Developmental methods and results for a novel virtual reality concussion detection system

Abstract Concussion commonly occurs in sports and military venues and is difficult to diagnose and manage. A medical device is needed to perform an assessment for concussion and identify functional deficits. This report presents the developmental methodology, including user experience and human factors results, for a new virtual reality (VR)-based technology. Data collection occurred between 2020 and 2024, amassing 1008 participants between the ages of 18 and 40 years. The initial software was modified, informed by human factors and user experience data, including symptom report and need for examiner assistance. The second version resulted in less necessity for examiner intervention to ensure participant understanding of the VR tests. Symptom provocation during the VR tests demonstrated that less than 5% of participants had a > 2-point increase in headache, nausea, and mental fogginess across the tests; 9.2% had > 2-point increase in dizziness. This is in line with reports from similar clinical tests, demonstrating that the VR environment does not pose an additional risk. Our work demonstrates that clinical tests of functional neurology (i.e., oculomotor, vestibular, reaction) were translated into a virtual environment and multidimensional sensor-based data was able to be collected on a sample of participants, who did not experience unexpected symptom provocation during test completion. The approach described here regarding the translation of real-world clinical tests into the VR environment, towards the development of a medical device, may be useful to many other healthcare disciplines and fields. Pending results of the machine learning classification, this device may prove useful in decision support for concussion diagnostics and determination of recovery.