Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures

Bioelectronic Medicine

Taya Hamilton¹  Hermano I. Krebs¹  Caio B. Moretti²  Bruce T. Volpe³  Johanna L. Chang³  Mar Cortes⁴  Dylan J. Edwards⁵  Avrielle Rykman Peltz⁶  Alexandre C. B. Delbe⁷ 

[1] Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA;Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA;Universidade de Sao Paulo, Avenida Trabalhador Saocarlense – 400, Sao Carlos, SP, Brazil;Feinstein Institute for Medical Research, 350 Community Dr, 11030, Manhasset, NY, USA;Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, 10029, New York, NY, USA;Moss Rehabilitation Research Institute, 60 Township Line Rd, 19027, Elkins Park, PA, USA;Rehabologym, 90 N Broadway, 10533, Irvington, NY, USA;Universidade de Sao Paulo, Avenida Trabalhador Saocarlense – 400, Sao Carlos, SP, Brazil;

关键词: Stroke;    Kinematics;    Outcome measures;    Correlation;    Robotics;    tDCS;   

DOI  :  10.1186/s42234-021-00082-8

来源: Springer

【 摘 要 】

BackgroundA 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.MethodsData 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.ResultsShoulder-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.ConclusionsDistal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations.Trial registrationhttp://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663.

【 授权许可】

CC BY