Argosleep: Monitoring Sleep Posture from Commodity Millimeter-Wave Devices

Abstract

We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.