Continuous Multi-user Activity Tracking via Room-Scale mmWave Sensing

Continuous detection of human activities and presence is essential for developing a pervasive interactive smart space. Existing literature lacks robust wireless sensing mechanisms capable of continuously monitoring multiple users' activities without prior knowledge of the environment. Developing such a mechanism requires simultaneous localization and tracking of multiple subjects. In addition, it requires identifying their activities at various scales, some being macro-scale activities like walking, squats, etc., while others are micro-scale activities like typing or sitting, etc. In this paper, we develop a holistic system called MARS using a single Commercial off the-shelf (COTS) Millimeter Wave (mmWave) radar, which employs an intelligent model to sense both macro and micro activities. In addition, it uses a dynamic spatial time sharing approach to sense different subjects simultaneously. A thorough evaluation of MARS shows that it can infer activities continuously with a weighted F1-Score of>94% and an average response time of approx 2 sec, with 5 subjects and 19 different activities.

[1]  Lei Wang,et al.  DF-Sense: Multi-user Acoustic Sensing for Heartbeat Monitoring with Dualforming , 2023, MobiSys.

[2]  L. Kong,et al.  mm3DFace: Nonintrusive 3D Facial Reconstruction Leveraging mmWave Signals , 2023, MobiSys.

[3]  Jun Yu Li,et al.  Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars , 2023, ACM Trans. Internet Things.

[4]  F. Gringoli,et al.  Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations , 2023, 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[5]  Prasenjit Karmakar,et al.  mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving , 2023, 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[6]  Prasenjit Karmakar,et al.  mmAssist : Passive Monitoring of Driver's Attentiveness Using mmWave Sensors , 2023, 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS).

[7]  Fusang Zhang,et al.  Mobi2Sense: empowering wireless sensing with mobility , 2022, MobiCom.

[8]  Wenyao Xu,et al.  mmEve: eavesdropping on smartphone's earpiece via COTS mmWave device , 2022, MobiCom.

[9]  S. Lee,et al.  Experience: practical problems for acoustic sensing , 2022, MobiCom.

[10]  P. Vasseur,et al.  Millimeter Wave FMCW RADARs for Perception, Recognition and Localization in Automotive Applications: A Survey , 2022, IEEE Transactions on Intelligent Vehicles.

[11]  Hsiao-Chun Wu,et al.  Noninvasive Human Activity Recognition Using Millimeter-Wave Radar , 2022, IEEE Systems Journal.

[12]  S. Lee,et al.  LASense , 2022, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[13]  Kui Ren,et al.  Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals , 2021, SenSys.

[14]  Jun Luo,et al.  RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network , 2021, IEEE Transactions on Mobile Computing.

[15]  Yaowen Yang,et al.  SiWa: see into walls via deep UWB radar , 2021, MobiCom.

[16]  Jun Luo,et al.  MoVi-Fi: motion-robust vital signs waveform recovery via deep interpreted RF sensing , 2021, MobiCom.

[17]  Peixian Gong,et al.  MMPoint-GNN: Graph Neural Network with Dynamic Edges for Human Activity Recognition through a Millimeter-Wave Radar , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

[18]  Anfu Zhou,et al.  m-Activity: Accurate and Real-Time Human Activity Recognition Via Millimeter Wave Radar , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Michele Rossi,et al.  Real-Time People Tracking and Identification From Sparse mm-Wave Radar Point-Clouds , 2021, IEEE Access.

[20]  Guoliang Xing,et al.  milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection , 2021, IoTDI.

[21]  Chris Harrison,et al.  Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition , 2021, CHI.

[22]  Raja Jurdak,et al.  SolAR: Energy Positive Human Activity Recognition using Solar Cells , 2021, 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[23]  Jun Luo,et al.  RF-net: a unified meta-learning framework for RF-enabled one-shot human activity recognition , 2020, SenSys.

[24]  Zhengxiong Li,et al.  VocalPrint: exploring a resilient and secure voice authentication via mmWave biometric interrogation , 2020, SenSys.

[25]  Anthony Rowe,et al.  Robust and Practical WiFi Human Sensing Using On-device Learning with a Domain Adaptive Model , 2020, BuildSys@SenSys.

[26]  Jian Liu,et al.  Acoustic-based sensing and applications: A survey , 2020, Comput. Networks.

[27]  Sampath Rangarajan,et al.  RFGo: a seamless self-checkout system for apparel stores using RFID , 2020, MobiCom.

[28]  Yuan Yuan,et al.  In-Home Daily-Life Captioning Using Radio Signals , 2020, ECCV.

[29]  Po-Hsuan Tseng,et al.  mmWave Radar-based Hand Gesture Recognition using Range-Angle Image , 2020, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[30]  Chris Xiaoxuan Lu,et al.  See through smoke: robust indoor mapping with low-cost mmWave radar , 2019, MobiSys.

[31]  Wei Cui,et al.  WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM , 2019, IEEE Transactions on Mobile Computing.

[32]  Mani Srivastava,et al.  RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar , 2019, mmNets.

