A System for Real-Time Feedback to Improve Gait and Posture in Parkinson's Disease.

For people with Parkinson's disease (PD), gait and postural impairments can significantly affect their ability to perform activities of daily living. Presentation of appropriate cues has been shown to improve gait in PD. Based on this, a treadmill-based system and experimental paradigm were developed to determine if people with PD can utilize real-time feedback (RTFB) of step length or back angle (uprightness) to improve gait and posture. Eleven subjects (mean age 67 ± 8 years) with mild-to-moderate PD (Hoehn and Yahr stage I-III) were evaluated regarding their ability to successfully utilize RTFB of back angle or step length during quiet standing and treadmill walking tasks during a single session in their medication-on state. Changes in back angle and step length due to feedback were compared using Friedman nonparametric tests with Wilcoxon Signed-Rank tests for post-hoc comparisons. Improvements in uprightness were observed as an increase in back angle during quiet standing (p = 0.005) and during treadmill walking (p = 0.005) with back angle feedback when compared to corresponding tasks without feedback. Improvements in gait were also observed as an increase in step length (p = 0.005) during step length feedback compared to tasks without feedback. These results indicate that people with mild-to-moderate PD can utilize RTFB to improve upright posture and gait. Future work will investigate the long-term effects of this RTFB paradigm and the development of systems for clinical or home-based use.

Interdisciplinary concepts for design and implementation of mixed reality interactive neurorehabilitation systems for stroke.

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.

Design of a home-based adaptive mixed reality rehabilitation system for stroke survivors.

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.

Interactive multimodal biofeedback for task-oriented neural rehabilitation.

Previous studies have suggested that task-oriented biofeedback training may be effective for functional motor improvement. The purpose of this project was to design an interactive, multimodal biofeedback system for the task-oriented training of goal-directed reaching. The central controller, based on a user context model, identifies the state of task performance using multisensing data and provides augmented feedback, through interactive 3D graphics and music, to encourage the patients' self-regulation and performance of the task. The design allows stroke patients to train with functional tasks, and receive real-time performance evaluation through successful processing of multimodal sensory feedback. In addition, the environment and training task is customizable. Overall, the system delivers an engaging training experience. Preliminary results of a pilot study involving stroke patients demonstrate the potential of the system to improve patients' reaching performance.