Authors:
Uri Feintuch
1
;
Larry Manevitz
2
and
Natan Silnitsky
2
Affiliations:
1
School of Occupational Therapy, Hadassah- Hebrew University Medical Center; Caesarea Rothschild Institute for Interdisciplinary Applications of Computer Science and University of Haifa, Israel
;
2
University of Haifa, Israel
Keyword(s):
Neglect, CVA, TBI, Classification, Clustering, Neural Networks, VR.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
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.