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

The rapid increase of the elderly population and chronic diseases have augmented disability in today's world. This situation has led researchers engineers to create tools technologies that allow health caregivers, physical trainers, policymakers understand, measure, treat people with disabilities. Nowadays, artificial intelligence techniques been applied improve performance these technologies. article shows development a novel classifier utilizes Machine Learning (ML) algorithms biomechanical signals predict subject's International Physical Activity Questionnaire (IPAQ) Falls Efficacy Scale (FES). Three ML were K-Nearest Neighbors (KNN), Decision tree, Support Vector (SVM). Results show accuracy classification over 95%, 99%, 89%, respectively, validate correlation between qualitative scales responses balance training. poses as an innovative tool help professionals adjust training programs.