Automated Sleep Period Estimation in Wearable Multi-sensor Systems

Abstract

Sleep period determination is essential to accurate sleep quality analysis. In this paper, we propose an automated algorithm for wearable multi-sensor systems to precisely estimate the sleep period. It leverages the information of accelerometer, and vital signs such as heart rate and breathing rate. Compared to the sleep periods determined by the clinical diagnosing-grade equipment, our algorithm achieves average time differences of 9.0 and 10.4 minutes for healthy subjects and clinical patients, respectively.