CLASSIFICATION AND CLUSTERING OF BRAIN INJURIES FROM MOTION DATA OF PATIENTS IN A VIRTUAL REALITY ENVIRONMENT

Virtual Reality (VR) has been found to be an effective rehabilitation tool for brain injury patients. We show that motion data from these VR sessions can be effectively used to both cluster and classify patients according to types of injury. Neural Network and other tools were used to differentially classify patients with traumatic brain injury, cerebral vascular accident (stroke) with and without spatial neglect and healthy individuals solely from the motion data. Clustering techniques also successfully duplicated the classification division. These results have potential implications for scientific research, automated diagnosis and integrated individually adaptive therapies in the virtual reality technology.