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

Caio B Moretti, Taya Hamilton, Dylan J Edwards, Avrielle Rykman Peltz, Johanna L Chang, Mar Cortes, Alexandre C B Delbe, Bruce T Volpe, Hermano I Krebs, Caio B Moretti, Taya Hamilton, Dylan J Edwards, Avrielle Rykman Peltz, Johanna L Chang, Mar Cortes, Alexandre C B Delbe, Bruce T Volpe, Hermano I Krebs

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations.

Trial registration: http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .

Keywords: Correlation; Kinematics; Outcome measures; Robotics; Stroke; tDCS.

Conflict of interest statement

TH reports personal fees from Bionik Laboratories, outside the submitted work; In addition, TH has a patent “An apparatus and/or method for positioning a hand for rehabilitation” pending to Bionik Laboratories. HIK declares he was the founder of Interactive Motion Technologies and Chairman of the Board (1998–2016). He successfully sold Interactive Motion Technologies on April 2016 to Bionik Laboratories, where he served as Chief Science Officer and Board Member until July 2017. HIK was the founder of 4Motion Robotics. HIK has patents; Interactive Robotic Therapist; US Patent 5,466,213; 1995; Massachusetts Institute of Technology issued, and a patent Wrist And Upper Extremity Motion; US Patent No. 7,618,381; 2009; Massachusetts Institute of Technology licensed to Bionik Laboratories.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Heatmap of S/E kinematic and kinetic data correlation with clinical scales
Fig. 2
Fig. 2
Heatmap of wrist-forearm kinematic data correlation with clinical scales
Fig. 3
Fig. 3
Unassisted proximal and distal movement attempts of three representative stroke participants at study baseline and completion

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