Movement Disorder Detection via Adaptively Fused Gait Analysis Based on Kinect Sensors

This paper presents a novel method for movement disorder detection using a single Kinect sensor. Numerous recent studies have focused on gait analysis involving spatiotemporal parameters, such as the step length and gait cycle. However, these methods ignore significant kinematic information, e.g., leg swing characteristics. Therefore, in this paper, we 1) introduce a new concept called gait symmetry to measure the similarity of leg swing motions using the correlation among the angles formed by the hip, knee, and ankle joints; 2) extract the step length and gait cycle using a zero-crossing detection method; and 3) explore the K-means and Bayesian methods to conduct a combined gait analysis using the three extracted gait parameters. In addition, to reduce the effects of walking differences related to age and gender, we conduct a gait analysis of different age and gender groups. In our experiments, we collect a data set consisting of data from 120 participants (50 with disordered walking patterns and 70 with normal walking patterns). The experimental results show that 1) including the proposed gait symmetry parameter greatly improves detection accuracy, 2) joint utilization of the three gait parameters can enable significantly better performance, and 3) considering age and gender information can enable more robust detection.