Abstract | ||
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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. |
Year | DOI | Venue |
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2022 | 10.1109/JSYST.2022.3140546 | IEEE SYSTEMS JOURNAL |
Keywords | DocType | Volume |
Point cloud compression, Radar, Doppler radar, Radar imaging, Noise measurement, Cameras, Activity recognition, Machine learning, millimeter-wave (mmWave) radar, noninvasive human activity recognition (HAR), smart home | Journal | 16 |
Issue | ISSN | Citations |
2 | 1932-8184 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
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Chengxi Yu | 1 | 0 | 0.34 |
zhezhuang | 2 | 37 | 7.81 |
Kun Yan | 3 | 0 | 0.34 |
Ying-Ren Chien | 4 | 39 | 10.48 |
Shih-Hau Fang | 5 | 0 | 0.68 |
Hsiao-chun Wu | 6 | 959 | 97.99 |