2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSS-9.1
Paper Title m-Activity: ACCURATE AND REAL-TIME HUMAN ACTIVITY RECOGNITION VIA MILLIMETER WAVE RADAR
Authors Yuheng Wang, Haipeng Liu, Kening Cui, Anfu Zhou, Wensheng Li, Huadong Ma, Beijing University of Posts and Telecommunications, China
SessionSS-9: Contactless and Wireless Sensing for Smart Environments
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Special Sessions: Contactless and Wireless Sensing for Smart Environments
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Natural human activity recognition (HAR) via millimeter wave (mmWave) sensing is a key to the human-computer interaction (HCI), e.g., activity assistance and living state monitoring. Prior work has shown the feasibility of HAR by utilizing mmWave radar, but it falls short of two real-world issues: poor recognition accuracy in the noisy environment and unable to give real-time response due to long latency. In this paper, we propose m-Activity, which can realize HAR while reducing noise caused by environmental multi-path effects, and operate fluently at runtime. m-Activity first distills the human orientated movements from the noisy background environment and then classify the movements using a customdesigned lightweight neural network called HARnet. To drive the above methods, we propose a simple but efficient response mechanism to enable real-time recognition. We prototype mActivity on a commodity mmWave radar chip and evaluate its recognition performance over 5 pre-defined human activities within the detection range of 3m, which results in off-line accuracy of 93.25%, and real-time accuracy of 91.52%. Furthermore, we validate m-Activity’s ability under a complex real-world scenario, i.e., fitness center, which is full of severe multi-path effects caused by various strong metal reflectors.