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

Well, assessment crapola again, NOT any useful recovery protocols.

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

CHIA-RU CHUNG1, MU-CHUN SU1, SI-HUEI LEE2,3, ERIC HSIAO-KUANG WU1, LI-HSIEN
TANG1, AND SHIH-CHING YEH1
1Department of Computer Science and Information Engineering, National Central University, Taoyuan 320,
Taiwan
2Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei 112, Taiwan
3National Yang-Ming University, Taipei 112, Taiwan
CORRESPONDING AUTHOR: Eric Hsiao-Kuang Wu (hsiao@csie.ncu.edu.tw)

Abstract

 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 regard to the assessment of motor function, however, clinical evaluation scales as Fugl-Meyer Assessment (FMA), Wolf Motor Test(WMFT), Test D'évaluation Membres Supérieurs Des Personnes  gées(TEMPA) are highly depended clinic. Further, there is not an assessment method that simultaneously consider motion trajectory and evaluation The objective of this study is establish an evidence based assessment model by machine learning method integrated motion trajectory task clinical evaluation scales. study, VR system 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 develop machine learning based model that represents 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 upto 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. 

Index Terms

—Stroke rehabilitation, Motor training, Virtual reality, Machine learning.
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