analysis behavior). The target behavior may be associated with a target MS. The target behavior may be the behavior of the target MS. The target behavior may be user-preferred / predefined / user-defined / user-designed / user-specified / user-selected / user-chosen behavior (e.g., specified / provided / designed / defined / input / selected / chosen by a user using some user interface of 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 / phase), and one(or more) reference sensing tasks (e.g., sensing tasks) are performed by transmitting one(or more) time series of reference wireless (sounding) signals from the Type 1 devices to the Type 2 devices through one(or more) reference wireless channels in one(or more) reference venues, where the reference wireless channels are affected by the reference motion of the reference object. One or more "reference" TSCIs may be obtained / extracted from the received time series of reference wireless sounding signals.A reference MS / MI / STI may be calculated based on the reference TSCI. A reference analysis may be calculated based on the reference MS / MI / STI. The resulting behavior (or overall value / average value / characteristic / clustered behavior) may be used as the target behavior. In some embodiments, the time series of radio sounding signals may use a 40 MHz band. The time series of reference radio sounding signals may cover / scan / use some or all possible 40 MHz bands.

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

[0403] In some embodiments, several "control points" may be determined for the MS univariate histogram and the target univariate histogram. The control points may include the mean (the mean value of MS, or the mean value of the target MS), mode, median (the median value of MS), percentile points (percentile values ​​of MS), 0%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or any other percentile point. Each pair of corresponding control points in the MS histogram and the target histogram may together form / be 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, (10% point of MS, 10% point 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 a monotonically non-decreasing (or monotonically increasing) line. The compensation may be used to connect the control points so that a monotonic (additive) mapping y=f(x) is obtained. The compensation may be a mapping y=f(x) obtained by connecting the control points using some kind of monotonic line (e.g., straight line, quadratic function, cubic function, polynomial function, exponential function, logarithmic function, convex function, concave function, etc.). In this way, the control points are inside / 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) may be applied to obtain / compute the mapping for compensation. The line fitting may be applied piecewise. The resulting mapping may be monotonically increasing or non-increasing. In this way, there may be one or more control points that are not inside / on the mapping.

[0404] In some embodiments, there may be two control points, the 0% point and the 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, other monotonic line, or a regression line. Or, the 1% point and the 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 the N - 1 "control points" may be obtained based on the N - 1 thresholds and the corresponding percentile values. Two control points of the 0% point and the 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 F1 is followed by the inverse function F2^{-1} 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 calculations. Thus, an alternative embodiment is to approximate the function F2^{-1}[F1(x)] by 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 the 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-variate), let the MS vector be an N-tuple X = [X_1, X_2, .., X_N], the target MS vector be an N-tuple T = [T_1, T_2, .., T_N], and the mapped / compensated MS vector be 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) may be obtained. The compensation may 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. And 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 independently manipulated / processed to give Y_1 and Y_2. In some embodiments, y_1 is computed as y_1=F1(x_1)=F2'^{-1}[F1'(x_1)], and then y_2 is computed 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 computed as y_2=f2(x_2)=F2''^{-1}[F1''(x_2)], and then y_1 is computed / 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 first be computed 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 computed based on x_{i2}. Then, given T_{i1}=y_{i1} and T_{i2}=y_{i2}, the third component y_{i3} may be computed based on x_{i3}, and so on. Then, given T_{i1}=y_{i1}, T_{i2}=y_{i2},...,T_{i(N-1)}=y_{i(N-1)}, the Nth component y_{iN} may be computed based on x_{iN}.

