AUTHOR=Hu Wenting , Combden Owen , Jiang Xianta , Buragadda Syamala , Newell Caitlin J. , Williams Maria C. , Critch Amber L. , Ploughman Michelle TITLE=Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis JOURNAL=Frontiers in Artificial Intelligence VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.952312 DOI=10.3389/frai.2022.952312 ISSN=2624-8212 ABSTRACT=
Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (