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使用具有最优分布三轴加速度计阵列和平行卷积时空网络的智能床垫进行无干扰睡眠姿势检测

Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network.

作者信息

Liu Zhuofu, Li Gaohan, Wang Chuanyi, Cascioli Vincenzo, McCarthy Peter W

机构信息

The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.

Murdoch University Chiropractic Clinic, Murdoch University, Murdoch 6150, Australia.

出版信息

Sensors (Basel). 2025 Jun 8;25(12):3609. doi: 10.3390/s25123609.

Abstract

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual's sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea.

摘要

睡眠姿势检测是睡眠质量评估和健康监测中一个潜在的重要组成部分。准确识别睡眠姿势可以为个人的睡眠模式、舒适度以及潜在健康风险提供有价值的见解。例如,不当的睡眠姿势可能导致肌肉骨骼问题、呼吸紊乱,甚至会使睡眠呼吸暂停等状况恶化。此外,对于长期卧床的患者,持续监测睡眠姿势对于预防压疮和其他并发症至关重要。传统的睡眠姿势检测方法存在若干局限性:可穿戴传感器会干扰自然睡眠并引起不适,基于摄像头的系统存在隐私问题且对环境条件敏感,而压力感应垫通常复杂且成本高昂。为了解决这些问题,我们开发了一种低成本的非接触式睡眠姿势检测系统。我们的系统具有八个优化分布的三轴加速度计,提供了一个舒适且非接触式的前端数据采集单元。对于睡眠姿势分类,我们采用了一种改进的密度峰值聚类算法,该算法融入了K近邻机制。此外,我们通过整合卷积神经网络(CNN)、长短期记忆网络(LSTM)和双向长短期记忆网络(Bi-LSTM)模块构建了一个并行卷积时空网络(PCSN)。实验结果表明,PCSN能够准确区分六种睡眠姿势:俯卧、仰卧、左侧卧、左侧胎儿式、右侧卧和右侧胎儿式。平均准确率为98.42%,优于大多数最先进的深度学习模型。PCSN在所有指标上均取得了最高分:精确率为98.64%,召回率为98.18%,F1分数为98.10%。所提出的系统在包括睡眠研究以及预防压疮和睡眠呼吸暂停等疾病在内的各种应用中显示出了巨大的潜力。

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