Li Y.-YWang S.-JYI-PING HUNG2023-06-092023-06-09202214248220https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126070704&doi=10.3390%2fs22052014&partnerID=40&md5=9bb1ff8592cadcdafa718fce7701e6d6https://scholars.lib.ntu.edu.tw/handle/123456789/632256Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and pose classification during sleep. The proposed system has two major advantages: first, it detects and predicts upper-body pose and head pose simultaneously during sleep, and second, it is a contact-free home security camera-based monitoring system that can work on remote subjects, as it uses images captured by a home security camera. In addition, a synopsis of sleep postures is provided for analysis and diagnosis of sleep patterns. Experimental results show that our multi-task model achieves an average of 92.5% accuracy on challenging datasets, yields the best performance compared to the other methods, and obtains 91.7% accuracy on the real-life overnight sleep data. The proposed system can be applied reliably to extensive public sleep data with various covering conditions and is robust to real-life overnight sleep data. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Deep multi-task learning; Head and upper-body detection; Head and upper-body pose classification; Sleep monitoring; Sleep posture[SDGs]SDG3Cameras; Deep learning; Image recognition; Patient monitoring; Body pose; Deep multi-task learning; Head and upper-body detection; Head and upper-body pose classification; Head pose; Pose classifications; Sleep monitoring; Sleep posture; Upper bodies; Sleep research; body position; human; sleep; Humans; Posture; Sleepjournal article10.3390/s22052014352711622-s2.0-85126070704