Improvement in Kinect based measurements using anthropometric constraints for rehabilitation

Abstract

The increasing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its affordability, portability and being a markerless system for human motion capture. However, it is inefficient in terms of accuracy in measuring 3-D body joint locations when compared to marker-based motion capture systems. The measured length of the physically connected joints (bone length) vary with time, along with joint fluctuations during static pose. In this paper, we propose a novel approach to filter the noise of the Kinect 3-D joint coordinates while minimizing the variation in bone length. Kalman is used to filter the data and track the motion, while differential evolutionary (DE) optimization is used to minimize the fluctuations in bone length. A feedback loop is introduced between the Kalman and DE for exchanging the parameters. The algorithm is tested on the data obtained from 26 healthy subjects performing Range of Motion, Single Limb Stance (SLS) exercises and 10 stroke survivors performing SLS. Experimental results show average 41% and 40% improvement in deviation of bone length, which are under motion, for the healthy subjects and stroke patients respectively, outperforming Kalman filter and other existing algorithms like low pass filter, exponential, double exponential filter.