论文标题
使用BilstM对虚拟现实和脑震荡检测中的平滑追求评估分析
Analysis of Smooth Pursuit Assessment in Virtual Reality and Concussion Detection using BiLSTM
论文作者
论文摘要
与运动有关的脑震荡（SRC）电池在很大程度上依赖于主观症状报告，以确定脑震荡的诊断。不幸的是，如果SRC的运动员不正确地出现SRC（RTP），则可能过早。至关重要的是，提供准确的评估，这些评估可以克服不足的报告以防止进一步的伤害。为了降低受伤的风险，需要一种更健壮，更精确的检测脑震荡方法来产生可靠和客观的结果。在本文中，我们提出了一种新的方法，使用眼动数据中的长期短期记忆（LSTM）复发性神经网络（RNN）架构检测SRC。特别是，我们提出了一个新的误差度量，该指标以不同比例的均值合并平方误差。与前庭眼运动筛查（VOM）症状相比，对VR-VOMS数据集的平滑追击测试的实验结果表明，所提出的方法可以预测具有更高准确性的脑震荡症状。
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).
