Automatic noise estimation and context-enhanced data fusion of IMU and Kinect for human motion measurement

The aim of this paper is to propose a robust, accurate and portable system for human body motion measurement. The system includes Inertial Measurement Units (IMU) and a Kinect or vision sensors. Since Kinect sampling rate is low (30 Hz per second) and it suffers from occlusion, it cannot individually measure human body motion accurately and robustly. On the other hand, IMU does not suffer from these problems, but it suffers from drift in particular with long-term motion monitoring and other types of errors (e.g., high acceleration motions, temperature and voltage variations). Thus, in this study, IMU and Kinect data were fused using a context enhanced extended Kalman filter. Rules were generated based on the context of motion in order to adjust Kalman filter parameters. In addition, an automated approach is introduced to estimate the variance of the noise of the sensors during the operation. Considering motion context and automatic noise detection, the robustness of monitoring is enhanced against errors related to motion context (i.e., high acceleration and long-term motions); furthermore, offline calibration is no longer required to set the parameters of the filter. The system was tested on leg and arm motions. The root mean square error of our fusion method was 6.08° lower than using only gyroscope, 16.98° lower than using only accelerometer, 2.49° lower than using only the Kinect and 8.99° lower than using simple EKF fusion method, which does not consider motion context and automatic noise estimation.