Paper Info
Title
m-Activity: ACCURATE AND REAL-TIME HUMAN ACTIVITY RECOGNITION VIA MILLIMETERWAVE RADAR
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.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9414686
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
human activity recognition, mmWave, noise reduction, DBSCAN, neural network
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yuheng Wang1185.93
Haipeng Liu2115.69
Kening Cui300.68
Anfu Zhou416018.60
Wensheng Li5195.76
Huadong Ma62020179.93