在家中使用简单传感器测量的运动重复率可用于估计中风后的上肢Fugl-Meyer评分。
Exercise repetition rate measured with simple sensors at home can be used to estimate Upper Extremity Fugl-Meyer score after stroke.
作者信息
Swanson Veronica A, Johnson Christopher A, Zondervan Daniel K, Shaw Susan J, Reinkensmeyer David J
机构信息
Biorobotics Laboratory, Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA, United States.
Biorobotics Laboratory, Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.
出版信息
Front Rehabil Sci. 2023 Jun 19;4:1181766. doi: 10.3389/fresc.2023.1181766. eCollection 2023.
INTRODUCTION
It 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.
METHODS
41 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.
RESULTS
Using linear regression, UEFM score was well estimated using the rep rate of one forward-reaching exercise from the set of 12 exercises (r = 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) r = 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 r = 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 r = 0.69), but only after we re-estimated coefficients using the home data.
DISCUSSION
These 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.
引言
如果家庭康复训练技术能够自动评估中风后的手臂损伤情况，那将很有价值。在此，我们测试了一种简单的测量方法——使用简单传感器测量进行特定锻炼时的重复率（或“重复速率”）——是否可用于估计上肢 Fugl-Meyer（UEFM）评分。
方法
41 名中风后有手臂损伤的个体在治疗师监督下使用由两个圆盘组成的商业传感器系统进行了 12 项传感器引导的锻炼，这两个圆盘利用力和运动传感来测量每次锻炼重复的开始和结束。其中 14 名参与者随后在家中使用该系统三周。
结果
使用线性回归，通过 12 项锻炼中的一项前伸锻炼的重复速率能很好地估计 UEFM 评分（r = 0.75）；这项锻炼要求参与者在他们面前的桌子上交替点击相距约 20 厘米的圆盘（一个近端，一个远端）。使用指数模型和前伸重复速率能更好地预测 UEFM 评分（留一法交叉验证（LOOCV）r = 0.83）。我们还测试了非线性多变量模型（回归树）预测 UEFM 的能力，但这样的模型并未改善预测效果（LOOCV r = 0.72）。然而，最优决策树也使用前伸任务以及捏握任务，以符合临床直觉的方式对损伤程度不同的患者进行细分。在家中，使用指数模型，前伸锻炼的重复速率能很好地预测 UEFM 评分（LOOCV r = 0.69），但前提是我们使用家庭数据重新估计系数。
讨论
这些结果表明，一种简单的测量方法——使用简单传感器测量锻炼重复速率——可用于推断手臂损伤评分，并表明预测模型应针对临床和家庭环境分别进行调整。
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