Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data

Crouch gait, also referred to as flexed knee gait, is an abnormal walking pattern, characterized by an excessive flexion of the knee, and sometimes also with anomalous flexion in the hip and/or the ankle, during the stance phase of gait. Due to the fact that the amount of clinical data related to crouch gait are scarce, it is difficult to find studies addressing this problem from a data-based perspective. Consequently, in this paper we propose a gait recognition strategy using synthetic data that have been obtained using a polynomial based-generator. Furthermore, though this study, we consider datasets that correspond to different levels of crouch gait severity. The classification of the elements of the datasets into the different levels of abnormality is achieved by using different algorithms like k-nearest neighbors (KNN) and Naive Bayes (NB), among others. On the other hand, to evaluate the classification performance we consider different metrics, including accuracy (Acc) and F measure (FM). The obtained results show that the proposed strategy is able to recognize crouch gait with an accuracy of more than 92%. Thus, it is our belief that this recognition strategy may be useful during the diagnosis phase of crouch gait disease. Finally, the crouch gait recognition approach introduced here may be extended to identify other gait abnormalities.