[0410] In some embodiments, the target behavior (e.g., histogram of MS) ​​may not be fully specified by the user. Instead, the user may specify that the number of classes (e.g., of MS) ​​is N, and 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 a 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 a 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, where the control points are (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 such that the mapping y=f(x) connects 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, feed-forward networks, attention-based networks, Transformer networks, or any combination)). Supervised, unsupervised, or semi-supervised learning may be applied to learn the compensation / mapping / functionality. The input of the compensation may be any of the following (or any feature / magnitude / phase / function / statistics / behavior / histogram): 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 corresponding feature / magnitude / phase / function / statistics / behavior / histogram). The training target (i.e., training criterion) may be yet another CI, IV, MI / MS / STI, analysis, TSCI, TSMI, or TSA (or corresponding feature / magnitude / phase / function / statistic / behavior / histogram), which may be obtained in several pre-training / pre-calibration stages / phases. An objective / cost / loss function (e.g., similarity / norm / score / dissimilarity / distance / variance / rate / variance / variance / variance / TRRS / correlation / covariance / autocorrelation / cross-correlation / inner product / mean squared / absolute error) between the training target (e.g., target behavior) and the output (e.g., output behavior) may be used to train the compensation / mapping / function. The objective / cost / loss function may be optimized / minimized / maximized under / without specific constraints to find the best / optimized compensation / mapping / functionality. The objective / cost / loss function may be parameterized, and the optimization / maximization / minimization may be with respect to a set of parameters.

[0412] In some embodiments, for the search, compensation may be computed by performing a search (e.g., exhaustive search, fast search, shrinkage search). Several candidate mappings may be determined and tentatively applied to obtain / compute the respective compensated behaviors. A distance or similarity / dissimilarity score between the target behavior and each compensated behavior may be computed (e.g., a "behavior distance," e.g., histogram distance, vector norm, city block distance, Euclidean distance, intersection distance, Bhattacharyya distance (B-distance), Kullback-Leibler distance (KL-distance)). The candidate mapping that provides the best match (e.g., smallest distance or largest similarity score) may be selected as compensation. A multi-stage search may be applied. For example, several candidate first mappings (e.g., mappings applied to CIs) may be determined. Then, for each candidate first mapping, several candidate second mappings (e.g., mappings applied to MSs or analytical values) may be determined. Both the candidate first mapping and the candidate second mapping may be tentatively applied to obtain / compute the respective compensated behaviors. A distance / similarity score between the target behavior and each compensated behavior may be calculated so that the candidate first mapping and the candidate second mapping that together provide the best match (e.g., smallest distance or largest similarity score) may be selected as the respective compensation. In some embodiments, inference and search may be applied together. For example, several candidate first mappings (e.g., to CIs) may be determined and tentatively applied. Then, for each candidate first mapping, a second mapping (e.g., to MSs) may be estimated. Both the candidate first mappings and each estimated second mapping may be tentatively applied to obtain / calculate the respective compensated behavior. A distance / similarity score between the target behavior and each compensated behavior may be calculated so that the candidate first mapping and each estimated second mapping that together provide the best match (e.g., smallest distance or largest similarity score) may be selected as the respective compensation.

[0413] In some embodiments, two or more MS / MI / STI / analyses may be computed for the same task or for different tasks (e.g., one for motion detection, a second for respiration detection, a third for fall detection, etc.) with different compensations for different MS / MI / STI / analytics. Different compensations may be applied to different MS / MI / STI / analytics. In some embodiments, MI may be an MS / MI / STI computed based on TSCI (or compensated TSCI).

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

[0415] Item C1. A time / device / system / software of a compensated wireless monitoring system, comprising: transmitting a wireless signal from a type 1 heterogeneous wireless device of the wireless sensing system through a wireless multipath channel of a venue, the wireless multipath channel being affected by motion of an object in the venue; and receiving a wireless signal from a type 2 heterogeneous wireless device of the wireless sensing system through the wireless multipath channel, the received signal being different from the transmitted wireless signal due to the wireless multipath channel of the venue and the motion of the object. using a processor, a memory, and a set of instructions to obtain a time series of channel information (CI) of a wireless multipath channel based on a received wireless signal; computing a time series of motion information (MI) based on the time series of CI (TSCI); computing a time series of analysis value based on the time series of MI (TSMI); computing a compensated time series analysis value by applying compensation to the computation of the time series analysis value (TSA), where the compensation includes monotonic mapping; and monitoring motion based on the compensated time series analysis value.

