Tracking of body status using extended Kalman filter

Summary form only given. Detection of human physical status is an important aspect of context awareness of modern health monitoring systems. In this paper, an approach for tracking body status by array signals from wearable body accelerometer sensors is presented. The method presented here is based on extended Kalman filter (EKF). The EKF takes the outputs of activity (standing, lying down and sitting) classifiers as the a priori knowledge of body postures and further computes the precise body posture and movement. This approach has been applied to activity monitoring and the experimental results indicate that the EKF are able to track various human activities with good accuracy.