Enhancing Biomechanical Understanding  Utilizing Effective Machine Learning Methods for Comprehensive Gait Analysis and Rehabilitation

Krishnapriya Mallampalli*, Kaparapu Babulu and Mamidipaka Hema

Department of Electronics and Communication Engineering , Jntu-Gv College of Engineering Vizianagaram, Dwarapudi, Andhra Pradesh.

Corresponding Author E-mail:krishnapriya411@gmail.com

ABSTRACT: Technological developments in predictive modeling (ML) hold the potential to revolutionize our understanding of biomechanics, especially in the areas of gait evaluation and rehabilitation. This work explores the use of machine learning techniques to improve the accuracy and scope of gait analysis. We analyze gait data using supervised or unsupervised algorithms to find anomalous movements and patterns more precisely than we could with conventional methods. We use neural networks to track gait in real time, clustering algorithms to classify patients, and predictive models to predict the course of rehabilitation. The findings show that machine learning (ML) greatly enhances the ability to identify mild gait abnormalities, which enables tailored rehabilitation regimens. This study demonstrates how machine learning (ML) has the potential to transform gait analysis, providing better biomechanical insights that lead to better the results achieved for patients and more effective healthcare practices.

KEYWORDS: Gait Analysis; Healthcare; Machine Learning; Medical Diagnosis; Supervised Algorithms

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