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- W4282032010 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 health caregivers, physical trainers, and health policymakers to understand, measure, and treat people with disabilities. Nowadays, artificial intelligence techniques 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 K-Nearest Neighbors (KNN), Decision tree, and Support Vector Machine (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. This classifier poses as an innovative tool to help professionals adjust and improve physical training programs." @default.
- W4282032010 created "2022-06-13" @default.
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- W4282032010 date "2022-05-22" @default.
- W4282032010 modified "2023-09-30" @default.
- W4282032010 title "Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning." @default.
- W4282032010 doi "https://doi.org/10.36909/jer.16527" @default.
- W4282032010 hasPublicationYear "2022" @default.
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