Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Biosciences Biotechnology Research Asia

DOI: 10.13005/bbra/3361

Authors: Krishnapriya Mallampalli, Kaparapu Babulu, Mamidipaka Hema

Institution: Jawaharlal Nehru Technological University-Gurajada, Vizianagaram

Language: English

Topic: Gait Recognition and Analysis

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4409115908