IP Library › Granted Patent US 12,695,658

Granted Patent B2

US 12,695,658 · App. 17/838,231 · Granted Jul 28, 2026

Method, apparatus, and system for identifying and qualifying devices for wireless sensing

Inventors: Beibei Wang (Clarksville, MD); Yuqian Hu (College Park, MD); Yi Han (Ellicott City, MD); Hung-Quoc Duc Lai (Parkville, MD); David N. Claffey (Somerville, MA); Chun-I Chen (Brookeville, MD); K. J. Ray Liu (Potomac, MD); Oscar Chi-Lim Au (San Jose, CA)

Assignee: ORIGIN WIRELESS, INC.

H04L27/362 H04B1/38 H04W72/21

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Quick Facts

Patent No.: US 12,695,658
App. No.: 17/838,231
Filed: Jun 12, 2022
Granted: Jul 28, 2026
Kind: B2
Examiner: LU, ZHIYU
Art Unit: 2665
USPC: 455/67.11

External Links

USPTO Patent Center↗ Full Patent PDF↗

Abstract

Methods, apparatus and systems for device identification and qualification for wireless sensing are described. In one example, a described system comprises: a transmitter configured to transmit a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a movement of an object in the venue; a receiver configured to receive the wireless signal through the wireless multipath channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the movement of the object; and a processor. The processor is configured for: obtaining a number of time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal, computing a time series of motion analytics (TSMA) based on the number of TSCI, computing an information of a target device based on the TSMA. The target device is one of: the transmitter, the receiver, or an additional device in the venue. The information of the target device comprises at least one of: an identity, a name, a label, a tag, or a location.

Claims (148)

1 . A system for wireless sensing, comprising:

a plurality of transmitter-receiver pairs in a venue, each transmitter-receiver pair comprising a wireless transmitter and a wireless receiver identified and paired by the system, each transmitter-receiver pair comprising a respective unidentified device to be identified by a user in an onboarding phase of the system, the respective unidentified device being either the wireless transmitter or the wireless receiver being identified by the system with a respective system-generated first tag information, wherein:

the respective system-generated first tag information of the respective unidentified device is generated by the system,

before the onboarding phase, the respective system-generated first tag information of the respective unidentified device is displayed to the user of the system and is unrecognizable to the user of the system,

in the onboarding phase: a wireless signal is transmitted in each transmitter-receiver pair from the transmitter to the receiver through a respective wireless multipath channel in the venue,

onboarding-phase wireless sensing is performed in the plurality of transmitter-receiver pairs by computing a plurality of time series of motion analytics (TSMA), each TSMA computed based on a respective received wireless signal in a respective transmitter-receiver pair,

the user is prompted to arbitrarily choose one of the unidentified devices as a user-chosen unidentified device, move near the user-chosen unidentified device and perform a sequence of prompted user proximity motions near the user-chosen unidentified device when the onboarding-phase wireless sensing is being performed,

the user-chosen unidentified device is identified interactively from among the unidentified devices based on a user input after a presentation of an outcome of the onboarding-phase wireless sensing during the prompted user proximity motions of the user; and

a plurality of processors comprising a respective processor associated with each respective transmitter-receiver pair and an additional processor, wherein the respective processor associated with each respective transmitter-receiver pair is configured for:

obtaining a respective time series of channel information (TSCI) of the respective wireless multipath channel based on the respective received wireless signal in the onboarding phase, and

computing a respective one of the plurality of TSMA based on an autocorrelation function of the respective TSCI,

wherein the additional processor is configured for:

prompting the user interactively to move to a proximity of the user-chosen unidentified device and to perform the sequence of prompted user proximity motions relative to the user-chosen unidentified device, the sequence of prompted user proximity motions comprising a sequence of individual user movements with associated timings,

obtaining the plurality of TSMA from the respective processors associated with the plurality of transmitter-receiver pairs,

presenting the plurality of TSMA to the user using a first user-interface of a user device,

obtaining a first user input from the user using a second user-interface of the user device, the first user input comprising a choice of one of the plurality of TSMA as a user-chosen TSMA,

identifying the user-chosen unidentified device as an unidentified device of the transmitter-receiver pair associated with the user-chosen TSMA,

obtaining a second user input from the user using the second user-interface of the user device, the second user input comprising a second tag information for the user-chosen TSMA and the user-chosen unidentified device,

onboarding the user-chosen unidentified device to the system based on the second tag information,

wherein any tag information comprises at least one of: an identity, a name, a label, or a tag.

