Journal article

Evaluating Rehabilitation Progress Using Motion Features Identified by Machine Learning

Lei Lu, Ying Tan, Marlena Klaic, Mary P Galea, Fary Khan, Annie Oliver, Iven Mareels, Denny Oetomo, Erying Zhao

IEEE Transactions on Biomedical Engineering | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2021

Abstract

Evaluating progress throughout a patient's rehabilitation episode is critical for determining the effectiveness of the selected treatments and is an essential ingredient in personalised and evidence-based rehabilitation practice. The evaluation process is complex due to the inherently large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper expl..

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Grants

Awarded by National Natural Science Foundation of China


Awarded by International Postdoctoral Exchange Fellowship


Awarded by Young Innovative Talent Training Program of Undergraduate Colleges of Heilongjiang Province


Awarded by Hielongjiang Province Science Foundation for Youths


Funding Acknowledgements

This work was supported by the National Natural Science Foundation of China (#51705106, #81903397), in part by the International Postdoctoral Exchange Fellowship (#20170042), in part by Young Innovative Talent Training Program of Undergraduate Colleges of Heilongjiang Province (#UNPYSCT-2018059), and in part by Hielongjiang Province Science Foundation for Youths (#QC2017019).