Compute Process and Measurement Noise Covariances for Human Motion Estimation: A Kalman Filter Approach with IoT Sensors

Abstract

The elderly people in the developed countries are significantly increased. Unfortunately, they are very vulnerable for falling, so it is very difficult for their take care. It is therefore expected that they will need to properly monitor from the smart control centre. Interestingly, the internet of things (IoT) is one of the promising technologies to assist them from the control centre. This paper proposes process and measurement noise covariances estimation scheme for human motion estimations using IoT sensors. After presenting the biomedical systems such as human motion structure, the IoT sensors are used to track and monitor human motion and posture. Technically, the Kalman filter based optimal algorithm is proposed for motion estimations. Based on the residual error and Kalman gain, the process noise covariance is determined. Moreover, the measurement noise covariance is calculated combine with the prior error covariance and residual error. Numerical simulation results demonstrate that the developed algorithm can accurately track human orientation even if there is cyber attack in the IoT sensor.