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

[0417] Section C3. The method / device / system / software of the compensated wireless monitoring system in Section C1 (or 2), wherein the compensation further includes at least one of a first compensation tailor made for a pair of a generic Type 1 device and a generic Type 2 device, 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 a Type 1 device and a Type 2 device.

[0418] Section C4. The method / device / system / software of the compensated wireless monitoring system in section C3 (or 1 or 2), further comprising: the monotonic mapping including at least one of a first monotonic mapping tailor made for a pair of a general Type 1 device and a general Type 2 device; 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 a Type 1 device and a Type 2 device.

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

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

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

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

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

[0424] Clause C7. The method / device / system / software of a compensated wireless monitoring system of clause C1 (or 2 or 3 or 4), wherein the compensation or monotonic mapping is applied to at least one of an intermediate value (IV) calculated based on TSMI, a number of IVs, a number of IVs in a sliding time window, an IV feature, an IV magnitude, an IV phase, an IV component, a number of IV components, an IV component magnitude, an IV component magnitude, an IV component phase, or an IV component phase.

[0425] In some embodiments, a monotonic mapping may be applied to the MI.

[0426] Clause C8. The method / device / system / software of the compensated wireless monitoring system of clause C1 (or 2 or 3 or 4), wherein the compensation or monotonic mapping is applied to at least one of MI, a number of MI, a number of MI in a sliding time window, a number of MI in a TSMI, a feature of the TSMI, a magnitude of the MI, a magnitude of the MI, a phase of the MI, a number of components of the MI, a magnitude of a component of the MI, a magnitude of a component of the MI, or a phase of a component of the MI, and further includes monitoring motion based on all compensated MI, compensated features of the MI, and compensated components of the MI.

[0427] In some embodiments, a monotonic mapping may be applied to the TSA.

[0428] Clause C9. The method / device / system / software of a compensated wireless monitoring system of clause C1 (or 2 or 3 or 4), wherein the compensation or monotonic mapping is applied to at least one of the following: analysis, several analyses, several analyses in a sliding time window, several analyses in a TSA, TSA, analysis features, analysis magnitude, analysis phase, analysis component, number of analysis components, analysis component magnitude, or analysis component phase.

[0429] In some embodiments, the monotonic mapping may be time-varying.

[0430] Clause C10. The method / device / system / software of the compensated wireless monitoring system of clause C1, further comprising changing the compensation or monotonic mapping based on at least one of a change in target behavior, a change in wireless signal, a change in bandwidth of the wireless signal, a change in bandwidth of the wireless signal, a specification, a system setting, a user input, a situation, an event, a time table, a strategy, or a plan.

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

[0432] Clause C11. The method / device / system / software of the compensation type wireless monitoring system in clauses C1 (to 10), further including, in a calibration stage, transmitting a calibration wireless signal from a calibration type 1 heterogeneous wireless device through a calibration wireless multipath channel of a calibration venue, where the calibration wireless multipath channel is affected by a calibration operation of a calibration object at the calibration venue; receiving the calibration wireless signal through the calibration wireless multipath channel, where the received calibration signal differs from the transmitted calibration wireless signal due to the calibration wireless multipath channel of the calibration venue and the calibration operation of the calibration object; obtaining a time series of calibration CIs of the received calibration wireless multipath channel using a calibration processor, a 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, the goal is to find a compensation or monotonic mapping such that after compensation of the monotonic mapping is applied, the compensated calibration behavior resembles the target behavior.

[0434] The method / device / system / software of the compensated wireless monitoring system in paragraphs C12.C11 further includes, during the calibration phase, calculating a time series of calibration MI based on the time series of calibration CI, calculating a time series of calibration analysis based on the time series of calibration MI, comparing behavior associated with the time series of calibration CI, calibration MI, or calibration analysis with target behavior, and calculating a monotonic mapping based on the comparison of the calibration behavior and the target behavior.