2 . The system of claim 1 , wherein each respective processor is further configured for:

monitoring the movement of another object in the venue by the plurality of transmitter-receiver pairs in a wireless sensing task after the onboarding phase of the system by:

receiving a respective second wireless signal from the transmitter of each transmitter-receiver pair by the respective receiver through the respective wireless multipath channel of the venue, wherein the respective received second wireless signal differs from the respective transmitted second wireless signal due to the respective wireless multipath channel and the movement of the another object,

obtaining a respective second TSCI of the respective wireless multipath channel by the respective processor associated with the respective transmitter-receiver pair based on the respective received second wireless signal,

computing a respective time series of second motion analytics (TSSMA) based on the respective second TSCI by the respective processor, and

monitoring the movement of the another object based on any TSSMA or any second TSCI by the respective processor or the additional processor.

3 . The system of claim 1 , wherein the additional processor is further configured for:

analyzing a behavior of the plurality of TSMA during the onboarding phase of the system;

performing an analysis of the plurality of TSMA based on at least one of: a predicted behavior of the TSMA of the user-chosen unidentified device in response to the sequence of prompted user proximity motions and the associated timings, or a predicted identity of the target device;

computing a predicted TSMA among the plurality of TSMA associated with the user-chosen unidentified device based on the analysis of the plurality of TSMA;

presenting the predicted TSMA to the user using the first user-interface of the user device; wherein the second user input comprises a confirmation or a rejection of the predicted TSMA.

4 . The system of claim 1 , wherein the additional processor is further configured for:

generating a prompting to the user to undergo the sequence of prompted user proximity motions,

wherein the prompting comprises at least one of: a signaling, a guidance, an instruction, a visual prompting, an audio prompting, a mechanical prompting, a vibration, or a mechanical motion.

5 . The system of claim 1 , wherein the additional processor is further configured for:

generating a real-time presentation of the plurality of TSMA on the user device,

wherein the real-time presentation comprises at least one of: a visual presentation, an animated presentation, a multimedia presentation, an audio presentation, or a mechanical presentation.

6 . The system of claim 1 , wherein:

a location of the user-chosen unidentified device in the venue is unknown to the system but known to the user; and

the second user input comprises the location.

7 . The system of claim 6 , wherein the additional processor is further configured for:

computing an additional tag information of the location of the target device interactively and dynamically based on an additional user input on the user device, wherein:

the location is a region of the venue, and

the additional tag information comprises at least one of: an identity, a name, a label or a tag of the region of the venue.

8 . The system of claim 7 , wherein:

all transmitter-receiver pairs share a common transmitter;

the unidentified device of any transmitter-receiver pair is the receiver of the pair; and

the respective processor associated with each transmitter-receiver pair is further configured to transmit the respective TSMA to the additional processor.

9 . The system of claim 1 , wherein the additional processor is further configured for:

generating a real-time presentation of the plurality of TSMA on the user device,

wherein a subset of the plurality of TSMA are presented visually on the user device in a side-by-side manner to enable visual comparison by the user.

10 . The system of claim 7 , wherein:

all receivers of the plurality of transmitter-receiver pairs are a common receiver;

all respective processors associated with the plurality of transmitter-receiver pairs are a common processor, which is the additional processor, associated with the common receiver; and

the unidentified device of any transmitter-receiver pair is the transmitter of the pair.

11 . The system of claim 10 , wherein the additional processor is further configured for:

generating a real-time presentation of the plurality of TSMA on the user device,

wherein the plurality of TSMA are presented visually on the user device in a side-by-side manner to enable visual comparison by the user.

