A Sleep Monitoring System Based on Audio, Video and Depth Information

TL;DR

This paper presents a noninvasive sleep monitoring system that uses audio, video, and depth sensors to detect sleep disturbances through event classification, providing a reliable way to evaluate sleep quality at home.

Contribution

The study introduces a multi-sensor sleep monitoring system with event-based detection for motion, light, and noise disturbances in home environments, which is novel in integrating depth, color, and audio data.

Findings

System reliably detects sleep disturbance events

Effective in low-light home conditions

Validates accuracy through experimental testing

Abstract

For quantitative evaluation of sleep disturbances, a noninvasive monitoring system is developed by introducing an event-based method. We observe sleeping in home context and classify the sleep disturbances into three types of events: motion events, light-on/off events and noise events. A device with an infrared depth sensor, a RGB camera, and a four-microphone array is used in sleep monitoring in an environment with barely light sources. One background model is established in depth signals for measuring magnitude of movements. Because depth signals cannot observe lighting changes, another background model is established in color images for measuring magnitude of lighting effects. An event detection algorithm is used to detect occurrences of events from the processed data of the three types of sensors. The system was tested in sleep condition and the experiment result validates the system reliability.