An image-based system for monitoring pregnant women's sleep posture

Pregnancy is an important period for both mother and child, and the quality of sleep plays a vital role in ensuring their health a sleep position monitoring system designed to aid pregnant women in maintaining a healthy posture during sleep, crucial for maternal and foetal well-being. Leveraging computer vision and machine learning techniques, the system detects four sleep positions based on shoulder coordinates obtained from the MediaPipe pose model. Employing SVM and random forest algorithms, two models are developed to enhance accuracy, and their results are averaged for robust sleep position identification. Upon detecting prolonged undesired positions, the system triggers a call via a GSM modem for timely intervention. Offering a non-invasive, automated, and cost-effective solution, this system facilitates proactive monitoring of pregnant women's sleep posture, potentially preventing harm to the foetus. By promoting healthy sleep habits throughout pregnancy, it aims to improve maternal and foetal health outcomes.