12 . The system of claim 1 , wherein:

the user comprises at least one of: a living object, a plant, an animal, a pet, a human, or a machine; and

the sequence of prompted user proximity motions comprises at least one of:

touching the target device, tapping the target device,

a movement of the user towards the target device,

a movement of the user away from the target device,

a radial movement of the user with respect to the target device,

a tangential movement of the user with respect to the target device,

a tapping motion, a repeating movement, a repeated movement,

a periodic movement, a transient movement,

a movement with a timing, a movement with a count, or

an intermittent movement, or a movement with timed pauses.

13 . The system of claim 1 , wherein the additional processor is further configured for performing at least one of the following in the onboarding phase of the system based on the second tag information:

generating a recommended configuration;

presenting the recommended configuration on the user device;

receiving a confirmation or rejection or modification of the recommended configuration based on a user input on the user device;

customizing or configuring the target device;

customizing or configuring the system;

customizing or configuring a network or an interconnection of the system;

customizing or configuring a server of the system;

customizing or configuring an app on a user device;

customizing or configuring a presentation of the system;

customizing or configuring a wireless sensing task, a wireless sensing algorithm, a fusion algorithm, or an aggregation algorithm associated with the system;

confirming an admissibility, acceptability, sufficiency, suitability or a qualification of the target device;

confirming a role of the target device in the system;

qualifying at least one of: the target device, the TSCI, the wireless signal, the transmission of the wireless signal, the reception of the wireless signal, and the obtaining of the TSCI;

locating the target device;

computing a coordinate of the target device with respect to a map;

naming the target device;

renaming the target device;

obtaining a network configuration or interconnection of the system from the user; or

obtaining from the user a configuration of the wireless sensing task, a wireless sensing algorithm, a fusion algorithm, or an aggregation algorithm.

14 . The system of claim 3 , wherein the additional processor is further configured for:

recognizing a pattern associated with the predicted behavior of the TSMA of the user-chosen unidentified device in response to the sequence of prompted user proximity motions of the user; and

computing the predicted TSMA of the user-chosen unidentified device based on the recognition of the pattern associated with the predicted behavior of the TSMA of the user-chosen unidentified device.

15 . The system of claim 14 , wherein the pattern comprises at least one of:

a monotonic increasing trend of a feature of the motion analytics in the TSMA as the user moves towards the user-chosen unidentified device in a neighborhood of the user-chosen unidentified device;

a monotonic decreasing trend of the feature of the motion analytics in the TSMA as the user moves away from the user-chosen unidentified device in the neighborhood of the user-chosen unidentified device;

a local maximum of the feature of the motion analytics in the TSMA at a point of closest approach of the user in the neighborhood of the user-chosen unidentified device;

a positive derivative or differential of the feature of the motion analytics in the TSMA as the user moves towards the user-chosen unidentified device in the neighborhood of the user-chosen unidentified device;

a negative derivative or differential of the feature of the motion analytics in the TSMA as the user moves away from the user-chosen unidentified device in the neighborhood of the user-chosen unidentified device;

a zero derivative or differential of the feature of the motion analytics in the TSMA at the point of closest approach;

a negative second derivative (or differential of differential) of the feature of the motion analytics in the TSMA at the point of closest approach;

a repetition of the feature of the motion analytics in the TSMA;

a relaxation oscillation of the feature of the motion analytics in the TSMA; or

a damping of the feature of the motion analytics in the TSMA.

16 . The system of claim 1 , wherein the motion analytics comprises at least one of:

a magnitude, a phase, a time, an occurrence, a transformation, a histogram, a sample mean, a standard deviation, a variance, a length, an area, a volume, a similarity score, a distance, a classification, a breathing rate, or a heart rate.

17 . A wireless device of a system for wireless sensing, comprising:

a processor;

a memory communicatively coupled to the processor; and

a receiver communicatively coupled to the processor, wherein:

the receiver and a plurality of transmitters of the system form a plurality of transmitter-receiver pairs, each transmitter-receiver pair comprising the receiver and a respective transmitter,

each transmitter-receiver pair comprises a respective unidentified device to be identified by a user in an onboarding phase of the system,

the respective unidentified device being the respective transmitter being identified by the system with a respective system-generated first tag information,

the respective system-generated first tag information of the respective unidentified device is generated by the system,

in the onboarding phase: a respective wireless signal is transmitted in each transmitter-receiver pair from the respective transmitter to the receiver through a respective wireless multipath channel,

