CHMMConvScaleNet:一个混合卷积神经网络和连续隐藏的马尔科夫模型,具有用于睡眠姿势检测的多尺度功能
Dikun Hu1, Weidong Gao2, Kai Keng Ang3,4
1School of Information and Communication Engineering, Institute for Beijing University of Posts and Telecommunications (BUPT), Beijing, 100876, China.
Scientific reports
|April 9, 2025
概括
这项研究引入了CHMMConvScaleNet,这是一种使用少数传感器识别睡眠姿势的新方法. 它准确地监测睡眠位置,显示出便携式家庭睡眠健康设备的前景.
科学领域:
- 睡眠医学 睡眠医学
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
背景情况:
- 睡眠姿势对睡眠健康至关重要,对阻塞性睡眠呼吸暂停等疾病有影响.
- 监测睡眠姿势对于卧床患者来说至关重要,以防止压力.
- 目前用于睡眠姿势识别的方法通常需要许多传感器.
研究的目的:
- 开发和验证一种新的方法,CHMMConvScaleNet,用于准确识别睡眠姿势.
- 使用有限的压电陶传感器评估CHMMConvScaleNet的有效性.
- 为了证明CHMMConvScaleNet在家用便携式睡眠监测中的潜力.
主要方法:
- CHMMConvScaleNet利用来自有限传感器阵列的压力信号.
- 一个运动文物和翻转识别 (MARI) 模块检测翻转事件.
- 使用子卷积网络提取多尺度的时空特征,并使用连续隐藏马尔科夫模型 (CHMM) 进行优化.
主要成果:
- CHMMConvScaleNet实现了高性能指标:92.91%的回忆,91.87%的精度和93.41%的准确性.
- 该方法的性能与使用显著更少的传感器的最先进技术相提并论.
- 通过使用32个传感器阵列从22名参与者收集数据,产生8583个样本.
结论:
- CHMMConvScaleNet提供了一种有效的解决方案,用于使用最小的传感器识别睡眠姿势.
- 该方法显示出开发用于家用睡眠监测的便携式设备的巨大潜力.
- 这种方法可以有助于改善睡眠健康和患者护理,特别是那些有睡眠呼吸暂停或压力的风险的人.
相关概念视频
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Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
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