Stroke commonly leads to long-term gait impairments, underscoring the need for objective and continuous functional assessment during rehabilitation. This study employs machine learning methods to assess a smart insole-based system in stroke gait recognition. Data were collected from stroke survivors and healthy control participants during Walk and Timed-Up-and-Go tasks. After preprocessing, group differences were quantified using Hedges' g, and multiple machine learning models were applied to classify two participant groups. Support Vector Machine and KNN achieved the best performance, with accuracies of 0.88. The results demonstrate that sensor-based gait features can be used to distinguish stroke gait patterns from control gait patterns, highlighting the potential of this approach for future homebased monitoring and personalised rehabilitation.
Chien et al. (Mon,) studied this question.