Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria
Through a novel, multi-sensor platform in a wearable glasses form factor, paired with crowd-sourced data collection, we have enabled the collection of tens of thousands of egocentric gaze recordings across hundreds of daily tasks without the limitations posed by traditional eye-tracking or lab resea...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11052289/ |
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| author | Yusuf Mansour Ajoy Savio Fernandes Kiran Somasundaram Tarek Hefny Mahsa Shakeri Oleg V. Komogortsev Abhishek Sharma Michael J. Proulx |
| author_facet | Yusuf Mansour Ajoy Savio Fernandes Kiran Somasundaram Tarek Hefny Mahsa Shakeri Oleg V. Komogortsev Abhishek Sharma Michael J. Proulx |
| author_sort | Yusuf Mansour |
| collection | DOAJ |
| description | Through a novel, multi-sensor platform in a wearable glasses form factor, paired with crowd-sourced data collection, we have enabled the collection of tens of thousands of egocentric gaze recordings across hundreds of daily tasks without the limitations posed by traditional eye-tracking or lab research. The eye tracking model that we describe and open source here was created with a large study of 1500 participants performing eye tracking calibration. Data collection utilized the device sensor suite, allowing us to leverage scene and contextual information across various environments and tasks. This paper will discuss the systems in place and technical steps we have taken to enable eye tracking for the project, and select lessons learned in the process. This large-scale data collection tool and open source eye tracking model will enable advances in mobile and pervasive eye tracking to understand nuances in eye movements and various future AI and eye-tracking applications. |
| format | Article |
| id | doaj-art-1f5b85fcb8034004b86ec9c14e4aed8f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1f5b85fcb8034004b86ec9c14e4aed8f2025-08-20T02:35:59ZengIEEEIEEE Access2169-35362025-01-011311473611474510.1109/ACCESS.2025.358362311052289Enabling Eye Tracking for Crowd-Sourced Data Collection With Project AriaYusuf Mansour0Ajoy Savio Fernandes1Kiran Somasundaram2https://orcid.org/0000-0001-8554-9083Tarek Hefny3Mahsa Shakeri4Oleg V. Komogortsev5Abhishek Sharma6Michael J. Proulx7https://orcid.org/0000-0003-4066-3645Meta Reality Labs Research, Redmond, WA, USAMeta Reality Labs Research, Redmond, WA, USAMeta Reality Labs Research, Redmond, WA, USAMeta Reality Labs Research, Redmond, WA, USAMeta Reality Labs, Redmond, WA, USAMeta Reality Labs Research, Redmond, WA, USAMeta Reality Labs Research, Redmond, WA, USAMeta Reality Labs Research, Redmond, WA, USAThrough a novel, multi-sensor platform in a wearable glasses form factor, paired with crowd-sourced data collection, we have enabled the collection of tens of thousands of egocentric gaze recordings across hundreds of daily tasks without the limitations posed by traditional eye-tracking or lab research. The eye tracking model that we describe and open source here was created with a large study of 1500 participants performing eye tracking calibration. Data collection utilized the device sensor suite, allowing us to leverage scene and contextual information across various environments and tasks. This paper will discuss the systems in place and technical steps we have taken to enable eye tracking for the project, and select lessons learned in the process. This large-scale data collection tool and open source eye tracking model will enable advances in mobile and pervasive eye tracking to understand nuances in eye movements and various future AI and eye-tracking applications.https://ieeexplore.ieee.org/document/11052289/Contextual AIegocentric perceptioneye trackinghuman-computer interactionmachine learningpervasive computing |
| spellingShingle | Yusuf Mansour Ajoy Savio Fernandes Kiran Somasundaram Tarek Hefny Mahsa Shakeri Oleg V. Komogortsev Abhishek Sharma Michael J. Proulx Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria IEEE Access Contextual AI egocentric perception eye tracking human-computer interaction machine learning pervasive computing |
| title | Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria |
| title_full | Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria |
| title_fullStr | Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria |
| title_full_unstemmed | Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria |
| title_short | Enabling Eye Tracking for Crowd-Sourced Data Collection With Project Aria |
| title_sort | enabling eye tracking for crowd sourced data collection with project aria |
| topic | Contextual AI egocentric perception eye tracking human-computer interaction machine learning pervasive computing |
| url | https://ieeexplore.ieee.org/document/11052289/ |
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