Noninvasive Human Activity Recognition Using Millimeter-Wave Radar

• Published in: IEEE systems journal Vol. 16; no. 2; pp. 1 - 12
• Main Authors: Yu, Chengxi, Xu, Zhezhuang, Yan, Kun, Chien, Ying-Ren, Fang, Shih-Hau, Wu, Hsiao-Chun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Artificial neural networks; Cameras; Doppler radar; Fall detection; Human activity recognition; Machine learning; Millimeter waves; millimeter-wave (mmWave) radar; Noise measurement; noninvasive human activity recognition (HAR); Point cloud compression; Radar; Radar imaging; Shielding; smart home
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2022.3140546

The millimeter-wave (mmWave) radar technology has attracted significant attention because it is susceptible to environmental lighting, wall shielding, and privacy concern. This article proposes a novel noninvasive human activity recognition system using a mmWave radar. The proposed framework first transforms mmWave signals into point clouds. Generally speaking, it consists of four major components: denosing, enhanced voxelization, data augmentation, and dual-view machine learning to lead to accurate and efficient human activity recognition. The proposed new methodology considers the spatial-temporal point clouds in physical environments through a modified voxelization approach, enriches the sparse data based on the symmetry property of radar rotations, and learns the activity using a dual-view convolutional neural network. To evaluate the performance of the proposed learning models, a dataset involving seven different activities has been established using a mmWave radar platform. The experimental results have demonstrated that the proposed system can achieve 97.61% and 98% accuracies during the tests of fall detection and activity classification, respectively. In comparison, the proposed scheme greatly outperforms four other conventional machine learning schemes in terms of the overall accuracy.