RESEARCH ARTICLE


Motion Cue Analysis for Parkinsonian Gait Recognition



Taha Khan* , Jerker Westin, Mark Dougherty
Computer Engineering, School of Technology and Business Studies, Dalarna University, 79188, Falun, Sweden


Article Metrics

CrossRef Citations:
27
Total Statistics:

Full-Text HTML Views: 759
Abstract HTML Views: 387
PDF Downloads: 190
Total Views/Downloads: 1336
Unique Statistics:

Full-Text HTML Views: 425
Abstract HTML Views: 245
PDF Downloads: 159
Total Views/Downloads: 829



Creative Commons License
© Khan et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Computer Engineering, School of Technology and Business Studies, Dalarna University, 79188, Falun Sweden; Tel: 0046-076-0831014; Fax: 0046-023-77 80 80; E-mails: tkh@du.se, tahak@rocketmail.com


Abstract

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson’s disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject’s body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

Keywords: Gait impairment, Parkinson’s disease, Gait video analysis, Image processing..