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

Abstract

The rapid increase of the elderly population and chronic diseases have augmented disability in today's world. This situation has led researchers and engineers to create tools and technologies that allow people with disabilities to treat or recover faster and easier. Nowadays, techniques of artificial intelligence have been applied to improve the performance of these technologies. This article shows 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 Falls Efficacy Scale (FES). Three ML algorithms were applied KNN, Decision tree, and SVM. Results show the accuracy of classification over 95%, 99%, and 89%, respectively, and validate the correlation between qualitative scales and biomechanical responses in balance training.