Moretti Caio B, Hamilton Taya, Edwards Dylan J, Peltz Avrielle Rykman, Chang Johanna L, Cortes Mar, Delbe Alexandre C B, Volpe Bruce T, Krebs Hermano I
Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Universidade de Sao Paulo, Avenida Trabalhador Saocarlense - 400, Sao Carlos, SP, Brazil.
Bioelectron Med. 2021 Dec 29;7(1):21. doi: 10.1186/s42234-021-00082-8.
A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models.
Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output.
Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model.
Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations.
http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .
详细的感觉运动评估对于规划有效的个体化中风后治疗至关重要。机器人运动学分析可能在理解中风恢复方面提供更高的准确性和分辨率。在此，我们研究远端腕部测量对近端机器人运动学分析的附加价值，以提高其与慢性中风患者临床上肢测量指标的相关性。其次，我们比较线性和非线性回归模型。
数据来源于2012年至2016年进行的一项多中心随机对照试验，该试验研究了机器人治疗和经颅直流电刺激（tDCS）的联合效果。从4项肩肘任务中得出24项运动学指标，从3项腕部和前臂评估任务中得出35项指标。进行了基于相关性的特征选择，仅保留与目标属性显著相关的特征（R>0.5）。非线性模型采用多层感知器神经网络的形式：一个隐藏层和一个线性输出。
肩肘指标与Fugl Meyer评估（上肢，FMA-UE）显示出显著相关性，线性模型的R=0.82（P<0.001），非线性模型的R=0.88（P<0.001）。同样，腕部运动学与FMA-UE也有高度相关性（线性模型和非线性模型的R分别为0.91（P<0.001）和0.92（P<0.001））。联合分析得出线性模型的R=0.91（P<0.001），非线性模型的R=0.91（P<0.001）。
远端腕部运动学与临床结果高度相关，值得未来对急性或亚急性中风人群探索我们的非线性腕部模型进行研究。
http://www.clinicaltrials.gov 。实际研究开始日期为2012年9月。首次注册于2012年11月15日。回顾性注册。唯一标识符：NCT01726673和NCT03562663 。
