MotionPhone: a Wireless Geophone-Based In-Bed Body Motion Detection and Classification System

Abstract

Measuring in-bed mobility is a very significant consideration when it comes to tracking patients or individuals during sleep. A variety of applications, such as sleep monitoring and unusual movements during sleep, can be enabled by observing a persons body movements throughout sleep. In-bed movement can be a sign of sleep disruption as it is associated with wakefulness that affects the quality of sleep and can be a sign of many illnesses. We introduce, in this study, an unobtrusive scheme for in-bed motion detection and classification using geophones sensor. Geophone can sense the vibration that caused by every in-bed movement. We have extracted two features from the sensed signal, which we named as Energy-Peak and Log-Peak. We, at that point, utilized a straightforward threshold-based calculation to identify each conceivable movement. In addition to movements detection, we further classify them as a big or small motion. We have assessed this framework by doing 30 tests with 15 members over a two-month. There are 35 movements in each experiment. By using our two primary approaches, Energy-Peak and Log-Peak, our system can identify in-bed motions with a 2% as a low error rate. For classification phase, we have extracted 4 features from every detected movement and we have used Random Forest technique for classification decision. Our system can classify every movement as big or small with 1.5% as an error rate.