Improving joint position estimation of Kinect using anthropometric constraint based adaptive Kalman filter for rehabilitation

Abstract

The role of Microsoft Kinect in rehabilitation and clinical assessments of elderly and stroke patients is growing due to its affordability and unobtrusiveness in assessing human body joint kinematics. However, Kinect data is highly contaminated by noise due to the nature of the structured light based system. The noise characteristics vary for static and dynamic joints effecting the anthropometric measurement of the body segments connecting any two joints. Here, we use Kalman filter to track the body joints and to denoise the unwanted joint vibrations. Initially, the process noise of Kalman filter is chosen based on minimization of standard deviation of the position of static joints and the latency (delay) in velocity adaptation for dynamic joints. Further, the measurement noise is dynamically adapted based on the velocity of the joints. Additionally, a differential evolutionary based constraint is applied to minimize the variation in the estimated body segment length. The proposed method outperforms the existing techniques like low pass filtering, exponential and double exponential filtering methods in both static and dynamic conditions. Experimental results for Kinect Xbox 360 show atleast 52% and 32% improvement in standard deviation of bone length, associated with joints under motion, for 26 healthy subjects for range of motion (ROM) and single limb standing respectively. We found 40% improvement in deviation of bone length, when tested on 10 stroke patient’s data. We also tested our algorithm on healthy population for ROM exercises in Kinect Xbox One and achieved an average improvement of 34% in variation in bone length.