Early adherence to biofeedback training predicts long-term improvement in stroke patients: A machine learning approach
Nandini Sengupta, Rezaul Begg, Aravinda S. Rao, Soheil Bajelan, Catherine M. Said, Lisa James, Marimuthu Palaniswami
TL;DR
This study uses machine learning to predict long-term stroke rehabilitation outcomes based on early training data, enabling personalized interventions.
Contribution
A novel adherence assessment metric and machine learning approach to predict stroke patient improvement with 100% accuracy.
Findings
Early training data can predict post-assessment improvement with 100% accuracy.
Patients with dissimilar Minimum Foot Clearance values from baseline and adherence to feedback are more likely to improve.
Non-improving patients may benefit from extended training periods, suggesting personalized rehabilitation strategies.
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 a novel approach using cosine similarity (CS), correlation coefficient (CC) and cross-correlation distance (XCRD) measures to predict post-assessment improvements in stroke patients. We also…
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Taxonomy
TopicsStroke Rehabilitation and Recovery · Traumatic Brain Injury Research · Balance, Gait, and Falls Prevention