Hu Dikun, Gao Weidong, Ang Kai Keng, Hu Mengjiao, Huang Rong, Chuai Gang, Li Xiaoyan
School of Information and Communication Engineering, Institute for Beijing University of Posts and Telecommunications (BUPT), Beijing, 100876, China.
Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore, 138632, Singapore.
Sci Rep. 2025 Apr 10;15(1):12206. doi: 10.1038/s41598-025-93541-0.
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.
睡眠姿势是睡眠健康的一个重要方面，已成为睡眠医学的关键关注点。研究表明，仰卧姿势会导致阻塞性睡眠呼吸暂停的发生率更高，而侧卧姿势可能会降低其发生率。对于卧床患者来说，频繁改变姿势对于预防溃疡和褥疮至关重要，这凸显了监测睡眠姿势的重要性。本文介绍了CHMMConvScaleNet，这是一种利用有限压电陶瓷传感器的压力信号进行睡眠姿势识别的新方法。它采用运动伪影和翻身识别（MARI）模块来检测关键的翻身事件，并使用六个具有不同长度相邻段的子卷积网络提取多尺度时空特征。为了优化性能，提出了一种具有翻身特征的连续隐马尔可夫模型（CHMM）。我们从22名参与者那里收集了连续的真实睡眠数据，从一个32传感器阵列中总共获得了8583个样本。实验表明，CHMMConvScaleNet的召回率为92.91%，精确率为91.87%，准确率为93.41%，与那些在非连续数据集上需要十倍数量的传感器才能实现略高的96.90%准确率的现有方法相当。因此，CHMMConvScaleNet展示了使用便携式设备进行家庭睡眠监测的潜力。
