AUTHOR=Maranesi Elvira , Barbarossa Federico , Biscetti Leonardo , Benadduci Marco , Casoni Elisa , Barboni Ilaria , Lattanzio Fabrizia , Fantechi Lorenzo , Fornarelli Daniela , Paci Enrico , Mecozzi Sara , Sallei Manuela , Giannoni Mirko , Pelliccioni Giuseppe , Riccardi Giovanni Renato , Di Donna Valentina , Bevilacqua Roberta 

TITLE=Older people and stroke: a machine learning approach to personalize the rehabilitation of gait

JOURNAL=Frontiers in Aging

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2025.1562355

DOI=10.3389/fragi.2025.1562355

ISSN=2673-6217

ABSTRACT=IntroductionStroke is a significant global public health challenge, ranking as the second leading cause of death after heart disease. One of the most debilitating consequences for stroke survivors is the restriction of mobility and walking, which greatly impacts their quality of life. The scientific literature extensively details the characteristics of post-stroke gait, which differs markedly from physiological walking in terms of speed, symmetry, balance control, and biomechanical parameters. This study aims to analyze the gait parameters of stroke survivors, considering the type of stroke and the affected cerebral regions, with the goal of identifying specific gait biomarkers to facilitate the design of personalized and effective rehabilitation programs.MethodsThe research focuses on 45 post-stroke patients who experienced either hemorrhagic or ischemic strokes, categorizing them based on the location of brain damage (cortical-subcortical, corona radiata, and basal ganglia). Gait analysis was conducted using the GaitRite system, measuring 39 spatio-temporal parameters.ResultsStatistical tests revealed no significant differences, but Principal Component Analysis identified a dominant structure. Machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN)—were employed for classification, with RF demonstrating superior performance in accuracy, precision, recall (all exceeding 85%), and F1 score compared to SVM and KNN. Results indicated ML models could identify stroke types based on gait variables when traditional tests could not. Notably, RF outperformed others, suggesting its efficacy in handling complex and nonlinear data relationships.DiscussionThe clinical implication emphasized a connection between gait parameters and cerebral lesion location, notably linking basal ganglia lesions to prolonged double support time. This underscores the basal ganglia’s role in motor control, sensory processing, and postural control, highlighting the importance of sensory input in post-stroke rehabilitation.