Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers

Abstract

Background: The efficacy of upper-limb Robot-assisted Therapy (ulRT) in stroke subjects is well-established. The robot-measured kinematic data can assess the biomechanical changes induced by ulRT and the progress of patient over time. However, literature on the analysis of pre-treatment kinematic parameters as predictive biomarkers of upper limb recovery is limited.Objective: The aim of this study was to calculate pre-treatment kinematic parameters from point-to-point reaching movements in different directions and to identify biomarkers of upper-limb motor recovery in subacute stroke subjects after ulRT.Methods: An observational retrospective study was conducted on 66 subacute stroke subjects who underwent ulRT with an end-effector robot. Kinematic parameters were calculated from the robot-measured trajectories during movements in different directions. A Generalized Linear Model (GLM) was applied considering the post-treatment Upper Limb Motricity Index and the kinematic parameters (from demanding directions of movement) as dependent variables, and the pre-treatment kinematic parameters as independent variables.Results: A subset of kinematic parameters significantly predicted the motor impairment after ulRT: the accuracy in adduction and internal rotation movements of the shoulder was the major predictor of post-treatment Upper Limb Motricity Index. The post-treatment kinematic parameters of the most demanding directions of movement significantly depended on the ability to execute elbow flexion-extension and abduction and external rotation movements of the shoulder at baseline.Conclusions: The multidirectional analysis of robot-measured kinematic data predicts motor recovery in subacute stroke survivors and paves the way in identifying subjects who may benefit more from ulRT.

Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke

“…In our previous analysis of the data (Goffredo et al, 2021), the error of the trajectory towards the body negatively affects the probability of increasing one class in the discharge Motricity Indexes. However, in the re-analysis of data with least squares multiple linear regression, we found that the error of the trajectory towards the body was a significant predictor of Motricity Indexes, which strongly correlate (r > 0.8 for the MI UL ) with the therapist-assigned ones (Figures 2, 3).…”

“…The procedure consisted of a re-analysis of 66 inpatients with subacute stroke who were included in a 4-week ulRT and whose data had been examined in our previous studies with different aims (Goffredo et al, 2019;Goffredo et al, 2021). In particular, in Goffredo et al (Goffredo et al, 2021), a relationship was found between RMK measures at baseline and (clinical and RMK) data at the end of ulRT, considering each direction of movement separately (Goffredo et al, 2021), and revealing that a subset of kinematic parameters was correlated with the probability of rising one class in the Motricity Indexes (when considered as Frontiers in Bioengineering and Biotechnology frontiersin.org categorical variables). In contrast, in the present study, we developed models to predict Motricity Indexes at discharge from selected RMK features and found that the SSV and LOOCV validation procedures explained 54% and 71% of the variance in clinical scores at discharge respectively.…”

“…The latter is in accordance with the kinematic approach to identify how the central nervous system represents and implements the motor control strategies necessary to obtain the movements in stroke patients ( Micera et al, 2005 ). RMK data were also processed to predict the clinical assessment outcomes ( Krebs et al, 2014 ; Duret et al, 2019 ; Agrafiotis et al, 2021 ; Goffredo et al, 2021 ; Grimm et al, 2021 ; Moretti et al, 2021 ), considering the importance of biomarkers of neurorehabilitation outcomes for evidence-based practice ( Langhorne et al, 2009 ; Scott and &Dukelow, 2011 ; Duret et al, 2015 ; Franceschini et al, 2018 ). In this context, the Predict REcovery Potential (PREP2) tool is the predominant predictor of upper limb functional outcomes from clinical assessment, Magnetic Resonance Imaging (MRI) and Transcranial Magnetic Stimulation (TMS) biomarkers, with an impact on rehabilitation planning and realistic treatment goal setting ( Stinear et al, 2017 ).…”

“…They found that selected RMK parameters, calculated from the total end-effector trajectory did not predict the upper limb Fugl-Meyer Assessment. In our previous paper ( Goffredo et al, 2021 ), we analyzed RMK metrics calculated from reaching movements with different directions. Specifically, the movement path error, the mean movement speed, and the number of speed peaks of each point-to-point trajectory were analyzed.…”

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

“…For example, therapy focused on arm use makes sense for patients with high PANU scores but is certainly less suitable for patients with mandatory compensations. In the same way that kinematic data measured during robot-assisted therapy can help predict patient recovery [ 58 ], using valid kinematic variables measured with the Kinect has the potential to predict which patients are the most likely to benefit from a specific therapy, and thus optimise patient care.…”