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Sensors 2016, 16(11), 1792; doi:10.3390/s16111792

Skeleton-Based Abnormal Gait Detection

1
DIRO, University of Montreal, Montreal, QC H3T 1J4, Canada
2
The University of Danang - University of Science and Technology, Danang 556361, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 5 August 2016 / Revised: 17 October 2016 / Accepted: 21 October 2016 / Published: 26 October 2016
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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Abstract

Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%. View Full-Text
Keywords: human gait; gait analysis; gait cycle; hidden Markov model; Kinect human gait; gait analysis; gait cycle; hidden Markov model; Kinect
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Nguyen, T.-N.; Huynh, H.-H.; Meunier, J. Skeleton-Based Abnormal Gait Detection. Sensors 2016, 16, 1792.

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