Analysis of Smooth Pursuit Assessment in Virtual Reality and Concussion Detection using BiLSTM
This work addresses the need for objective concussion detection in athletes to prevent injury from underreporting, representing a domain-specific incremental improvement.
The paper tackled the problem of subjective symptom reporting in sport-related concussion diagnosis by proposing a novel LSTM-based approach using oculomotor data, achieving higher accuracy in predicting concussion symptoms compared to symptom provocation on the vestibular ocular motor screening.
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).