onboarding-phase wireless sensing is performed in the plurality of transmitter-receiver pairs by computing a plurality of time series of motion analytics (TSMA) by the processor, each TSMA computed based on the respective received wireless signal,

the user is prompted to arbitrarily choose one of the plurality of the unidentified devices as a user-chosen unidentified device, move near the user-chosen unidentified device and perform a sequence of prompted user proximity motions near the user-chosen unidentified device when the onboarding-phase wireless sensing is being performed,

the user-chosen unidentified device is identified interactively from among the plurality of unidentified devices based on a user input after a presentation of an outcome of the onboarding-phase wireless sensing during the prompted user proximity motions of the user, and

the processor is configured for:

generating a signal for prompting the user interactively to move to a proximity of the user-chosen unidentified device and to perform the sequence of prompted user proximity motions relative to the user-chosen unidentified device, the sequence of prompted user proximity motions comprising a sequence of individual user movements with associated timings,

obtaining a respective number of time series of channel information (TSCI) of the wireless multipath channel based on the respective received wireless signal,

computing the plurality of TSMA, each TSMA based on a respective autocorrelation function of the respective number of TSCI,

presenting the plurality of TSMA to the user using a first user-interface of a user device,

obtaining a first user input from the user using a second user-interface of the user device, the first user input comprising a choice of one of the plurality of TSMA as a user-chosen TSMA,

identifying the user-chosen unidentified device as an unidentified device associated with the user-chosen TSMA,

onboarding the user-chosen unidentified device to the system based on the second tag information,

wherein any tag information comprises at least one of: an identity, a name, a label, or a tag.

18 . A method for wireless sensing, comprising:

transmitting a respective wireless signal from a respective one of a plurality of transmitters of a system through a respective wireless multipath channel of a venue to a receiver of the system in an onboarding phase of the system, wherein:

the receiver and the plurality of transmitters form a plurality of transmitter-receiver pairs in the system, each transmitter-receiver pair comprising the receiver and a respective transmitter,

each transmitter-receiver pair comprises a respective unidentified device to be identified by a user in the onboarding phase of the system,

the respective unidentified device being the respective transmitter being identified by the system with a respective system-generated first tag information,

the respective system-generated first tag information of the respective unidentified device is generated by the system,

in the onboarding phase: onboarding-phase wireless sensing is performed in the plurality of transmitter-receiver pairs by computing a plurality of time series of motion analytics (TSMA) by the processor, each TSMA computed based on the respective received wireless signal,

the user is prompted to arbitrarily choose one of the plurality of unidentified devices as a user-chosen unidentified device, move near the user-chosen unidentified device and perform a sequence of prompted user proximity motions near the user-chosen unidentified device when the onboarding-phase wireless sensing is being performed,

obtaining a respective number of time series of channel information (TSCI) of the wireless multipath channel based on the respective received wireless signal;

computing the plurality of TSMA, each TSMA based on a respective autocorrelation function of the respective number of TSCI;

presenting the plurality of TSMA to the user using a first user-interface of a user device;

obtaining a first user input from the user using a second user-interface of the user device, the first user input comprising a choice of one of the plurality of TSMA as a user-chosen TSMA;

identifying the user-chosen unidentified device as an unidentified device associated with the user-chosen TSMA;

onboarding the user-chosen unidentified device to the system based on the second tag information,

wherein any tag information comprises at least one of: an identity, a name, a label, or a tag.

Assignments (1)

ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 20, 2026

From: WANG, BEIBEI; HU, YUQIAN; HAN, YI; LAI, HUNG-QUOC DUC; CLAFFEY, DAVID N.; CHEN, CHUN-I; LIU, K. J. RAY; AU, OSCAR CHI-LIM

To: ORIGIN WIRELESS, INC.

