PosMonitor: Fine-Grained Sleep Posture Recognition With mmWave Radar

Abstract

Sleep posture recognition is practically important in various scenarios such as sleep healthcare, bedridden patient care, and chronic disease diagnosis. With concerns of user privacy preserving, we prefer the wireless sensing methods to computer vision methods when dealing with sleep posture recognition. However, the existing wireless sensing methods suffer from at least one of the following major limitations: (i) difficult to deploy in practice; (ii) few posture categories; (iii) insufficient accuracy; (iv) poor generalization ability. In this paper, we use commercial-off-the-shelf (COTS) mmWave radar to implement a sleep posture recognition system called PosMonitor. When designing the PosMonitor system, we need to address the following challenging issues. First, we propose an angle purification method based on multi-frame joint analysis to alleviate the sparsity and instability of the point cloud. Then, we endow the point cloud with respiratory features to enhance its representation of the sleep posture. Further, to make the system applicable to different users, we extract relative respiratory features by normalization to overcome individual differences. Extensive experimental results show that our PosMonitor system can achieve 98% accuracy on average in recognizing 6 typical sleep postures and has good reliability across different conditions.