Nystagmus Detection Method Based on Gating Mechanism and Attention Mechanism

Abstract In this paper, a new model based on the combination of improved LSTM and self-attention mechanism is studied for the detection of nystagmus caused by vestibular illusion in pilots during flight. An efficient and robust nystagmus detection method was proposed by constructing experimental simulation scenarios and collecting and analyzing pilot eye movement data. The improved LSTM model enhances the ability of capturing the medium and long term dependence of the ocular shock sequence by adding a gating unit, and the introduction of self-attention mechanism further improves the analytical accuracy of the model for complex eye movement sequences. The experimental results show that the model has excellent performance in accuracy, recall rate and F1 score, which is significantly better than the traditional model, providing a new technical means for the detection of vestibular illusion.The LSTM-GRU-Attention model has been experimentally verified to perform best in accuracy, recall, and F1 score, reaching 095, 0.91, and 0.93 respectively, indicating that the outperforms the other two models in overall classification performance, positive sample recognition ability, and balance between accuracy and recall.