An Intelligent Motor Assessment Method Utilizing a Bi-Lateral Virtual-Reality Task for Stroke Rehabilitation on Upper...

Virtual reality (VR) has been widely adopted by therapists to provide rich motor training tasks. Time series data of motion trajectory accompanied with the interaction of VR system may contain important clues in regard to the assessment of motor function, however, clinical evaluation scales such as Fugl-Meyer Assessment (FMA), Wolf Motor Function Test (WMFT), and Test D'évaluation Des Membres Supérieurs Des Personnes Âgées (TEMPA) are highly depended in clinic. Further, there is not yet an assessment method that simultaneously consider motion trajectory and clinical evaluation scales. The objective of this study is to establish an evidence-based assessment model by machine-learning method that integrated motion trajectory of a VR task with clinical evaluation scales. In this study, a VR system for upper-limb motor training was proposed for stroke rehabilitation. Clinical trials with 20 stroke patients were performed. A variety of motor indicators that derived via motion trajectory were proposed. The correlations between motor indicators and clinical evaluation scales were examined. Further, motor indicators were integrated with evaluation scales to develop a machine-learning based model that represents an evidence-based motor assessment approach. Clinical evaluation scales, FMA, TEMPA and WMFT, were significantly progressed. A few motor indicators were found significantly correlated with clinical evaluation scales. The accuracy of machine-learning based assessment model was up to 86%. The proposed VR system is validated to be effective in motor rehabilitation. Motor indicators derived from motor trajectory were with potential for clinical motor assessment. Machine learning could be a promising tool to perform automatic assessment. Clinical and Translational Impact Statement-A VR task for motor rehabilitation was exanimated via clinical trials. Integrating motor indices with clinical assessment, a machine-learning model with accuracy of 86% was developed to evaluate motor function.