Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study
<b>Introduction:</b> This study aimed to develop and validate an efficient eye-tracking algorithm suitable for the analysis of images captured in the visible-light spectrum using a smartphone camera. <b>Methods:</b> The investigation primarily focused on comparing two algorit...
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MDPI AG
2025-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/12/1446 |
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| author | Wanzi Su Damon Hoad Leandro Pecchia Davide Piaggio |
| author_facet | Wanzi Su Damon Hoad Leandro Pecchia Davide Piaggio |
| author_sort | Wanzi Su |
| collection | DOAJ |
| description | <b>Introduction:</b> This study aimed to develop and validate an efficient eye-tracking algorithm suitable for the analysis of images captured in the visible-light spectrum using a smartphone camera. <b>Methods:</b> The investigation primarily focused on comparing two algorithms, which were named CHT_TM and CHT_ACM, abbreviated from the core functions: Circular Hough Transform (CHT), Active Contour Models (ACMs), and Template Matching (TM). <b>Results:</b> CHT_TM significantly improved the running speed of the CHT_ACM algorithm, with not much difference in the resource consumption, and improved the accuracy on the x axis. CHT_TM achieved a reduction by 79% of the execution time. CHT_TM performed with an average mean percentage error of 0.34% and 0.95% in the x and y direction across the 19 manually validated videos, compared to 0.81% and 0.85% for CHT_ACM. Different conditions, like manually opening the eyelids with a finger versus without a finger, were also compared across four different tasks. <b>Conclusions:</b> This study shows that applying TM improves the original eye-tracking algorithm with CHT_ACM. The new algorithm has the potential to help the tracking of eye movement, which can facilitate the early screening and diagnosis of neurodegenerative diseases. |
| format | Article |
| id | doaj-art-2e84edae585546aaacaa9353c14aef1b |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-2e84edae585546aaacaa9353c14aef1b2025-08-20T02:24:38ZengMDPI AGDiagnostics2075-44182025-06-011512144610.3390/diagnostics15121446Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot StudyWanzi Su0Damon Hoad1Leandro Pecchia2Davide Piaggio3School of Engineering, University of Warwick, Library Road, Coventry CV4 7AL, UKWarwick Medical School, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UKDepartment of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, 00128 Rome, ItalySchool of Engineering, University of Warwick, Library Road, Coventry CV4 7AL, UK<b>Introduction:</b> This study aimed to develop and validate an efficient eye-tracking algorithm suitable for the analysis of images captured in the visible-light spectrum using a smartphone camera. <b>Methods:</b> The investigation primarily focused on comparing two algorithms, which were named CHT_TM and CHT_ACM, abbreviated from the core functions: Circular Hough Transform (CHT), Active Contour Models (ACMs), and Template Matching (TM). <b>Results:</b> CHT_TM significantly improved the running speed of the CHT_ACM algorithm, with not much difference in the resource consumption, and improved the accuracy on the x axis. CHT_TM achieved a reduction by 79% of the execution time. CHT_TM performed with an average mean percentage error of 0.34% and 0.95% in the x and y direction across the 19 manually validated videos, compared to 0.81% and 0.85% for CHT_ACM. Different conditions, like manually opening the eyelids with a finger versus without a finger, were also compared across four different tasks. <b>Conclusions:</b> This study shows that applying TM improves the original eye-tracking algorithm with CHT_ACM. The new algorithm has the potential to help the tracking of eye movement, which can facilitate the early screening and diagnosis of neurodegenerative diseases.https://www.mdpi.com/2075-4418/15/12/1446eye-trackingeye movementneurodegenerative conditionbiosignal processing |
| spellingShingle | Wanzi Su Damon Hoad Leandro Pecchia Davide Piaggio Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study Diagnostics eye-tracking eye movement neurodegenerative condition biosignal processing |
| title | Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study |
| title_full | Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study |
| title_fullStr | Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study |
| title_full_unstemmed | Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study |
| title_short | Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study |
| title_sort | validation of an eye tracking algorithm based on smartphone videos a pilot study |
| topic | eye-tracking eye movement neurodegenerative condition biosignal processing |
| url | https://www.mdpi.com/2075-4418/15/12/1446 |
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