Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

Abstract

The rapid increase in the elderly population and chronic diseases has increased disability worldwide. This has led researchers and engineers to create tools and technologies that allow health caregivers, physical trainers, and health policymakers to understand, measure, and treat people with disabilities. In addition, artificial intelligence techniques have been used to improve the performance of these technologies. This article presents the development of a novel classifier that utilizes machine learning (ML) algorithms and biomechanical signals to predict a subject’s International Physical Activity Questionnaire (IPAQ) and Fall Efficacy Scale (FES) scores. Three ML algorithms were applied: K-nearest neighbors (KNN), decision tree, and support vector machine (SVM). The results showed classification accuracies of over 95%, 99%, and 89%, respectively, and validated the correlation between qualitative scales and biomechanical responses in balance training. This classifier is an innovative tool that helps professionals adjust and improve their physical training programs.