  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).
