Sleep posture recognition based on machine learning: A systematic review

Background: In recent years, the application of artificial intelligence in the field of sleep medicine has rapidly emerged. One of the main concerns of many researchers is the recognition of sleep positions, which enables efficient monitoring of changes in sleeping posture for precise and intelligent adjustment. In sleep monitoring, machine learning is able to analyze the raw data collected and optimizes the algorithm in real-time to recognize the sleeping position of the human body during sleep. Methodology: A detailed search of relevant databases was conducted through a systematic search process, and we reviewed research published since 2017, focusing on 27 articles on sleep recognition. Results: Through the analysis and study of these articles, we propose several determinants that objectively affect sleeping posture recognition, including the acquisition of sleep posture data, data pre-processing, recognition algorithms, and validation analysis. Moreover, we analyze the categories of sleeping postures adapted to different body types. Conclusion: A systematic evaluation combining the above determinants provides solutions for system design and rational selection of recognition algorithms for sleep posture recognition, and it is necessary to regularize and standardize existing machine learning algorithms before they can be incorporated into clinical monitoring of sleep.