[0435] Clause C13. The method / device / software of clause C12, wherein the behavior includes at least one of the following: statistical behavior, temporal behavior, frequency behavior, transform domain behavior, projection domain behavior, magnitude behavior, phase behavior, component behavior, histogram, probability distribution, cumulative histogram, characteristic function, moment generating 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 be between two timestamps, histogram of CI, MI or analytics between two characteristic points of CI, MI or analytics, histogram of CI, MI or analytics between two characteristic points of CI, MI or analytics histogram of observations related to analytics, processed CI, MI or CI between two characteristic points of an analysis, histogram of observations related to an MI or analysis, CI between two characteristic points of an observation, histogram of observations related to an MI or analysis, CI between two characteristic points of an observation, histogram of observations related to an MI or analysis, CI between two characteristic points conditioned on the condition of two characteristic points, histogram of observations related to an MI or analysis, 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, minimum, zero crossing, range, magnitude, phase, component magnitude, component phase, variance, standard deviation, dispersion, variance, kurtosis, entropy, information content, moment, central moment, correlation, correlation coefficient, covariance, autocorrelation function (ACF), autocovariance function, cross-correlation or cross-covariance, wherein the observables associated with the items comprise at least one of the items, the magnitudes of the items, the phases of the items, the features of the items, the components of the items, the magnitudes of the components of the items, the phases of the components of the items, the features of the components of the items, or functions of any of the above; Here, any characteristic point is either a local maximum, a local minimum, or a zero crossing point.

[0436] In some embodiments, possible methods for obtaining the monotonic mapping include (1a) looking up at the calibration stage, (1b) subtracting at the calibration stage, and (2) subtracting from a user or another device (e.g., a server) or system setting / specification.

[0437] Paragraph C14. The method / device / system / software of the compensated wireless monitoring system of paragraph C12, further including the monotonic mapping being (1) calculated based on search or inference during a calibration phase; (2) obtained from a specification, configuration, user input, or another device; (3) calculated based on supervised, unsupervised, or semi-supervised learning; or (4) obtained by artificial intelligence, machine learning, deep learning, or neural networks.

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

[0439] Clause C15. The method / device / system / software of the compensated wireless monitoring system of clause C14, further comprising, in the calibration phase, searching a determined number of candidate monotonic mappings, and for each candidate monotonic mapping, applying a respective compensation including the candidate monotonic mapping to the calculation of the TSA, calculating a respective similarity score between the target behavior and the resulting behavior associated with the calibration CI, calibration MI, or calibration analysis time series after the respective compensation has been applied, and selecting the monotonic mapping as the candidate monotonic mapping with the greatest similarity score.

[0440] In some embodiments, a possible method for obtaining a monotonic mapping involves (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 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 in a calibration phase.

[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 estimating the monotonic mapping as a monotonic line connecting the N control points in a calibration phase.

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

[0447] Clause C19. The method / device / system / software of the compensated wireless monitoring system of clause C18, wherein the monotonic line comprises at least one of a linear map, a quadratic map, a cubic map, an affine map, a polynomial map, an exponential map, a logarithmic map, a convex map, a concave map, a spline map, a piecewise linear map, a piecewise quadratic map, a piecewise cubic map, a monotonically increasing map, or a 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] Clause C20. The method / device / system / software of the compensated wireless monitoring system of clause C17, further comprising estimating the monotonic mapping as a curve fitted to the N control points according to a fitting criterion.

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

[0451] Item C21. The method / device / system / software of the compensated wireless monitoring system of item C20, further including fitting the curve to the N control points according to a least-squares fitting criterion.

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

[0453] Clause C22. The method / device / system / software of the compensated wireless monitoring system of clause C17, further including the monotonic mapping being estimated by applying regression, linear regression, robust linear regression, orthogonal regression, Deming regression, major axis regression, separation regression, polynomial regression, or regression dilution to the N control points.

