CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection.

Sleep posture, a vital aspect of sleep wellness, has become a crucial focus in sleep medicine. Studies show that supine posture can lead to a higher occurrence of obstructive sleep apnea, while lateral posture might reduce it. For bedridden patients, frequent posture changes are essential to prevent ulcers and bedsores, highlighting the importance of monitoring sleep posture. This paper introduces CHMMConvScaleNet, a novel method for sleep posture recognition using pressure signals from limited piezoelectric ceramic sensors. It employs a Movement Artifact and Rollover Identification (MARI) module to detect critical rollover events and extracts multi-scale spatiotemporal features using six sub-convolution networks with different-length adjacent segments. To optimize performance, a Continuous Hidden Markov Model (CHMM) with rollover features is presented. We collected continuous real sleep data from 22 participants, yielding a total of 8583 samples from a 32-sensor array. Experiments show that CHMMConvScaleNet achieves a recall of 92.91%, precision of 91.87%, and accuracy of 93.41%, comparable to state-of-the-art methods that require ten times more sensors to achieve a slightly improved accuracy of 96.90% on non-continuous datasets. Thus, CHMMConvScaleNet demonstrates potential for home sleep monitoring using portable devices.