Gait Classification Using Machine Learning for Foot Disseises Diagnosis
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Abstract
Person recognition systems based on biometrics have recently attracted a lot of attention in the scientific community. It’s an ever-evolving technology that aspires to do biometric recognition automatically, rapidly, precisely, and consistently. In recent decades, gait recognition has emerged as a type of biometric identification that focuses on recognizing individuals using personal measures and correlations, such as trunk and limb size, as well as space-time information linked to intrinsic patterns in individuals’ motions. Lower-limb surgery is one of the leading causes of loss of autonomy in patients. An improved rehabilitation process is a vital aspect for care facilities since it improves both the patient’s quality of life and the associated costs of the post-surgery procedure. Proper progress monitoring is critical to the success of a rehabilitation program. In this paper, we employed machine learning methods as classifiers to classify foot diseases and then monitor the progress in the patient case. Five classifiers were utilized to train and test the EMG dataset in the lower limb. These classifiers are K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Stochastic Gradient Descent (SGD). The experimental results show high accuracy reaching 99% in both KNN and RF classifiers and 97% in the DT classifier. The fundamental benefit of the suggested procedures is their high estimation accuracy, which leads to better therapeutic results.
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