Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models
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 de...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | BMC Sports Science, Medicine and Rehabilitation |
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| Online Access: | https://doi.org/10.1186/s13102-025-01284-2 |
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| author | Fatma Söğüt Hüseyin Yanık Evren Değirmenci İnci Kesilmiş Ülkü Çömelekoğlu |
| author_facet | Fatma Söğüt Hüseyin Yanık Evren Değirmenci İnci Kesilmiş Ülkü Çömelekoğlu |
| author_sort | Fatma Söğüt |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc7bfca3dc3e4935b3a8bca6bc21a14b |
| institution | Kabale University |
| issn | 2052-1847 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Sports Science, Medicine and Rehabilitation |
| spelling | doaj-art-dc7bfca3dc3e4935b3a8bca6bc21a14b2025-08-20T03:42:52ZengBMCBMC Sports Science, Medicine and Rehabilitation2052-18472025-08-0117111910.1186/s13102-025-01284-2Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning modelsFatma Söğüt0Hüseyin Yanık1Evren Değirmenci2İnci Kesilmiş3Ülkü Çömelekoğlu4Vocational School of Health Service, Mersin UniversityInformation Systems and Technologies, Mersin UniversityDepartment of Electrical and Electronics Engineering, Faculty of Engineering, Mersin UniversityDepartment of Coaching Education, Faculty of Sports Science, Mersin UniversityDepartment of Biophysics, Faculty of Medicine, Mersin UniversityAbstract 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.https://doi.org/10.1186/s13102-025-01284-2Quiet eyeElectrooculographyWavelet transformConvolutional neural networksLong-short term memoryTransformer |
| spellingShingle | Fatma Söğüt Hüseyin Yanık Evren Değirmenci İnci Kesilmiş Ülkü Çömelekoğlu Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models BMC Sports Science, Medicine and Rehabilitation Quiet eye Electrooculography Wavelet transform Convolutional neural networks Long-short term memory Transformer |
| title | Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models |
| title_full | Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models |
| title_fullStr | Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models |
| title_full_unstemmed | Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models |
| title_short | Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models |
| title_sort | automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models |
| topic | Quiet eye Electrooculography Wavelet transform Convolutional neural networks Long-short term memory Transformer |
| url | https://doi.org/10.1186/s13102-025-01284-2 |
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