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

## TL;DR

This paper introduces a deep learning method to automatically detect quiet eye durations in archery using EOG signals, offering a more objective and efficient alternative to manual evaluation.

## Contribution

The novel contribution is the development and comparison of deep learning models for automated, accurate detection of quiet eye durations in archery using EOG data.

## Key findings

- The CNN + LSTM model achieved the highest accuracy (95%) in detecting quiet eye durations from EOG signals.
- Deep learning models outperformed traditional models like SVM in capturing spatial and temporal dependencies in EOG data.
- The proposed automated approach can provide real-time feedback for sports training and has potential for broader cognitive and motor skill assessments.

## Abstract

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 achieved the highest accuracy (95%), followed closely by CNN + GRU (93%), demonstrating superior performance in capturing both spatial and temporal dependencies in the EOG signals. Although Transformer-based and UNet models performed competitively, they exhibited lower precision in distinguishing QE periods. The performance of the traditional model was inferior to deep learning approaches. These results indicate that deep learning provides an effective and scalable solution for objective QE analysis, substantially reducing the dependence on expert annotations. This automated approach can enhance sports training by offering real-time, data-driven feedback to athletes and coaches. Furthermore, the methodology holds promise for broader applications in cognitive and motor skill assessments across various domains. Future work will focus on expanding the dataset, enabling real-time deployment, and evaluating model generalizability across different skill levels and sports disciplines.