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

[0455] Paragraph C23. The method / device / system / software of the compensated wireless monitoring system of paragraph C17, wherein the N control points include at least one of the following: (Mean of X, Mean of Y), (Weighted mean of X, Weighted mean of Y), (Ternium mean of X, Ternium 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, the monotonic mapping may be computed directly based on the cumulative distribution functions F_X and F_Y.

[0457] Clause C24. The method / device / system / software of the compensated wireless monitoring system of clause C16, further including, during a calibration phase, determining a behavior of a univariate observable X associated with the CI, MI, or analysis value based on a time series of calibration CIs, a time series of calibration MIs, or a time series of calibration analyses, the behavior including a cumulative univariate distribution F_X of X; determining a target behavior of a target observable Y, the target behavior including 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, the monotonic mapping may be computed directly based on the formulas for the cumulative distribution functions F_X and F_Y.

[0459] Clause C25. The method / device / system / software of the compensated wireless monitoring system of clause C24, further comprising estimating the monotonic mapping as F_Y^{-1}[F_X(X)], where F_Y^{-1} is the inverse of the function F_Y.

[0460] In some embodiments, since the formulas can be complex, approximations can be used instead.

[0461] Clause C26. The method / device / system / software of the compensated wireless monitoring system of clause C24, further comprising estimating a monotonic mapping as an approximation of F_Y^{-1}(F_X(X)), where F_Y^{-1} is the inverse of the function F_Y, and the approximation comprises at least one of a linear map, a quadratic map, a cubic map, an affine map, a polynomial map, an exponential map, a logarithmic map, a convex map, a concave map, a spline map, a piecewise linear map, a piecewise quadratic map, or a piecewise cubic map.

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

[0463] The method / device / system / software for a compensated wireless monitoring system of paragraph C27.C16 further includes, during a calibration phase, determining a behavior of a multivariate observable X associated with a CI, MI, or analysis value based on a time series of calibration CIs, a time series of calibration MIs, or a time series of calibration analyses, the behavior including N multivariate tuples {X_1,X_2,...X_N}; determining a target behavior of a multivariate target observable Y, the target behavior including N multivariate tuples {Y_1,Y_2,...Y_N}; defining N control points of a monotonic mapping by mapping the N multivariate tuples of X to the N multivariate tuples of Y, the control points being determined as (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, hyperplanes or manifolds may be used to (piecewise) connect some control points.

[0465] Item C28. The method / device / system / software of the compensated wireless monitoring system of item C27, further comprising, during the calibration phase, estimating the monotonic mapping as a combination of a plurality of piecewise hyperplanes each connecting some of the N control points.

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

[0467] Clause C29. The method / device / system / software of the compensated wireless monitoring system of clause C27, further comprising, in the calibration stage, estimating the monotonic mapping as a manifold or hyperplane that fits the N control points according to a fitting criterion.

[0468] In some embodiments, the monotonic mapping may be estimated based on a formula.

[0469] The method / device / system / software of the compensated wireless monitoring system in paragraph C30. paragraph C27 further includes, in the calibration stage, estimating a monotonic mapping based on the cumulative distribution function of X and the cumulative distribution function of Y.

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

[0471] Section C31. The method / device / system / software for a compensated wireless monitoring system in Section C14 further includes, in a calibration phase, determining a monotonic mapping including a first monotonic mapping and a second monotonic mapping, determining and searching for a plurality of candidate first monotonic mappings, estimating a respective second monotonic mapping for each candidate first monotonic mapping, applying each compensation including the candidate first monotonic mapping and each estimated second monotonic mapping to calculate a TSA, calculating each similarity score between the target behavior and the resulting behavior associated with the calibration CI, calibration MI, or calibration analysis time series after each compensation has been applied, and selecting the monotonic mapping as the candidate first monotonic mapping and the respective estimated second monotonic mapping with the largest similarity score.

