Human gait analysis for stroke rehabilitation therapy using video processing tools has become popular in recent years. This paper proposes a single-camera system for capturing gait patterns using a Kalman-Structural-Similarity-based algorithm which tracks multiple markers simultaneously. This algorithm is initialized by obtaining the user-selected blocks in the first frame of each video, and the tracker is implemented by using Structural-Similarity image quality assessment algorithm to detect each marker frame by frame within a search area determined by a discrete Kalman filter. Experimental results show the trajectories of the markers fixed on the joints of a human body. The obtained numerical results are used to generate gait information (e.g., knee joint angle) that is later used for diagnostics. The proposed method aims to explore an alternative and portable way to implement human gait analysis with significantly less cost compared to a state-of-the-art 3D motion capture system.
['Cheng Yang', 'Ukadike C. Ugbolue', 'Bruce Carse', 'Vladimir Stankovic', 'Lina Stankovic', 'Philip Rowe']
Multiple marker tracking in a single-camera system for gait analysis