[33]  P. Mohapatra,et al.  mmSense: Multi-Person Detection and Identification via mmWave Sensing , 2019, mmNets.

[34]  Dina Katabi,et al.  Making the Invisible Visible: Action Recognition Through Walls and Occlusions , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[35]  Mary F Lesch,et al.  Employee acceptance of wearable technology in the workplace. , 2019, Applied ergonomics.

[36]  Sophia Bano,et al.  Deep human activity recognition using wearable sensors , 2019, PETRA.

[37]  Andrew Markham,et al.  mID: Tracking and Identifying People with Millimeter Wave Radar , 2019, 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).

[38]  Zi Wang,et al.  MultiTrack: Multi-User Tracking and Activity Recognition Using Commodity WiFi , 2019, CHI.

[39]  Yanwen Wang,et al.  Modeling RFID Signal Reflection for Contact-free Activity Recognition , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[40]  Chenglin Miao,et al.  Towards Environment Independent Device Free Human Activity Recognition , 2018, MobiCom.

[41]  Gierad Laput,et al.  Vibrosight: Long-Range Vibrometry for Smart Environment Sensing , 2018, UIST.

[42]  Snehasis Mukherjee,et al.  Human activity recognition in RGB-D videos by dynamic images , 2018, Multimedia Tools and Applications.

[43]  Jürgen Dickmann,et al.  Semantic Segmentation on Radar Point Clouds , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[44]  Thomas D. C. Little,et al.  Refining light-based positioning for indoor smart spaces , 2018, SmartObjects@MobiHoc.

[45]  Antonio Torralba,et al.  Through-Wall Human Pose Estimation Using Radio Signals , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[46]  Jiangchuan Liu,et al.  TagFree Activity Identification with RFIDs , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[47]  Richard F. Sesek,et al.  Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals , 2018, Hum. Factors.

[48]  Xingshe Zhou,et al.  C-FMCW Based Contactless Respiration Detection Using Acoustic Signal , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[49]  Xiaonan Guo,et al.  WiFi-Enabled Smart Human Dynamics Monitoring , 2017, SenSys.

[50]  Karthikeyan Sundaresan,et al.  RIO: A Pervasive RFID-based Touch Gesture Interface , 2017, MobiCom.

[51]  Parth H. Pathak,et al.  Monitoring vital signs using millimeter wave , 2016, MobiHoc.

[52]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[53]  Wei Wang,et al.  Keystroke Recognition Using WiFi Signals , 2015, MobiCom.

[54]  Xinyu Zhang,et al.  mTrack: High-Precision Passive Tracking Using Millimeter Wave Radios , 2015, MobiCom.

[55]  Bernard Ghanem,et al.  ActivityNet: A large-scale video benchmark for human activity understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Rob Miller,et al.  Smart Homes that Monitor Breathing and Heart Rate , 2015, CHI.

[57]  Kaishun Wu,et al.  We Can Hear You with Wi-Fi! , 2014, IEEE Transactions on Mobile Computing.

[58]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[59]  Gaddi Blumrosen,et al.  Noncontact Wideband Sonar for Human Activity Detection and Classification , 2014, IEEE Sensors Journal.

[60]  Shyamnath Gollakota,et al.  Bringing Gesture Recognition to All Devices , 2014, NSDI.

[61]  Fadel Adib,et al.  See through walls with WiFi! , 2013, SIGCOMM.

[62]  Yutaka Hata,et al.  Wearable Human Activity Recognition by Electrocardiograph and Accelerometer , 2013, 2013 IEEE 43rd International Symposium on Multiple-Valued Logic.

[63]  Mark Billinghurst,et al.  Seamless interaction in space , 2011, OZCHI.

[64]  O. Pujol,et al.  Human Activity Recognition from Accelerometer Data Using a Wearable Device , 2011, IbPRIA.

[65]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[66]  Bir Bhanu,et al.  Human Activity Recognition in Thermal Infrared Imagery , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[67]  Mohan M. Trivedi,et al.  Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[68]  M. Harville,et al.  Fast, integrated person tracking and activity recognition with plan-view templates from a single stereo camera , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[69]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[70]  Sanjib Sur,et al.  MilliPCD , 2022, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[71]  Luis A. Leiva,et al.  Pantomime , 2021, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[72]  Mayank Goel,et al.  IMU2Doppler: Cross-Modal Domain Adaptation for Doppler-based Activity Recognition Using IMU Data , 2021, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[73]  Cesar Iovescu The fundamentals of millimeter wave radar sensors (Rev. A) , 2021 .

[74]  Jim Torresen,et al.  Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning , 2021, IEEE Access.

[75]  Kevin Bouchard,et al.  Activity Recognition in Smart Homes using UWB Radars , 2020, ANT/EDI40.

[76]  Leonardo Bonanni,et al.  CounterIntelligence: Augmented Reality Kitchen , 2005 .

[77]  Ramon Nitzberg,et al.  Constant-False-Alarm-Rate Signal Processors for Several Types of Interference , 1972, IEEE Transactions on Aerospace and Electronic Systems.