The Principles of advanced virtual reality-based neurorehabilitation how the training in virtual reality and based on principles can support the recovery and diagnosis of disabilities after stroke

Abstract

The increase in stroke survivors poses a global challenge for the current health care system. The way that standard therapy is provided today impacts long-term disability and dependency in ADL insufficiently. The associated need for long-term care and the increase in health-care costs hence demand for novel therapeutic approaches. For this reason, we need to obtain gain a better understanding of the manifold consequences and the recovery process after stroke. In this dissertation we will advance the idea that besides increasing the intensity other factors need to be considered. Rehabilitation must incorporate learning strategies that induce recovery by changing the impaired behaviour. The principles of learning can be obtained from animal and human learning neuroscientific literature. As symptoms are neurologically and behaviourally interrelated, they can be addressed by common learning methods. We argue that technology is an aptly medium to implement and test these methods. Technology-based rehabilitation systems are not only cost-efficient, scalable and accessible, but also allow us to induce virtual manipulations which enhances learning in a way that is not possible in reality. The main goal of this dissertation is to design, test and deliver advanced neuroscience-based therapies in virtual reality that exploit principles of learning. We first offer a synthesis of known principles of learning obtained from human and animal behaviour and show that VR-based systems that incorporate these principles can have a significant impact on recovery. We then explore in three studies how augmented sensorimotor performance, individualized challenges and goal-oriented embodied training in a VR-based rehabilitation system can modify behaviour to address physical, cognitive and social post-stroke consequences. Lastly, we offer two possibilities how the information gained through the VR-based training can help to understand deficits better and therefore complement diagnostics. The contribution of the scientific work presented in this dissertation is that a systematic principle-based approach that augments learning with the advantages of technology can address a variety of post-stroke deficits and advance the understanding of recovery.