Reel/Frame 074706/0059 →

Continuity (42)

Continuation In Part 15326112 · Jul 17, 2015

Continuation In Part 16127151 · Sep 10, 2018

Continuation In Part 16790610 · Feb 13, 2020

Continuation In Part 16790627 · Feb 13, 2020

Continuation In Part 16871000 · May 10, 2020

Continuation In Part 16871004 · May 10, 2020

Continuation In Part 16871006 · May 10, 2020

Continuation In Part 16909913 · Jun 23, 2020

Continuation In Part 16909940 · Jun 23, 2020

Continuation In Part 16945827 · Aug 1, 2020

Continuation In Part 16945837 · Aug 1, 2020

Continuation In Part 17019270 · Sep 13, 2020

Continuation In Part 17113023 · Dec 5, 2020

Continuation In Part 17149625 · Jan 14, 2021

Continuation In Part 17149667 · Jan 14, 2021

Continuation In Part 17180763 · Feb 20, 2021

Continuation In Part 17180762 · Feb 20, 2021

Continuation In Part 17180760 · Feb 20, 2021

Continuation In Part 17180766 · Feb 20, 2021

Continuation In Part 17214841 · Mar 27, 2021

Continuation In Part 16667648 · Oct 29, 2019

Continuation In Part 16446589 · Jun 19, 2019

Continuation In Part 16101444 · Aug 11, 2018

Continuation In Part 17214836 · Mar 27, 2021

Continuation In Part 17352185 · Jun 18, 2021

Continuation In Part 17352306 · Jun 20, 2021

Continuation In Part 17492599 · Oct 2, 2021

Continuation In Part 17492598 · Oct 2, 2021

Continuation In Part 17537432 · Nov 29, 2021

Continuation In Part 17539058 · Nov 30, 2021

Continuation In Part 17827902 · May 30, 2022

Provisional Application 63209907 · Jun 11, 2021

Provisional Application 63235103 · Aug 19, 2021

Provisional Application 63253083 · Oct 6, 2021

Provisional Application 63276652 · Nov 7, 2021

Provisional Application 63281043 · Nov 18, 2021

Provisional Application 63293065 · Dec 22, 2021

Provisional Application 63300432 · Jan 18, 2022

Provisional Application 63308927 · Feb 10, 2022

Provisional Application 63332658 · Apr 19, 2022

Provisional Application 63349082 · Jun 4, 2022

Related Publication 20220303167A1 · Sep 22, 2022

References Cited (48)

US 20090102654A1 · Schoettle · 2009 [cited by examiner]

US 20100052870A1 · King · 2010 [cited by examiner]

US 20110096954A1 · Dahl · 2011 [cited by examiner]

US 20120001875A1 · Li · 2012 [cited by examiner]

US 20140235171A1 · Molettiere · 2014 [cited by examiner]

Xu et al., “Improving RF-Based Device-Free Passive Localization In Cluttered Indoor Environments Through Probabilistic Classification Methods”, Apr. 2012, ACM 978-1-4503-1227—Jan. 12, 2004, pp. 209-220 (Year: 2012). [cited by examiner]

Wang et al., “E-eyes: Device-free Location-oriented Activity Identification Using Fine-grained WiFi Signatures”, Sep. 2014, ACM 978-1-4503-2783—Jan. 14, 2009, pp. 617-628 (Year: 2014). [cited by examiner]

Fu et al., “Writing in the Air with WiFi Signals for Virtual Reality Devices”, May 2018, IEEE Transactions on Mobile Computing, vol. 18, No. 2, pp. 473-484 (Year: 2018). [cited by examiner]

Beibei Wang et al., “The Promise of Radio Analytics: A Future Paradigm for Wireless Positioning, Tracking, and Sensing”, IEEE Signal Processing Magazine, vol. 35, No. 3, pp. 59-80, May 2018. [cited by applicant]

Feng Zhang et al., “WiSpeed: A Statistical Electromagnetic Approach for Device-Free Indoor Speed Estimation”, IEEE Internet of Things Journal, vol. 5, No. 3, pp. 2163-2177, Jun. 2018. [cited by applicant]

Feng Zhang et al., “WiBall: A Time-Reversal Focusing Ball Method for Decimeter-Accuracy Indoor Tracking”, IEEE Internet of Things Journal, vol. 5, No. 5, pp. 4031-4041, Oct. 2018. [cited by applicant]

Chenshu Wu et al., “EasiTrack: Decimeter-Level Indoor Tracking with Graph-based Particle Filtering”, IEEE Internet of Things Journal., vol. 7, No. 3, pp. 2397-2411, Mar. 2020. [cited by applicant]

