通过睡眠姿势分析推进心理健康预测:一种堆叠集合学习方法

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-07-01 DOI:10.1007/s12652-024-04827-6
Muhammad Nouman, Sui Yang Khoo, M. A. Parvez Mahmud, Abbas Z. Kouzani
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引用次数: 0

摘要

睡眠姿势与睡眠质量密切相关,可以帮助了解个人的健康状况。这种相关性可能有助于早期发现抑郁症和焦虑症等精神疾病。目前的研究重点是在床单中嵌入压力传感器,在受试者胸部安装加速度计,以及在卧室中安装摄像头以监测睡眠姿势。然而,这些解决方案牺牲了用户的睡眠舒适度或隐私。本研究探讨了使用非接触式超宽带(UWB)传感器进行睡姿监测的有效性。我们采用了一个 UWB 数据集,该数据集由 12 名志愿者的睡眠测量数据组成。我们引入了一种堆叠集合学习方法来监测睡眠姿势转换,这种方法构成了两个层次的学习。在基础学习层面,在训练数据集上训练了六个迁移学习模型(VGG16、ResNet50V2、MobileNet50V2、DenseNet121、VGG19 和 ResNet101V2),用于初始预测。然后,采用逻辑回归作为元学习器,在基础学习器的预测基础上进行训练,以获得最终的睡眠姿势转换。此外,还提出了一种睡眠姿势监测算法,可以准确统计总的睡眠姿势转换。通过大量实验,睡眠姿势转换分类的最高准确率达到了 86.7%。此外,还采用了时间序列数据增强技术,使准确率提高了 13%。本文提出的保护隐私的睡眠监测解决方案有望应用于心理健康研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Advancing mental health predictions through sleep posture analysis: a stacking ensemble learning approach

Sleep posture is closely related to sleep quality, and can offer insights into an individual’s health. This correlation can potentially aid in the early detection of mental health disorders such as depression and anxiety. Current research focuses on embedding pressure sensors in bedsheets, attaching accelerometers on a subject’s chest, and installing cameras in bedrooms for sleep posture monitoring. However, such solutions sacrifice either the user's sleep comfort or privacy. This study explores the effectiveness of using contactless ultra-wideband (UWB) sensors for sleep posture monitoring. We employed a UWB dataset that is composed of the measurements from 12 volunteers during sleep. A stacking ensemble learning method is introduced for the monitoring of sleep postural transitions, which constitute two levels of learning. At the base-learner level, six transfer learning models (VGG16, ResNet50V2, MobileNet50V2, DenseNet121, VGG19, and ResNet101V2) are trained on the training dataset for initial predictions. Then, the logistic regression is employed as a meta-learner which is trained on the predictions gained from the base-learner to obtain final sleep postural transitions. In addition, a sleep posture monitoring algorithm is presented that can give accurate statistics of total sleep postural transitions. Extensive experiments are conducted, achieving the highest accuracy rate of 86.7% for the classification of sleep postural transitions. Moreover, time-series data augmentation is employed, which improves the accuracy by 13%. The privacy-preserving sleep monitoring solution presented in this paper holds promise for applications in mental health research.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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