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Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods.
Li et al. (Wed,) studied this question.