Real-Time and Cost-Effective Smart Mat System Based on Frequency Channel Selection for Sleep Posture Recognition in IoMT

• Published in: IEEE internet of things journal Vol. 9; no. 21; pp. 21421 - 21431
• Main Authors: Diao, Haikang, Chen, Chen, Liu, Xiangyu, Yuan, Wei, Amara, Amara, Tamura, Toshiyo, Lo, Benny, Fan, Jiahao, Meng, Long, Pun, Sio Hang, Zhang, Yuan-Ting, Chen, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Classification algorithms; Complexity theory; Data acquisition; Embedded systems; Flexible components; Frequency channel selection; Internet of medical things; Internet of Medical Things (IoMT); Low cost; Medical research; Medical services; Microprocessors; Real time; Real-time systems; Recognition; Reliability analysis; Sensor arrays; Sleep; Sleep apnea; sleep posture recognition; smart mat system; Ulcers
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3181599

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system's ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.