Reanalyzing Effective Eye-related Information for Developing User’s Intent Detection Systems
Article No.: 71, Pages 1 - 3
Abstract
Studies on gaze-based interactions have utilized natural eye-related information to detect user intent. Most use a machine learning-based approach to minimize the cost of choosing appropriate eye-related information. While those studies demonstrated the effectiveness of an intent detection system, understanding which eye-related information is useful for interactions is important. In this paper, we reanalyze how eye-related information affected the detection performance of a previous study to develop better intent detection systems in the future. Specifically, we analyzed two aspects of dimensionality reduction and adaptation to different tasks. The results showed that saccade and fixation are not always useful, and the direction of gaze movement could potentially cause overfitting.
References
[1]
Roman Bednarik, Hana Vrzakova, and Michal Hradis. 2012. What Do You Want to Do Next: A Novel Approach for Intent Prediction in Gaze-based Interaction. In Proceedings of the 2012 ACM Symposium on Eye Tracking Research & Applications (Santa Barbara, California) (ETRA ’12). Association for Computing Machinery, New York, NY, USA, 83–90. https://doi.org/10.1145/2168556.2168569
[2]
Brendan David-John, Candace Peacock, Ting Zhang, T. Scott Murdison, Hrvoje Benko, and Tanya R. Jonker. 2021. Towards Gaze-Based Prediction of the Intent to Interact in Virtual Reality. In ACM Symposium on Eye Tracking Research and Applications (Virtual Event, Germany) (ETRA ’21 Short Papers). Association for Computing Machinery, New York, NY, USA, Article 2, 7 pages. https://doi.org/10.1145/3448018.3458008
[3]
Toshiya Isomoto, Shota Yamanaka, and Buntarou Shizuki. 2022. Dwell Selection with ML-Based Intent Prediction Using Only Gaze Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 3, Article 120 (Sep 2022), 21 pages. https://doi.org/10.1145/3550301
[4]
Candace E. Peacock, Ben Lafreniere, Ting Zhang, Stephanie Santosa, Hrvoje Benko, and Tanya R. Jonker. 2022. Gaze as an Indicator of Input Recognition Errors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, ETRA, Article 142 (May 2022), 18 pages. https://doi.org/10.1145/3530883
[5]
Naveen Sendhilnathan, Ting Zhang, Ben Lafreniere, Tovi Grossman, and Tanya R. Jonker. 2022. Detecting Input Recognition Errors and User Errors Using Gaze Dynamics in Virtual Reality. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22). Association for Computing Machinery, New York, NY, USA, Article 38, 19 pages. https://doi.org/10.1145/3526113.3545628
Index Terms
- Reanalyzing Effective Eye-related Information for Developing User’s Intent Detection Systems
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May 2023
441 pages
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Published: 30 May 2023
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ETRA '23: 2023 Symposium on Eye Tracking Research and Applications
May 30 - June 2, 2023
Tubingen, Germany
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