Abstract
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.
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