Stance and Gait Analysis of Apollo Astronauts on the Moon.

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Abstract

[Indexed for MEDLINE] Conflict of interest statement: The authors declare no conflicts of interest. 19. Sensors (Basel). 2016 Oct 26;16(11):1792. doi: 10.3390/s16111792. Skeleton-Based Abnormal Gait Detection. Nguyen TN(1), Huynh HH(2), Meunier J(3). Author information: (1)DIRO, University of Montreal, Montreal, QC H3T 1J4, Canada. nguyetn@iro.umontreal.ca. (2)The University of Danang - University of Science and Technology, Danang 556361, Vietnam. hhhung@dut.udn.vn. (3)DIRO, University of Montreal, Montreal, QC H3T 1J4, Canada. meunier@iro.umontreal.ca. 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%. DOI: 10.3390/s16111792 PMCID: PMC5134451