[0472] Paragraph C32. The method / device / system / software of the compensated wireless monitoring system of paragraph C12, further including obtaining target behavior from a specification, system configuration, user input, a server, a cloud server, a remote server, a local server, a sensing server, or another device.

[0473] Paragraph C33. The method / device / system / software of the compensated wireless monitoring system of paragraph C12, further comprising modifying target behavior based on specifications, system settings, user input, conditions, events, time tables, strategies, and plans.

[0474] The method / device / system / software of a compensated wireless monitoring system in paragraph C34.C11, wherein the first bandwidth of the calibration wireless signal comprises the second bandwidth of the wireless signal, and the first bandwidth of the calibration wireless signal comprises the second bandwidth of the wireless signal.

[0475] Paragraph C35. The method / device / system / software of a compensated wireless monitoring system of paragraph C11, wherein the calibration object resembles the object in a first way and the calibration motion of the calibration object resembles the motion of the object in a second way.

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

[0477] Paragraph C37. The method / device / system / software of paragraph C36 for a compensated wireless monitoring system, wherein the target behavior includes a set of base behaviors that may appear in the compensated wireless monitoring system.

[0478] Item C38. The method / device / system / software of the compensated wireless monitoring system in item C11 further includes, in a recalibration stage after the calibration stage, calculating a time series of a recalibration MI based on the recalibration TSCI, comparing the behavior of the time series of the recalibration MI with the recalibration target behavior, calculating an updated monotonic mapping based on the comparison of the behavior of the time series of the recalibration MI with the recalibration target behavior, and replacing the monotonic mapping with the updated monotonic mapping in the compensation for the calculation of the TSA.

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

[0480] Paragraph C39. The method / device / system / software of a compensated wireless monitoring system in paragraph C11 (or 12), wherein the calibration Type 1 device is a reference Type 1 device and 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] Paragraph C40. A method / device / system / software for a compensated wireless monitoring system in paragraph C11 (or 12), wherein the calibration Type 1 device is a Type 1 device, the calibration Type 2 device is a reference Type 2 device, and the compensation and monotonic mapping are adjusted for 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] Paragraph C41. A method / device / system / software for a compensated wireless monitoring system in paragraph C11 (or 12), wherein the calibration type 1 device is a type 1 device and the calibration type 2 device is a type 2 device, and the compensation and monotonic mapping are adjusted for the pair of type 1 and type 2 devices to compensate the calculation of the TSA based on the TSCI obtained from wireless signals transmitted between the pair of devices for motion monitoring.

[0483] Clause C42. A method / device / system / software for a compensated wireless monitoring system in clause 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, and wherein the compensation and monotonic mapping are customized for any pair of generic Type 1 devices represented by the representative Type 1 device and generic Type 2 devices represented by the representative Type 2 device to compensate 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, and wherein the Type 1 device is a generic Type 1 device and the Type 2 device is a generic Type 2 device.

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

[0485] Clause C43. The method / device / system / software of the compensated wireless monitoring system of clause C1, comprising: calculating a second time series of analytical values ​​based on the time series of MI; and calculating a compensated second time series of analytical values ​​by applying a second compensation to the calculation of the second time series of analytical values, wherein the second compensation includes a second monotonic mapping for monitoring motion based on the compensated second time series of analytical values.

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

[0487] Item C44. The method / device / system / software of the compensated wireless monitoring system of item 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 may be different if the wireless signals for Type 1 and Type 2 devices are the same but different MIs are used.

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

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

[0491] Item C46. The method / device / system / software of the compensated wireless monitoring system of item 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, the monotonic mapping may be different if the Type 1 devices are different but the Type 2 devices are the same.

