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Classification of Gait Anomaly due to Lesion Using Full-Body Gait Motions
Tsuyoshi HIGASHIGUCHI Toma SHIMOYAMA Norimichi UKITA Masayuki KANBARA Norihiro HAGITA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/04/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
gait motion, full-body motion, lesioned part, 3D human skeleton,
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This paper proposes a method for evaluating a physical gait motion based on a 3D human skeleton measured by a depth sensor. While similar methods measure and evaluate the motion of only a part of interest (e.g., knee), the proposed method comprehensively evaluates the motion of the full body. The gait motions with a variety of physical disabilities due to lesioned body parts are recorded and modeled in advance for gait anomaly detection. This detection is achieved by finding lesioned parts a set of pose features extracted from gait sequences. In experiments, the proposed features extracted from the full body allowed us to identify where a subject was injured with 83.1% accuracy by using the model optimized for the individual. The superiority of the full-body features was validated in in contrast to local features extracted from only a body part of interest (77.1% by lower-body features and 65% by upper-body features). Furthermore, the effectiveness of the proposed full-body features was also validated with single universal model used for all subjects; 55.2%, 44.7%, and 35.5% by the full-body, lower-body, and upper-body features, respectively.