Frontiers in Human Neuroscience (Aug 2022)
Automating provision of feedback to stroke patients with and without information on compensatory movements: A pilot study
Abstract
Providing effective feedback to patients in a rehabilitation training program is essential. As technologies are being developed to support patient training, they need to be able to provide the users with feedback on their performance. As there are various aspects on which feedback can be given (e.g., task success and presence of compensatory movements), it is important to ensure that users are not overwhelmed by too much information given too frequently by the assistive technology. We created a rule-based set of guidelines for the desired hierarchy, timing, and content of feedback to be used when stroke patients train with an upper-limb exercise platform which we developed. The feedback applies to both success on task completion and to the execution of compensatory movements, and is based on input collected from clinicians in a previous study. We recruited 11 stroke patients 1–72 months from injury onset. Ten participants completed the training; each trained with the rehabilitation platform in two configurations: with motor feedback (MF) and with no motor feedback (control condition) (CT). The two conditions were identical, except for the feedback content provided: in both conditions they received feedback on task success; in the MF condition they also received feedback on making undesired compensatory movements during the task. Participants preferred the configuration that provided feedback on both task success and quality of movement (MF). This pilot experiment demonstrates the feasibility of a system providing both task-success and movement-quality feedback to patients based on a decision tree which we developed.
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