Estimation of sleeping posture for M-Health by a wearable tri-axis accelerometer

Sensor technology has been developed for measuring daily activity. Measurement instruments of all kinds have continued to become much smaller, consume less power, and increase in resolution and/or sensitivity. This study proposed a way to detect sleeping postures from data acquired using a tri-axis accelerometer strapped to subject's chest. We defined sleeping postures as five states focusing on basal-surface, which indicates the place of human's body on the ground. An evaluation was performed both manually by a technician and automatically by designed software. Results is that the accelerometer allowed us to accurately classify postures. Changes in posture were detected with a mean error of less than 3 s, which is acceptable for clinical cardiovascular applications.