Interactive neurorehabilitation (INR) systems provide therapy that can evaluate and deliver feedback on a patient's movement computationally. There are currently many approaches to INR design and implementation, without a clear indication of which methods to utilize best. This article presents key interactive computing, motor learning, and media arts concepts utilized by an interdisciplinary group to develop adaptive, mixed reality INR systems for upper extremity therapy of patients with stroke. Two INR systems are used as examples to show how the concepts can be applied within: (1) a small-scale INR clinical study that achieved integrated improvement of movement quality and functionality through continuously supervised therapy and (2) a pilot study that achieved improvement of clinical scores with minimal supervision. The notion is proposed that some of the successful approaches developed and tested within these systems can form the basis of a scalable design methodology for other INR systems. A coherent approach to INR design is needed to facilitate the use of the systems by physical therapists, increase the number of successful INR studies, and generate rich clinical data that can inform the development of best practices for use of INR in physical therapy.
This paper proposes a computational framework for movement quality assessment using a decision tree model that can potentially assist a physical therapist in a telerehabilitation context. Using a dataset of key kinematic attributes collected from eight stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment of a reach-to-grasp cone task, an activity commonly used in upper extremity stroke rehabilitation therapy. The proposed framework is capable of providing movement quality scores that are highly correlated to the ratings provided by therapists, who used a custom rating rubric created by rehabilitation experts. Our hypothesis is that a decision tree model could be easily utilized by therapists as a potential assistive tool, especially in evaluating movement quality on a large-scale dataset collected during unsupervised rehabilitation (e.g., training at the home), thereby reducing the time and cost of rehabilitation treatment.
This paper presents the design of a home-based adaptive mixed reality system (HAMRR) for upper extremity stroke rehabilitation. The goal of HAMRR is to help restore motor function to chronic stroke survivors by providing an engaging long-term reaching task therapy at home. The system uses an intelligent adaptation scheme to create a continuously challenging and unique multi-year therapy experience. The therapy is overseen by a physical therapist, but day-to-day use of the system can be independently set up and completed by a stroke survivor. The HAMMR system tracks movement of the wrist and torso and provides real-time, post-trial, and post-set feedback to encourage the stroke survivor to self-assess his or her movement and engage in active learning of new movement strategies. The HAMRR system consists of a custom table, chair, and media center, and is designed to easily integrate into any home.
In this paper, we propose a general framework for tuning component-level kinematic features using therapists’ overall impressions of movement quality, in the context of a Home-based Adaptive Mixed Reality Rehabilitation (HAMRR) system. We propose a linear combination of non-linear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overall movement quality provided by a therapist. The kinematic features are chosen such that they correlate with the quality of wrist movements to clinical assessment scores. Further, the proposed features are designed to be reliably extracted from an inexpensive and portable motion capture system using a single reflective marker on the wrist. Using a dataset collected from ten stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment in HAMRR systems. The system is currently being deployed for large-scale evaluations, and will represent an increasingly important application area of motion capture and activity analysis.
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