دورية أكاديمية
Noninvasive Human Activity Recognition Using Millimeter-Wave Radar
| العنوان: | Noninvasive Human Activity Recognition Using Millimeter-Wave Radar |
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| المؤلفون: | Yu, Chengxi, Xu, Zhezhuang, Yan, Kun, Chien, Ying-Ren, Fang, Shih-Hau, Wu, Hsiao-Chun |
| المصدر: | Faculty Publications |
| بيانات النشر: | LSU Digital Commons |
| سنة النشر: | 2022 |
| المجموعة: | LSU Digital Commons (Louisiana State University) |
| مصطلحات موضوعية: | 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, NETWORKS, ALGORITHMS, FALLS, Computer Sciences, Engineering, Operations Research, Systems Engineering and Industrial Engineering |
| الوصف: | 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. |
| نوع الوثيقة: | text |
| اللغة: | unknown |
| العلاقة: | https://digitalcommons.lsu.edu/eecs_pubs/9Test; https://ieeexplore.ieee.org/document/9691471Test/ |
| DOI: | 10.1109/JSYST.2022.3140546 |
| الإتاحة: | https://doi.org/10.1109/JSYST.2022.3140546Test https://digitalcommons.lsu.edu/eecs_pubs/9Test https://ieeexplore.ieee.org/document/9691471Test/ |
| رقم الانضمام: | edsbas.27E19519 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/JSYST.2022.3140546 |
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