Erica L. Waters

Purpose: Robot-assisted therapy predominantly targets motor impairment, although post-stroke motor and cognitive impairments co-occur. We report user experience in a longitudinal deployment of a Rehab Community-based Affordable Robot Exercise System (RehabCARES) for delivering personalized motor/cognitive training. Methods: Six adults with chronic stroke (FM-UE 20-61; MoCA 22-28) completed 12 therapy visits with THEAbot, a reconfigurable 1-DoF robot. Rehab CARES also included a haptic/biometric dynamic difficulty adjustment controller and eight games characterized by cognitive processes and motor/cognitive demand. We report gaming preference, usage, and user experience (pain, exertion, usability, affect, workload) across therapy. Results: User experience varied, but generally, self-reported pain was low, perceived exertion was light to moderate, enjoyment was strong and stable, and sense of control increased while excitement plateaued. Workload showed decreasing frustration and effort. Game selection during therapy was partially consistent with motor/cognitive clinical profiles; preferences and perceptual abilities may have influenced choices. Usability scores exceeded acceptability at all time points, peaking at the final visit (86 +/- 4.5 out of 100). Conclusions: RehabCARES provided personalized motor/cognitive training to a heterogeneous cohort in a community-based setting, delivering a positive user experience. Hardware reconfigurability and motor/cognitive game variety likely enabled this success.

Background: Following stroke, motor and cognitive impairments often limit functional independence. Personalized rehabilitation requires comprehensive clinical assessments, which are costly and time-consuming. Imaging scans are routinely collected, but their predictive value in determining post-stroke function remains unclear. Objectives: To determine the ability of clinical assessments, lesion features with medical chart information, and a combined clinical and biomedical model to predict kinematic outcomes from a robot-based tracking task in chronic stroke individuals. Methods: Eighteen participants (70±6 years) completed a robot-based tracking task and a battery of motor and cognitive assessments. Three elastic net regression models were developed: 1) clinical assessments only; 2) lesion metrics and chart information only; and 3) a combined model. Models were evaluated using leave-one-subject-out cross-validation, bootstrapping, and permutation testing with false discovery rate correction. Robot-based outcomes included tracking error, normalized distance, velocity error response, and delay. Results: The clinical assessment model significantly predicted tracking error (R²=0.45), delay (R²=0.22), velocity error response (R²=0.18), and normalized distance (R²=0.12). The combined model significantly predicted tracking error (R²=0.37), delay (R²=0.19), and velocity error response (R²=0.11). The imaging model did not significantly predict any outcome. Box and Blocks Test and Color Trails 1 were stable features. The right dorsal attention network was a stable predictor in several combined models. Conclusion: Clinical assessments outperformed lesion features in predicting tracking performance after chronic stroke. Attention- and motor-related measures were the strongest predictors, highlighting the importance of cognitive-motor interactions in function. A larger sample and more granular lesion features may improve prediction from imaging biomarkers.

Rehabilitation robots have the potential to alleviate the global burden of neurorehabilitation. Robot-based multiplayer gaming with virtual and haptic interaction may improve motivation, engagement, and implicit learning in robotic therapy. Over the past few years, there has been growing interest in robot mediated haptic dyads, or human-robot-robot-human interaction. The effect of such a paradigm on motor learning in general and specifically for individuals with motor and/or cognitive impairments is an open area of research. We reviewed the literature to investigate the effect of a robot-based haptic dyad on motor learning. Thirty-eight articles met the inclusion criteria for this review. We summarize study characteristics including device, haptic rendering, and experimental task. Our main findings indicate that dyadic training's impact on motor learning is inconsistent in that some studies show significant improvement of motor training while others show no influence. We also find that the relative skill level of the partner and interaction characteristics such as stiffness of connection and availability of visual information influence motor learning outcomes. We discuss implications for neurorehabilitation and conclude that additional research is needed to determine optimal interaction characteristics for motor learning and to extend this research to individuals with cognitive and motor impairments.