基于树莓派并采用人工智能物联网技术的睡眠姿势识别系统。
Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique.
作者信息
Chen Pei-Jarn, Hu Tian-Hao, Wang Ming-Shyan
机构信息
Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan City 71005, Taiwan.
出版信息
Healthcare (Basel). 2022 Mar 11;10(3):513. doi: 10.3390/healthcare10030513.
The relationship between sleep posture and sleep quality has been studied comprehensively. Over 70% of chronic diseases are highly correlated with sleep problems. However, sleep posture monitoring requires professional devices and trained nursing staff in a medical center. This paper proposes a contactless sleep-monitoring Internet of Things (IoT) system that is equipped with a Raspberry Pi 4 Model B; radio-frequency identification (RFID) tags are embedded in bed sheets as part of a low-cost and low-power microsystem. Random forest classification (RFC) is used to recognize sleep postures, which are then uploaded to the server database via Wi-Fi and displayed on a terminal. The experimental results obtained using RFC were compared to those obtained via the support vector machine (SVM) method and the multilayer perceptron (MLP) algorithm to validate the performance of the proposed system. The developed system can be also applied for sleep self-management at home and wireless sleep monitoring in medical wards.
睡眠姿势与睡眠质量之间的关系已得到全面研究。超过70%的慢性病与睡眠问题高度相关。然而，睡眠姿势监测需要专业设备和医疗中心经过培训的护理人员。本文提出了一种非接触式睡眠监测物联网（IoT）系统，该系统配备了树莓派4 B型；射频识别（RFID）标签嵌入床单中，作为低成本、低功耗微系统的一部分。随机森林分类（RFC）用于识别睡眠姿势，然后通过Wi-Fi上传到服务器数据库并显示在终端上。将使用RFC获得的实验结果与通过支持向量机（SVM）方法和多层感知器（MLP）算法获得的结果进行比较，以验证所提出系统的性能。所开发的系统还可应用于家庭睡眠自我管理和医疗病房的无线睡眠监测。
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