m-Activity: Accurate and Real-Time Human Activity Recognition Via Millimeter Wave Radar
Yuheng Wang, Haipeng Liu, Kening Cui, Anfu Zhou, Wensheng Li, Huadong Ma
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 custom-designed 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 m-Activity 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.
BibTeX
@inproceedings{icassp2021_mactivityaccurat,
title = {m-Activity: Accurate and Real-Time Human Activity Recognition Via Millimeter Wave Radar},
author = {Yuheng Wang and Haipeng Liu and Kening Cui and Anfu Zhou and Wensheng Li and Huadong Ma},
booktitle = {ICASSP 2021},
year = {2021}
}