Human Sleep Posture Recognition Exploiting Spatial Morphology and Local Micro-Doppler Characteristics Using MIMO Radar

Abstract

Radar sleep posture recognition has received much attention due to its advantages, such as privacy preservation and freedom from quilt occlusion. However, radar echo signals are highly sensitive to different humans, which leads to poor generalization ability. To address this problem, we propose a human sleep posture recognition method exploiting spatial morphology and local micro-Doppler characteristics using multiple-input multiple-output (MIMO) radar. We first perform multidimensional imaging of human targets. Then, we extract the spatial morphology features of human sleep posture and local micro-Doppler features of human sleep life activities and utilize the Bagged Trees classifier to complete the classification of left lateral, right lateral, and nonlateral. For the nonlateral posture, we further extract new feature sequences from multidimensional imaging and combine them with a long and short-term memory (LSTM) network to complete the classification of supine and prone posture. Experimental results show that the proposed method achieves an average accuracy of 97.6% for recognizing the four sleep postures, has good reliability across individuals, and performs efficiently in most types of beds with single human at different sleeping positions. Finally, we have deployed the proposed method in a designed real-time system, validating its low computation complexity and the feasibility of edge applications.