Virtual-Reality based Vestibular Ocular Motor Screening for Concussion Detection using Machine-Learning

TL;DR

This study leverages virtual reality and machine learning to standardize and improve concussion detection through Vestibular Ocular Motor Screening, achieving near-perfect accuracy compared to traditional methods.

Contribution

It introduces a VR-based standardized VOMS assessment combined with machine learning models for highly accurate concussion detection.

Findings

VR data for smooth pursuit and VMS tests are highly reliable for concussion detection

Machine learning models achieved around 99.9% true positive rate

VR-based VOMS outperforms traditional manual assessments

Abstract

Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. At the same time, the current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. Therefore, for the assessment and management of concussion detection, standardization is required to lower the risk of injury and increase the validation among clinicians. With the advancement of technology, virtual reality (VR) can be utilized to advance the standardization of the VOMS, increasing the accuracy of testing administration and decreasing overall false positive rates. In this paper, we experimented with multiple machine learning methods to detect SRC on VR-generated data using VOMS. In our observation, the data generated from VR for smooth pursuit (SP) and the Visual Motion Sensitivity (VMS) tests are highly reliable for concussion detection. Furthermore, we train and evaluate these models, both qualitatively and quantitatively. Our findings show these models can reach high true-positive-rates of around 99.9 percent of symptom provocation on the VR stimuli-based VOMS vs. current clinical manual VOMS.