Upper-extremity performance assessment using an interactive, personalized computer-assisted neurorehabilitation motivating framework

Xin Feng, Marquette University

Abstract

The disability and aging populations have been increasing during the last decade. In the United States, stroke affects about 5.6 million individuals today; the aging population has grown to about one in every eight Americans. Economic pressure has gradually shifted the burden of rehabilitation to outpatient and home healthcare with limited supervision, creating a need for alternative approaches to neurorehabilitation. These approaches should be cost-effective and accessible for the home environment, while also semi-autonomously providing timely assessment and a greater amount of therapy. In response to this challenge, a computer-assisted motivational neurorehabilitation framework, coined "UniTherapy", has been designed and implemented. It uniquely integrates three core technologies: (i) support for a suite of standard-compliant computer input devices, including force-reflecting joysticks and driving wheels as physical therapeutic interfaces, (ii) support for a suite of personalizable and remotely tunable goal-directed performance assessment and motivational interventional exercise protocols, including features like data archive, management, and analysis tools, and (iii) support for providing personalized user interfaces that are tuned to the abilities and preferences of the user while also supporting emerging user interface and remote access standards. The potential of the framework was evaluated via a suite of collaborative pilot studies. By using the selected goal-directed tasks and kinematic metrics, it was shown that the framework had the capability to differentiate between human subjects with various level of stroke-induced impairment and performance differences under different task settings (e.g. device type, force field settings). Usability data from study subjects, as well as from a focus group consisting of rehabilitation practitioners, suggested that the potential of the framework as a cost-effective, sensor-based assessment tool and a home-based motivational therapy platform. A second study evaluated the movement features of subjects with stroke-induced impairment in the trajectory tracking tasks under different force and tracking speed settings using the UniTherapy platform. Nonlinear effects for the selected kinematic measures confirm the necessity to customize the parameters of the training protocol for each individual client. The force from the conventional joystick is enough to influence the performance of accuracy and stability across subjects. The results also suggest that the selected kinematic metrics can be sensitive clinical measures, yet quick to administer in a simple setup. In summary, a computer-assisted motivational neurorehabilitation framework has been designed and implemented. The results of the evaluation studies had shown its potential as a sensitive upper-extremity assessment tool and a home-based motivating therapy platform. The results from the goal-directed task under various task settings, suggest the necessity to personalize the parameters of the training protocol for each client.

This paper has been withdrawn.