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%.
Highlights
Musculoskeletal disorders are one of the leading causes of physician visits and have a major impact on society in terms of long-term disability and economics
It is obvious that our hidden Markov model (HMM) is significantly better than dynamic time warping (DTW) in detecting abnormal human gaits
We propose an approach for abnormal gait detection without prior knowledge about anomaly in human walking
Summary
Musculoskeletal disorders are one of the leading causes of physician visits and have a major impact on society in terms of long-term disability and economics. The key of this research is that a set of consecutive skeletons is represented by a spatio-temporal feature, which is determined based on 3D position of joints together with the motion’s age This approach provided good experimental results, it is difficult to obtain a similar accuracy in practical situations. (3) using a simple and fast HMM-based algorithm with discrete observations, i.e., codewords, for online classification; (4) proposing a low-cost and easy to use Kinect-based gait analysis system for a clinical setting; and (5) confirming accuracy of abnormal gait detection by evaluating it on a large number of gait types from several databases and comparing it with a state-of-the-art study [10].
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