Machine Learning Algorithms for Posture Identification of Obstructive Sleep Apnea Patients using IoT Solutions

Sleep apnea is a serious sleep disorder in which individuals breathing repeatedly stops and starts. Even after the continuous sleep of 6-8 hours, person feels fatigue and tiredness. This disorder turns even more serious if the person has history of heart problem. The symptoms of sleep apnea are snoring, fatigue, and somnolence, while the main types of sleep apnea are: obstructive sleep apnea, central sleep apnea and complex sleep apnea. Among these obstructive sleep apnea (OSA) is most frequent and can be treated by correct sleeping posture. Research has proved that a change in in-bed posture plays vital role for OSA. In this research we used data of two separate experiments from thirteen healthy subjects in different sleeping postures using two commercially available internet of thing (IoT) based pressure mats. On this data we employed machine learning based supervised learning algorithms for posture identification. This monitoring system may help sleep apnea patients and caregivers to be alerted of improper postures in timely manner and helps in identifying sleeping style of each patient.