Non-invasive Sleep Posture Analysis and Anomaly Detection Algorithm Based on Computer Vision

Sleep posture plays a vital role in sleep and has an important impact on sleep quality and health. However, traditional sleep monitoring methods usually need to wear equipment or get body information through bed sensors, which is inconvenient and invasive, limiting its practical application. The purpose of this study is to develop a non-invasive sleep posture analysis and anomaly detection algorithm based on computer vision to improve the convenience and feasibility of sleep monitoring. Firstly, a sleep posture recognition model is constructed by using deep learning technology, which can automatically recognize different postures during sleep, including supine, lateral, prone, and so on. Subsequently, we introduced an abnormality detection module to identify abnormal postures during sleep, such as snoring and abnormal turn-over frequency. Experimental results show that our algorithm has excellent performance in a variety of actual sleep scenarios, accurately identifying different postures and detecting abnormal situations in time. Providing support for early diagnosis and intervention of sleep disorders is expected to improve people's quality of life and health.