[0493] Clause C47. A method / apparatus / system / software for a compensated wireless monitoring system in clause 1, comprising: transmitting a second wireless signal from a second Type 1 heterogeneous wireless device of the 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; The method includes using a processor, memory, and set of instructions in the multipath channel to obtain a second TSCI of the received second wireless signal; calculating a second MI of the time series based on the second TSCI; calculating a second analysis value of the second compensated time series based on the second MI of the time series; calculating the second analysis value of the compensated time series by applying second compensation to the calculation of the second analysis value of the time series, wherein the second compensation includes a second monotonic mapping; and monitoring 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] Paragraph C48. The method / device / system / software of the compensated wireless monitoring system of paragraph 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 be different if the Type 2 devices are different but the Type 1 devices are the same.

[0497] Paragraph C49. The method / device / system / software of the compensated wireless monitoring system of paragraph C1, comprising transmitting a second wireless signal from a Type 1 heterogeneous wireless device of the wireless sensing system through a wireless multipath channel of the 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 the object. using a processor, a second memory, and a second instruction set, obtaining a second TSCI of the wireless multipath channel based on the received second wireless signal; computing a second MI of the time series based on the second TSCI; computing a second analysis value of the time series based on the second MI of the time series; computing a compensated second analysis value of the time series by applying second compensation to the computation of the second analysis value, where the second compensation includes a second monotonic mapping; and monitoring motion based on the second analysis value of the compensated 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] Item C50. The method / device / system / software of the compensated wireless monitoring system of item 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 be different when the Type 1 and Type 2 devices are the same but the wireless signals ar...

Claims

1. 1. A method for wireless monitoring, comprising: transmitting a wireless signal from a first wireless device through a wireless multipath channel of a venue, the wireless multipath channel being affected by motion of objects within the venue; receiving the wireless signal by 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 the object; using a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory, to obtain a time series channel information (TSCI) of the wireless multipath channel based on the received wireless signal; performing a sliding time window classification, the classification comprising at least: computing a test score (TS) for each CI included in the TSCI within the sliding time window based on a number of respective temporally adjacent CIs, wherein the TS includes at least one of difference, magnitude, vector similarity, vector dissimilarity, dot product, and cross product; and classifying each CI included in the TSCI within the sliding time window based on a corresponding TS; performing the sliding time window classification by Computing a motion information (MI) for the sliding time window based on the TSCI and the classification of the sliding time window; monitoring the motion of the object based on the MI; A method comprising:

2. 2. The method of claim 1, wherein the MI comprises: a similarity score between two temporally adjacent CIs included in the TSCI; and ACF (autocorrelation function) of the TSCI; The feature points of the ACF; The method is calculated based on at least one of the following:

3. 3. The method of claim 1 or 2, further comprising: If the sliding time window is classified as a first sliding window class based on the classification, calculating the MI using a first method based only on CIs 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, calculating the MI using a second method 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, calculating the MI using a third method based on a first subset of the CIs included in the TSCI within the sliding time window without using a second subset of the CIs included in the TSCI within the sliding time window, wherein the first subset and the second subset are disjoint; A method comprising:

4. 4. The method of claim 3, further comprising: Calculating a linkwise test score (LTS) based on the total values ​​for all TSs for the CIs included in the TSCI within the sliding time window; performing the classification of the sliding time window based on the LTS; A method comprising:

5. 5. The method of claim 4, If the LTS is less than a first threshold, the sliding time window is classified as a first sliding window class or a second sliding window class; If the LTS is greater than a second threshold, the sliding time window is classified as a third sliding window class.

6. 4. The method of claim 3, 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 smaller than a third threshold, and as a second CI class if the corresponding TS is greater than a fourth threshold.

7. 7. The method of claim 6, If all CIs included in the TSCI within the 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; A method in which 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.

8. 7. The method of claim 6, 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, each run including a distinct run length of consecutive CIs of a distinct same CI class within the sliding time window, each run length being one of a number, amount, or count greater than zero; classifying the sliding time window based on runs of first-class CIs and second-class CIs and the respective run lengths; A method comprising:

9. 8. The method of claim 7, If at least one selected run of the first class CI is selected, the sliding time window is classified as a third sliding window class; If the selected run of the first class CI is not selected, the sliding time window is classified as a second sliding window class.

