Abstract
Human gait data follows distinct and identifiable patterns that are critical for movement analysis and evaluation Like other biological signals. The success of a rehabilitation program is dependent on the execution of proper progress monitoring. To ensure success, diagnosis of gait anomalies, as well as the implementation of therapy to address them, must be validated in a constant and timely manner in developing youngsters. In this paper, machine learning techniques were utilized to classify foot diseases and the purpose is to increase the accuracy of disease detection and diagnosis because intelligent systems can contribute significantly in the medical field and have proven their worth in diagnosing many diseases. The results show high accuracy of the used machine learning algorithms, where the accuracy of the classifiers reached 100% for Random Forest (RF), Decision Tree (DT), and k-nearest neighbors (KNN), while it reached 98% for Logistic Regression. Index Terms—Biometrics, Machine Learning (ML), Drop foot (DF), Leg Rehabilitation, and Human gait.
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