Dyad Learning in Wrist-robotic Environment After Stroke

Plain-language summary

"Robot-assisted rehabilitation is used to mitigate the devastating effects of stroke and to recover the ability to perform complex motor tasks in survivors. One common issue is that robotic devices are costly and only available to individuals in limited capacity. Increasing efficiency by reducing required physical practice time or by improving training gains is therefore crucial to improve outcomes in robot-assisted rehabilitation after stroke. We are aiming to implement dyad learning as a learning paradigm that increases motor learning within a given timeframe for individuals to overcome this issue. Dyad learning is form of sensorimotor learning where participants learn in pairs, alternating between physical and observational practice. Between trials, learners are often allowed to engage in inter-trial dialog. Dyad learning has not been established as a tool in rehabilitation, despite its potential to either reduce required physical practice time or increase motor improvement. The general aim of this project is to determine the usability (Aim 1) and feasibility (Aim 2) of dyad learning in a wrist robot environment in individuals with chronic stroke, as well as transfer effects in two transfer task (Aim 3). Dyad learning is characterized as motor learning where learners alternate between physical and observational training. Physical practice means that a person performs the motor task themselves. Observational practice means that the learner watches someone else practice the motor task. In particular, the specific aims are as follows: Aim 1: To determine if a dyad learning paradigm in the wrist robot environment can be implemented in participants with chronic stroke. This aim seeks to determine the usability of dyad learning of a gamified motor task in the wrist robot environment. Successful completion of the study by participants will verify this aim. Aim 2: To determine the effect of dyad learning to improve wrist joint motor performance within the wrist robot environment. Participants with chronic stroke will be trained in a gamified motor task using a dyad learning paradigm. Motor performance will be measured by a performance motor score, a composite score that comprises spatial and temporal variables. Improvement in the performance motor score at the end of training will verify this aim. Aim 3: To determine if dyadic wrist motor training in the wrist robot environment leads to improvements in the performance of two untrained functional wrist movement tasks in participants with chronic stroke. This aim seeks to identify the transfer effects of dyadic learning-related motor training gains on functional motor performance. Participants will perform two untrained wrist join motor task before and after training. The motor task are line tracing and tracking tasks. Performance will be measured by calculating the root mean square error (RMSE), measuring how much the participant deviates from the line, as well as time-to-complete in seconds. A decrease in RMSE and/or in time-to-complete at the second visit compared to the first performance will verify this aim.

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