Adaptive mixed reality stroke rehabilitation: System architecture and evaluation metrics

This paper presents a novel system architecture and evaluation metrics for an Adaptive Mixed Reality Rehabilitation (AMRR) system for stroke patient. This system provides a purposeful, engaging, hybrid (visual, auditory and physical) scene that encourages patients to improve their performance of a reaching and grasping task and promotes learning of generalizable movement strategies. This system is adaptive in that it provides assistive adaptation tools to help the rehabilitation team customize the training strategy. Our key insight is to combine the patients, rehabilitation team, multimodal hybrid environments and adaptation tools together as an adaptive experiential mixed reality system. There are three major contributions in this paper: (a) developing a computational deficit index for evaluating the patient's kinematic performance and a deficit-training-improvement (DTI) correlation for evaluating adaptive training strategy, (b) integrating assistive adaptation tools that help the rehabilitation team understand the relationship between the patient's performance and training and customize the training strategy, and (c) combining the interactive multimedia environment and physical environment together to encourage patients to transfer movement knowledge from media space to physical space. Our system has been used by two stroke patients for one-month mediated therapy. They have significant improvement in their reaching and grasping performance (+48.84% and +39.29%) compared to other two stroke patients who experienced traditional therapy (-18.31% and -8.06%).