Machine Learning Based Sleep-Status Discrimination Using a Motion Sensing Mattress

This paper presents a novel sleep-status discrimination system by adopting a motion sensing mattress which detects the user's activities on bed including the movement of head, chest, legs and feet. Unlike traditional methods like Polysomnography (PSG) which needs electrical equipment connected to users, or like wrist actigraphy which needs to be contact to users, the proposed system distinguishes sleep states in a non-conscious and non-contact way. The proposed system is built by a machine learning technique in the offline stage, and distinguishes sleep states in the online stage by using our designed sleep-status discrimination algorithm. The experimental results illustrate that the proposed method efficiently distinguishes sleep statuses without using a wearable device contact to body or using PSG diagnosis undertaken at hospitals.