Noninvasive Human Activity Recognition Using Millimeter-Wave Radar
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.
Publication Source (Journal or Book title)
IEEE SYSTEMS JOURNAL
Yu, C., Xu, Z., Yan, K., Chien, Y., Fang, S., & Wu, H. (2022). Noninvasive Human Activity Recognition Using Millimeter-Wave Radar. IEEE SYSTEMS JOURNAL https://doi.org/10.1109/JSYST.2022.3140546