10. 10. The method of claim 9, further comprising: selecting at least one selected run of the first class CI based on a run length of each run of the first class CI and the TS associated with the run; and calculating the MI based on the at least one selected run of a first class CI; The method, 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 the sliding time window.

11. 10. The method of claim 9, If the individual run lengths of a leading run of consecutive first-class CIs, including the CI that is the first in the sliding time window, are greater than a first individual threshold, a 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 CI that is the last one in the sliding time window, are greater than a second individual threshold, a second selected run is selected as the leading run; The method wherein any selected run that is not a leading run or a trailing run is selected if the respective run length of the leading run or the trailing run is greater than a third respective threshold.

12. 10. The method of claim 9, The at least one selected run is selected such that the quantity of the at least one selected run is equal to or less than a predetermined number; and The method wherein the at least one selection run is selected such that for each selection run, all associated TSs are below a threshold.

13. 10. The method of claim 9, further comprising: If 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 a first class CI included in the TSCI within the sliding time window; constructing the second subset by including all second-class CIs that are included in the TSCIs within the sliding time window; If the sliding time window is classified as the second sliding window class, calculating the MI as a sum of at least one adjacent MI; Each neighboring MI is associated with one of a past neighboring sliding time window of a CI included in the TSCI, a future neighboring sliding time window of a CI included in the TSCI, or a neighboring sliding time window of a CI included in another TSCI; A method wherein a neighboring MI is computed based on at least one CI included in the TSCI outside the sliding time window.

14. 10. The method of claim 9, further comprising: computing at least one interim MI for the sliding time window, each interim MI being computed based on a separate selection run of first-class CIs included in the TSCI within the sliding time window; calculating the MI as a sum of the at least one interim MI; A method comprising:

15. 10. The method of claim 9, further comprising: determining a selected run as the leading run of a CI included in the TSCI within the sliding time window; combining the leading run of a CI with a trailing run of a CI included in the TSCI within a previous sliding time window to form a combined run of a CI included in the TSCI; calculating a first interim MI based on the composite run of CIs; A method comprising:

16. 10. The method of claim 9, further comprising: determining a selected run as the last run of a CI included in the TSCI within the sliding time window; combining the trailing run of CI with the leading run of CI included in the TSCI within a next sliding time window to form a combined run of CI included in the TSCI; calculating a second interim MI based on the composite run of CIs; A method comprising:

17. 15. The method of claim 14, further comprising: For each interim MI calculated based on a respective selection run, calculating a respective calculated weight based on the run length of the respective selection run; computing the MI as a weighted aggregate of the at least one interim MI, each interim MI being weighted by the respective computed weight; A method comprising:

18. 15. The method of claim 14, further comprising: A method comprising: calculating the MI as a combined value of the at least one interim MI and at least one adjacent MI, each adjacent MI being associated with one of 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.

19. 1. A wireless monitoring system, comprising: a first wireless device configured to transmit a wireless signal through a wireless multipath channel of a venue, the wireless multipath channel being affected by motion of objects within the venue; and a second wireless device configured to receive the wireless signal 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 the object; and a processor, the processor comprising: Obtaining TSCI (Time Series Channel Information) of the wireless multipath channel based on the received wireless signal; performing a sliding time window classification, the classification comprising at least: computing a test score (TS) for each CI included in the TSCI within the sliding time window based on a number of respective temporally adjacent CIs, wherein the TS includes at least one of difference, magnitude, vector similarity, vector dissimilarity, dot product, and cross product; and classifying each CI included in the TSCI within the sliding time window based on a corresponding TS; performing the sliding time window classification by Computing a motion information (MI) for the sliding time window based on the TSCI and the classification of the sliding time window; monitoring the motion of the object based on the MI; A wireless monitoring system configured to:

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