Virtual Reality and Games for Physical Rehabilitation

Upper or lower limb rehabilitation (rehab) is commonly required in various health conditions. For example, post-Stroke, post-Cardiac arrest, Multiple Sclerosis, Cerebral Palsy, Phantom Limb, Brain Injury, and post-Breast Cancer Surgery. Frequent, intense physiotherapy and/or occupational therapy can have a significant positive impact on quality of life and can allow a person to participate more fully in activities of daily living. However, there are several common challenges for patients in maintaining high-quality physiotherapy exercise programmes: It is particularly difficult to maintain high-quality/intensity exercise at home without a therapist. Motivation and engagement are key problems.

Compliance with rehab within a person’s home is typically self-reported and often inaccurate. In many cases, a person’s impairment is such that they cannot use complex equipment. Previous research at Ulster has investigated solutions to these issues [1-15], including games, as well as augmented and virtual reality, to guide and engage patients through their rehabilitation journey, multi-user social environments to increase motivation with physiotherapy, and enhanced upper limb tracking and hand gesture recognition for natural hand movement as game controllers.

This new interdisciplinary PhD will further this research by exploring the following areas: The use of immersive technologies and social activities to increase engagement with physical rehabilitation tasks. The development of state-of-the-art hand movement and position/gesture models using machine learning and exploring synthetic hand data for boosting recognition. Enhanced movement prediction and modelling with minimal sensors. The research will involve working with Health Science colleagues in Ulster and at other global health technology research centres. ​The project will benefit significantly from the new Automata Lab at Ulster, a state-of-the-art multi-agent testing facility for immersive interaction analytics equipped with technologies for capturing and analysing natural human movement, emotion analysis, psychometrics, predictive user modelling, and complex systems simulation