VR, Eye Tracking & Machine Learning based Vestibular/Ocular Motor Screening

Mild Traumatic Brain Injuries (mTBI), also known as concussions, remain an active major public health issue that affects all levels of participation in sport and recreational activities with an average of 1.6 to 3.8 million occurrences each year. A higher risk of traumatic brain injury (TBI) is linked with many of these activities. In particular TBI affects an estimated 1.7 million people every year in the United States according to the estimation of the US Centers for Disease Control and Prevention (CDC). Sport-related concussions (SRCs) are a variety of injuries that produce transitory neurological impairment.

Assessment of concussion is challenging as it requires recognizing the post-injury symptoms rapidly. The available diagnostic tests for concussion often rely on the athlete’s self-reported symptoms, and sometimes the symptoms go unrecognized, resulting in misleading outcomes. Additionally, the physicians’ concussion assessment is sometimes influenced by self-reported bias. Even the most extensively used tests to detect concussion such as SCAT5 are not always accurate.

In this project, I am part of a multi-disciplinary team that is developing VR, eye tracking and machine learning based automated diagnostic tools for sports related concussion. We are developing a test battery that would objectively measure SRC based on the users real-time ocular (eye movements) and vestibular (head movements) signals.