Early adherence to biofeedback training predicts long-term improvement in stroke patients: A machine learning approach

Abstract

Biofeedback-based treadmill training generally involves 10 or more sessions to assess its effectiveness during stroke rehabilitation. Improvements are seen in some patients during the assessment, while others do not progress. Our aim in this study is to determine (i) if signs of progress are evident from the initial training session and (ii) whether quantitative measurements between consecutive training sessions can guide interventions for non-progressing patients. The study analyzes Minimum Foot Clearance (MFC) data from 15 stroke patients during their baseline and second training sessions to predict outcomes in the post-assessment phase. Based on early biofeedback training data, we propose..