Radar-based human activity recognition with adaptive thresholding towards resource constrained platforms

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

Automated summary

To address the problem of limited computational resources on embedded platforms, researchers propose an adaptive magnitude thresholding approach to highlight the region of interest in multi-domain micro-Doppler signatures. This approach extracts salient features with simplicity and low computational cost. Experimental results show that the proposed approach achieves an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods with significant reductions in training time, memory footprint, and inference time. These results are crucial for enabling embedded platform deployment.