Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke - Goffredo, Michela; Proietti, Stefania; Pournajaf, Sanaz; Galafate, Daniele; Cioeta, Matteo; Le Pera, Domenica; Posteraro, Federico; Franceschini, Marco (Frontiers in Bioengineering and Biotechnology, 2022)
Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can predict concurrently registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient’s upper limb abilities is needed. Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics. Methods: A re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was conducted. Two least squares error multiple linear regression models for outcome prediction were developed that differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected Upper Limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics. Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV ones. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant. Conclusions: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical assessment of upper limb muscle strength and may be useful to clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.