A quality of performance model for evaluating post-stroke patients

Abstract

Augmented reality (AR) has recently emerged as an assistive tool for effective diagnosis and rehabilitation intervention. However, measuring the quality of performance (QoP) of patients has gained limited attention from the research community. The objective of this paper is to propose and test a evaluation taxonomy for an AR-based stroke patient rehabilitation system that is currently under development at the MCRlab, University of Ottawa. The taxonomy is modeled using a fuzzy logic inference (FLI) system to quantitatively measure the QoP of the patient and eventually provide the therapist with discrete recommendation regarding the progress of patient treatment.