Chenshu Wu et al., “RF-based Inertial Measurement”, ACM SIGCOMM, Beijing, China, Aug. 19-24, 2019. [cited by applicant]

Wei-Hsiang Wang et al., “Device-Free Room-Level Localization with WiFi Utilizing Spatial-Frequency-Time Diversity,” IEEE Internet of Things Journal, vol. 11, No. 21, pp. 35689-35698, Nov. 2024. [cited by applicant]

Chenshu Wu et al., “mmTrack: Passive Multi-Person Localization Using Commodity Millimeter Wave Radio”, IEEE International Conference on Computer Communications, Beijing, China, Apr. 27-30, 2020. [cited by applicant]

Qinyi Xu et al., “Radio Biometrics: Human Recognition Through a Wall”, IEEE Trans. on Information Forensics and Security, vol. 12, No. 5, pp. 1141-1155, May 2017. [cited by applicant]

Qinyi Xu et al., “TRIEDS: Wireless Events Detection Through the Wall”, IEEE Internet of Things Journal, vol. 4, No. 3, pp. 723-735, Jun. 2017. [cited by applicant]

Chen Chen et al., “TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection”, IEEE Trans. on Biomedical Engineering, vol. 65, No. 3, pp. 489-501, Mar. 2018. [cited by applicant]

Fengyu Wang et al., “Respiration Tracking for People Counting and Recognition”, IEEE Internet of Things Journal, vol. 7, No. 6, pp. 5233-5245, Jun. 2020. [cited by applicant]

Fengyu Wang et al., “ViMo: Multi-person Vital Sign Monitoring using Commodity Millimeter Wave Radio”, IEEE Internet of Things Journal, vol. 8, No. 3, pp. 1294-1307, Feb. 2021. [cited by applicant]

Beibei Wang et al., “Green Wireless Communications: A Time-Reversal Paradigm”, IEEE Journal of Selected Areas in Communications , special issue on Energy-Efficient Wireless Communications, vol. 29, No. 8, pp. 1698-1710,… [cited by applicant]

Feng Zhang et al., “WiDetect: Robust Motion Detection with a Statistical Electromagnetic Model”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 3, No. 3, Article 122:1-… [cited by applicant]

Yuqian Hu et al., Robust Passive Proximity Detection using Wi-Fi, IEEE Internet of Things Journal, vol. 10, No. 7, pp. 6221-6234, Apr. 2023. [cited by applicant]

Yuqian Hu et al., “DeFall: Environment-Independent Passive Fall Detection Using WiFi,” IEEE Internet of Things Journal, vol. 9, No. 11, pp. 546-557, Jun. 2022. [cited by applicant]

Sai Deepika Regani et al., “FallAware: An Explainable Learning Approach to Robust Fall Detection with WiFi,” IEEE Journal of Selected Areas in Sensors, vol. 2, pp. 71-83, Dec. 2024. [cited by applicant]

Feng Zhang et al., “SMARS: Sleep Monitoring via Ambient Radio Signals”, IEEE Trans. on Mobile Computing, vol. 20, No. 1, pp. 217-231, Jan. 2021. [cited by applicant]

Wei-Hsiang Wang et al., “WiResP: A Robust Wi-Fi-Based Respiration Monitoring via Spectrum Enhancement,” IEEE Sensors Journal, vol. 24, No. 13, pp. 20999-21011, Jul. 2024. [cited by applicant]

K. J. Ray Liu et al., “Wireless Al: Wireless Sensing, Positioning, IoT, and Communications,” Cambridge University Press, 2019. [cited by applicant]

Chenshu Wu et al., “Wi-Fi Can Do More: Toward Ubiquitous Wireless Sensing,” IEEE Communications Standards Magazine, vol. 6, No. 2, pp. 42-49, Jun. 2022. [cited by applicant]

K. J. Ray Liu et al., “Statistical Principles of Time Reversal [Perspectives],” IEEE Signal Processing Magazine, vol. 41, No. 1, pp. 31-37, Jan. 2024. [cited by applicant]

