Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan.
Sensors (Basel). 2022 Nov 1;22(21):8409. doi: 10.3390/s22218409.
Doppler-radar-based continuous human motion recognition recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of continuous human motion recognition (CHMR) using mmWave FMCW Radar are not considered the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS) problem, called as CHMR-HS, by using the modified Transformer-based learning model. As far as we know, this is the first result in the literature to investigate the continuous HMR with the high similarity. To obtain the clear FMCW radar images, the background and target signals of the detected human are separated through the background denoising and the target extraction algorithms. To investigate the effects of the spectral-temporal multi-features with different dimensions, Doppler, range, and angle signatures are extracted as the 2D features and range-Doppler-time and range-angle-time signatures are extracted as the 3D features. The 2D/3D features are trained into the adjusted Transformer-encoder model to distinguish the difference of the high-similarity human motions. The conventional Transformer-decoder model is also re-designed to be Transformer-sequential-decoder model such that Transformer-sequential-decoder model can successfully recognize the continuous human motions with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme are 95.2% and 94.5% if using 3D and 2D features, the simulation results also illustrates that our CHMR-HS scheme has advantages over existing CHMR schemes.
基于多普勒雷达的连续人体运动识别最近引起了广泛关注，这是隐私和人身安全的理想选择。使用毫米波 FMCW 雷达进行连续人体运动识别 (CHMR) 的现有结果并未考虑具有高相似度的连续人体运动问题。在本文中，我们提出了一种新的 CHMR 算法，称为 CHMR-HS，考虑了高相似度 (HS) 问题，该算法使用了改进的基于 Transformer 的学习模型。据我们所知，这是首次研究具有高相似度的连续 HMR 的文献结果。为了获得清晰的 FMCW 雷达图像，通过背景去噪和目标提取算法将检测到的人体的背景和目标信号分离。为了研究不同维度的时频多特征的影响，提取多普勒、距离和角度特征作为 2D 特征，提取距离-多普勒-时间和距离-角度-时间特征作为 3D 特征。将 2D/3D 特征输入到调整后的 Transformer-encoder 模型中，以区分高相似度人体运动的差异。还重新设计了传统的 Transformer-decoder 模型，使其成为 Transformer-sequential-decoder 模型，以便 Transformer-sequential-decoder 模型能够成功识别具有高相似度的连续人体运动。实验结果表明，如果使用 3D 和 2D 特征，我们提出的 CHMR-HS 方案的准确率分别为 95.2%和 94.5%，仿真结果也表明我们的 CHMR-HS 方案优于现有 CHMR 方案。
