Sleep Posture and Snoring Identification Using Deep Learning Techniques

抄録

Sleep is a key mechanism for the body to recover and regulate. The quality of sleep is closely related to health. Sleeping posture and snoring conditions significantly affect sleep quality. Insufficient and irregular sleep can lead to serious long-term health issues. This study explores the relationship between Obstructive Sleep Apnea (OSA) and sleeping posture. Existing detection methods are complex and costly, making them difficult to apply widely. Therefore, this research develops an innovative sleep detection system based on deep learning techniques with image and sound recognition. The system utilizes home cameras and a self-designed mobile application to monitor personal sleeping posture and real-time snoring conditions. We aimed to enhance the feasibility of home health monitoring and increase opportunities for early prevention and personalized sleep management.