Chenshu Wu et al., “GaitWay: Monitoring and Recognizing Gait Through the Walls”, IEEE Trans. on Mobile Computing, vol. 20, No. 6, pp. 2186-2199, Jun. 2021. [cited by applicant]

Sai Deepika Regani et al., “Driver Authentication for Smart Car Using Wireless Sensing”, IEEE Internet of Things Journal, vol. 7, No. 3, pp. 2235-2246, Mar. 2020. [cited by applicant]

Qinyi Xu et al., “Wireless AI in Smart Car: How Smart a Car Can Be?”, IEEE Access. [cited by applicant]

Fengyu Wang et al., “Driver Vital Signs Monitoring Using Millimeter Wave Radio,” IEEE Internet of Things Journal, vol. 9, No. 13, pp. 11283-11298, Jul. 2022. [cited by applicant]

Xiaolu Zeng et al., “WiCPD: Wireless Child Presence Detection System for Smart Cars,” IEEE Internet of Things Journal, vol. 9, No. 24, pp. 24866-24881, Dec. 2022. [cited by applicant]

Guozhen Zhu et al., “Wi-MoID: Human and Nonhuman Motion Discrimination Using WiFi With Edge Computing,” IEEE Internet of Things Journal, vol. 11, No. 8, pp. 13900-13912, Apr. 2024. [cited by applicant]

Guozhen Zhu et al., “SrcSense: Robust WiFi-based Motion Source Recognition via Signal-Informed Deep Learning,” IEEE Journal of Selected Areas in Sensors, vol. 2, pp. 40-53, Dec. 2024. [cited by applicant]

Chenshu Wu et al., “mSense: Towards Mobile Material Sensing with a Single Millimeter-Wave Radio”, PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Virtual Online, Sep. 2020. [cited by applicant]

Feng Zhang et al., “mmEye: Super-Resolution Millimeter Wave Imaging”, IEEE Internet of Things Journal, vol. 8, No. 8, pp. 6995-7008, Apr. 2021. [cited by applicant]

Sakila S. Jayaweera et al., “HRNet: High-Resolution Neural Network for Human Imaging Using mmWave Radar,” IEEE Internet of Things Journal, vol. 12, No. 1, pp. 881-893, Jan. 2025. [cited by applicant]

Sai Deepika Regani et al., “mmWrite: Passive Handwriting Tracking using a Single Millimeter Wave Radio”, IEEE Internet of Things Journal, vol. 8, No. 17, pp. 13291-13305, Sep. 2021. [cited by applicant]

Fengyu Wang et al., “mmHRV: Contactless Heart Rate Variability Monitoring using Millimeter-Wave Radio”, IEEE Internet of Things Journal, vol. 8, No. 22, pp. 16623-16636, Nov. 2021. [cited by applicant]

Yuqian Hu et al., “mmKey: Universal Virtual Keyboard using A Single Millimeter Wave Radio”, IEEE Internet of Things Journal, vol. 9, No. 1, pp. 510-524, Jan. 2022. [cited by applicant]

Mohammed Zahid Ozturk et al., “GaitCube: Deep Data Cube Learning for Human Recognition with Millimeter-Wave Radio”, IEEE Internet of Things Journal, vol. 9, No. 1, pp. 546-557, Jan. 2022. [cited by applicant]

Sai Deepika Regani et al., “GWrite: Enabling Through-the-Wall Gesture Writing Recognition Using WiFi,” IEEE Internet of Things Journal, vol. 10, No. 7, pp. 5977-5991, Nov. 2022. [cited by applicant]

Mohammed Zahid Ozturk et al., “RadioMic: Sound Sensing via Radio Signals,” IEEE Internet of Things Journal, vol. 10, No. 5, pp. 4431-4448, Oct. 2022. [cited by applicant]

Mohammed Zahid Ozturk et al., “RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System,” IEEE/ACM Transactions on Audio Speech, and Language Processing, vol. 31, pp. 1333-1347, Mar. 2023. [cited by applicant]

Mohammed Zahid Ozturk et al., “RadioVAD: mmWave-Based Noise and Interference-Resilient Voice Activity Detection,” IEEE Internet of Things Journal, vol. 11, No. 15, pp. 26005-26019, Aug. 2024. [cited by applicant]