Human Sleep Posture Recognition Method Based on Interactive Learning of Ultra-Long Short-Term Information

The sleep posture recognition and tracking using radar sensor is one of the most important techniques because of its advantages of stable performance, privacy protection, and so on. However, radar echo signals are highly sensitive to different humans, resulting in a week generalization ability. To solve this problem, we propose a human sleep posture recognition method based on interactive learning of ultra-long short-term information using millimeter-wave radar. The motivation of the proposed method using the interactive learning strategy lies that it is easy to obtain stable information with an ultra-long-term observation. Specifically, we first conduct short-term human state separation sequentially by body movement index estimation, big movement detection, range–Doppler map calculation, and lateral and nonlateral rough identification. This could separate the three human states of big movement, lateral posture, and other recumbency, and prepare for finer posture recognition. Second, we sort the three sleep postures of supine, side, and prone and extract personalized features with an ultra-long-term observation to enhance the robustness. Finally, combined with the extracted personalized ultra-long-term features, we achieve recognition of three sleep postures and seven sleep postural conversions (SPCs) with a short-term observation for real-time judgment. The experimental results show that the proposed method has low computational complexity and can achieve an average accuracy of 91% in three sleep postures' recognition and 83.7% in seven SPCs' classification.