Real-time Sleeping Posture Recognition For Smart Hospital Beds
Ngoc Phu Doan, Nguyen Duc Anh Pham, Hung Pham, Huu Trung Nguyen, Thùy Anh Nguyễn, Huy Hoang Nguyen
Abstract
Unsuitable sleeping positions are the important contributors that result in bad sleep quality and even serious long-term consequences. Many studies emphasize that pressure sensor-based solutions are effective on the in-bed postures assessment in both home and hospital environments. Surprisingly, none of the studies considers Edge computing-based solution for body pose recognition on smart hospital beds. In this paper, we propose the development of a real-time sleeping posture recognition algorithm which is a combination of a preprocessing technique and an EfficientNet B0 based classifier with an AM-Softmax loss function. Experimental results confirm that our proposed method can gain the accuracy of over 99 % in 5-fold as well as 10-fold cross-validation and 95.32% in the Leave-One-Subject-Out (LOSO) validation for 17 sleeping postures, which greatly surpasses the previous method in the same task. Furthermore, our solution can satisfy the real-time requirement for various data sampling rates when deploying on the Edge computing-based smart hospital bed.