Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models

Söğüt, Fatma | Yanık, Hüseyin | Değirmenci, Evren | Kesilmiş, İnci | Çömelekoğlu, Ülkü

This study presents a deep learning-based approach for the automated detection of Quiet Eye (QE) durations from electrooculography (EOG) signals in archery. QE—the final fixation or tracking of the gaze before executing a motor action—is a critical factor in precision sports. Traditional detection methods, which rely on expert evaluations, are inherently subjective, time-consuming, and inconsistent. To overcome these limitations, EOG data were collected from 10 licensed archers during controlled shooting sessions and preprocessed using a wavelet transform and a Butterworth bandpass filter for noise reduction. We implemented and compared a traditional model (SVM) and five deep learning models—CNN+LSTM, CNN+GRU, Transformer, UNet, and 1D CNN—for QE detection. The CNN+LSTM model achiev...