Exercise repetition rate measured with simple sensors at home can be used to estimate Upper Extremity Fugl-Meyer score after stroke

Frontiers in Rehabilitation Sciences

Rehabilitation Sciences

Christopher A. Johnson¹  Veronica A. Swanson²  David J. Reinkensmeyer³  Susan J. Shaw⁴  Daniel K. Zondervan⁵ 

[1] Biorobotics Laboratory, Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States;Biorobotics Laboratory, Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA, United States;Biorobotics Laboratory, Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA, United States;Department of Anatomy and Neurobiology, UC Irvine School of Medicine, University of California, Irvine, Irvine, CA, United States;Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States;Flint Rehab, LLC, Irvine, CA, United States;

关键词: assessment;    stroke;    sensors;    mRehab;    Fugl-Meyer;    rehabilitation;    home;    remote;   

DOI  :  10.3389/fresc.2023.1181766

 received in 2023-03-07, accepted in 2023-06-01,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionIt would be valuable if home-based rehabilitation training technologies could automatically assess arm impairment after stroke. Here, we tested whether a simple measure—the repetition rate (or “rep rate”) when performing specific exercises as measured with simple sensors—can be used to estimate Upper Extremity Fugl-Meyer (UEFM) score.Methods41 individuals with arm impairment after stroke performed 12 sensor-guided exercises under therapist supervision using a commercial sensor system comprised of two pucks that use force and motion sensing to measure the start and end of each exercise repetition. 14 of these participants then used the system at home for three weeks.ResultsUsing linear regression, UEFM score was well estimated using the rep rate of one forward-reaching exercise from the set of 12 exercises (r2 = 0.75); this exercise required participants to alternately tap pucks spaced about 20 cm apart (one proximal, one distal) on a table in front of them. UEFM score was even better predicted using an exponential model and forward-reaching rep rate (Leave One Out Cross Validation (LOOCV) r2 = 0.83). We also tested the ability of a nonlinear, multivariate model (a regression tree) to predict UEFM, but such a model did not improve prediction (LOOCV r2 = 0.72). However, the optimal decision tree also used the forward-reaching task along with a pinch grip task to subdivide more and less impaired patients in a way consistent with clinical intuition. At home, rep rate for the forward-reaching exercise well predicted UEFM score using an exponential model (LOOCV r2 = 0.69), but only after we re-estimated coefficients using the home data.DiscussionThese results show how a simple measure—exercise rep rate measured with simple sensors—can be used to infer an arm impairment score and suggest that prediction models should be tuned separately for the clinic and home environments.

【 授权许可】

Unknown   
© 2023 Swanson, Johnson, Zondervan, Shaw and Reinkensmeyer.