Isayas Berhe Adhanom

Khondker Fariha Hossain, Sharif Amit Kamran, Prithul Sarker et al.

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.

Prithul Sarker, Khondker Fariha Hossain, Isayas Berhe Adhanom et al.

The sport-related concussion (SRC) battery relies heavily upon subjective symptom reporting in order to determine the diagnosis of a concussion. Unfortunately, athletes with SRC may return-to-play (RTP) too soon if they are untruthful of their symptoms. It is critical to provide accurate assessments that can overcome underreporting to prevent further injury. To lower the risk of injury, a more robust and precise method for detecting concussion is needed to produce reliable and objective results. In this paper, we propose a novel approach to detect SRC using long short-term memory (LSTM) recurrent neural network (RNN) architectures from oculomotor data. In particular, we propose a new error metric that incorporates mean squared error in different proportions. The experimental results on the smooth pursuit test of the VR-VOMS dataset suggest that the proposed approach can predict concussion symptoms with higher accuracy compared to symptom provocation on the vestibular ocular motor screening (VOMS).