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Walking pattern classification using a granular linguistic analysis
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DOI：10.1016/j.asoc.2015.04.036.png)
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Abstract
En 中文
Classifying walking patterns helps the diagnosis of health status, disease progression and the effect of interventions. In this paper, we develop previous research on human gait to extract a meaningful set of parameters that allow us to design a highly interpretable system capable of identifying different gait styles with linguistic fuzzy if-then rules. The model easily discriminates among five different walking patterns, namely: normal walk, on tiptoes, dragging left limb, dragging right limb, and dragging both limbs. We have carried out a complete experimentation to test the performance of the extracted parameters to correctly classify these five chosen gait styles. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Walking pattern classification
Human gait model
Linguistic modeling
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