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Using a Skeleton Gait Energy Image for Pathological Gait Classification

Loureiro, J. ; Correia, P.L.

Using a Skeleton Gait Energy Image for Pathological Gait Classification, Proc IEEE International Conference on Automatic Face and Gesture Recognition FG, Buenos Aires, Argentina, Vol. 1, pp. 410 - 414, May, 2020.

Digital Object Identifier: 10.1109/FG47880.2020.00064

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Abstract
Gait, or the way of walking, of a person can be affected by several pathologies. Automatic gait analysis methods have recently been proposed to identify and distinguish different types of pathological gait. Vision-based methods are often adopted, using some representation of the gait video sequence as input to feature extraction and classification algorithms. The Gait Energy Image (GEI), a mean image of the person’s silhouettes along a gait cycle, is one of the most used gait representations for biometric recognition purposes and also for pathology identification. In this paper we propose an alternative compact gait representation, the Skeleton Energy Image (SEI), computed based on skeleton images instead of silhouettes, to remove from the representation some characteristics mostly useful for biometric recognition purposes, like the body shape,
while preserving and highlighting the movement information. A method for feature extraction and classification is developed, and a new pathological gait dataset is acquired to test the system, which is made available to the research community. The proposed SEI representation allows obtaining better gait pathology classification results than the GEI. A method combining both representations further improves the performance of the system.