Abstract

For proper walking, metatarsophalangeal joints play a crucial role during gait. In this study, we specify characteristic features observed in the foot motion signals measured by an in-shoe motion sensor that are significantly affected by the first metatarsophalangeal angle (FMTPA). To validate these specified features, we conduct a classification test for high and low FMTPA subjects by using them. Fifty subjects (23 males and 27 females) participated in our study, and their FMTPAs were measured by digital photography. The subjects with FMTPA above and below 20 degrees were categorized as the high and low groups, respectively, on the basis of the FMTPA distribution in all subjects. The perspective features that have a level of significance of p < 0.05 were specified by comparing the foot motion of the subjects in both groups. The two groups were classified by support vector machine learning. From the results, most significant differences are observed in the coronal plane during pre-swing. By applying the specified features as a predictor, 82.1% of strides are correctly classified in the leave-one-subject-out cross-validation test, which suggests the specified foot motion features intrinsically reflect the gait change induced by FMTPA.

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