Toshiya Isomoto
Buntarou Shizuki
Skip Abstract Section
Skip Supplemental Material SectionAbstract
We developed a dwell selection system with ML-based prediction of a user's intent to select. Because a user perceives visual information through the eyes, precise prediction of a user's intent will be essential to the establishment of gaze-based interaction. Our system first detects a dwell to roughly screen the user's intent to select and then predicts the intent by using an ML-based prediction model. We created the intent prediction model from the results of an experiment with five different gaze-only tasks representing everyday situations. The intent prediction model resulted in an overall area under the curve (AUC) of the receiver operator characteristic curve of 0.903. Moreover, it could perform independently of the user (AUC=0.898) and the eye-tracker (AUC=0.880). In a performance evaluation experiment with real interactive situations, our dwell selection method had both higher qualitative and quantitative performance than previously proposed dwell selection methods.
Supplemental Material
Available for Download
zip
isomoto.zip (52.1 MB)
Supplemental movie, appendix, image and software files for, Dwell Selection with ML-based Intent Prediction Using Only Gaze Data
- Sunggeun Ahn, Stephanie Santosa, Mark Parent, Daniel Wigdor, Tovi Grossman, and Marcello Giordano. 2021. StickyPie: A Gaze-Based, Scale-Invariant Marking Menu Optimized for AR/VR. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 739, 16 pages. https://doi.org/10.1145/3411764.3445297Google Scholar
Digital Library
- Sunggeun Ahn, Jeongmin Son, Sangyoon Lee, and Geehyuk Lee. 2020. Verge-It: Gaze Interaction for a Binocular Head-Worn Display Using Modulated Disparity Vergence Eye Movement. In Proceedings of the 2020 CHI Extended Abstracts on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA '20). Association for Computing Machinery, New York, NY, USA, 264:1-7. https://doi.org/10.1145/3334480.3382908Google ScholarDigital Library
- Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-Generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD '19). Association for Computing Machinery, New York, NY, USA, 2623--2631. https://doi.org/10.1145/3292500.3330701Google ScholarDigital Library
- Borji Ali and Itti Laurent. 2013. State-of-the-Art in Visual Attention Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013), 185--207.Google ScholarDigital Library
- 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/10.1145/2168556.2168569Google ScholarDigital Library
- James E. Birren, Roland C. Casperson, and Jack Botwinick. 1950. Age Changes in Pupil Size. Journal of Gerontology 5, 3 (07 1950), 216--221. https://doi.org/10.1093/geronj/53.216 arXiv:https://academic.oup.com/geronj/article-pdf/5/3/216/1647183/5-3-216.pdfGoogle ScholarCross Ref
- Andrew P. Bradley. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 7 (1997), 1145--1159. https://doi.org/10.1016/S0031-3203(96)00142-2Google ScholarCross Ref
- John Brooke. 1996. Usability Evaluation in Industry. CRC Press, Chapter SUS-A Quick and Dirty Usability Scale, 189--194.Google Scholar
- Çağla Çiğ Karaman and Tevfik Metin Sezgin. 2018. Gaze-based predictive user interfaces: Visualizing user intentions in the presence of uncertainty. International Journal of Human-Computer Studies 111 (2018), 78--91. https://doi.org/10.1016/j.ijhcs.2017.11.005Google ScholarDigital Library
- Ishan Chatterjee, Robert Xiao, and Chris Harrison. 2015. Gaze+Gesture: Expressive, Precise and Targeted Free-Space Interactions. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (Seattle, Washington, USA) (ICMI '15). Association for Computing Machinery, New York, NY, USA, 131--138. https://doi.org/10.1145/2818346.2820752Google ScholarDigital Library
- Myungguen Choi, Daisuke Sakamoto, and Tetsuo Ono. 2020. Bubble Gaze Cursor + Bubble Gaze Lens: Applying Area Cursor Technique to Eye-Gaze Interface. In ACM Symposium on Eye Tracking Research and Applications (Stuttgart, Germany) (ETRA '20). Association for Computing Machinery, New York, NY, USA, Article 11, 10 pages. https://doi.org/10.1145/3379155.3391322Google ScholarDigital Library
- Manfred Clynes. 1962. The Non-Linear Biological Dynamics of Unidirectional Rate Sensitivity Illustrated by Analog Computer Analysis, Pupillary Reflex to Light and Sound, and Heart Rate Behavior. Annals of the New York Academy of Sciences 98, 4 (1962), 806--845. https://doi.org/10.1111/j.1749-6632.1962.tb30600.x arXiv:https://nyaspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1749-6632.1962.tb30600.xGoogle ScholarCross Ref
- 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/10.1145/3448018.3458008Google ScholarDigital Library
- William Delamare, Teng Han, and Pourang Irani. 2017. Designing a Gaze Gesture Guiding System. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (Vienna, Austria) (MobileHCI '17). Association for Computing Machinery, New York, NY, USA, Article 26, 13 pages. https://doi.org/10.1145/3098279.3098561Google ScholarDigital Library
- Heiko Drewes, Mohamed Khamis, and Florian Alt. 2018. Smooth Pursuit Target Speeds and Trajectories. In Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia (Cairo, Egypt) (MUM '18). Association for Computing Machinery, New York, NY, USA, 139--146. https://doi.org/10.1145/3282894.3282913Google ScholarDigital Library
- Tobii Dynavox. 2021. Assistive technology for communication/AAC - Tobii Dynavox. https://www.tobiidynavox.com/ (Retrieved January 27, 2021).Google Scholar
- Augusto Esteves, Eduardo Velloso, Andreas Bulling, and Hans Gellersen. 2015. Orbits: Gaze Interaction for Smart Watches using Smooth Pursuit Eye Movements. In Proceedings of the 28th Annual ACM Symposium on User Interface Software and Technology (Charlotte, NC, USA) (UIST '15). Association for Computing Machinery, New York, NY, USA, 457--466. https://doi.org/10.1145/2807442.2807499Google ScholarDigital Library
- Bernard Ghanem Fabian Caba Heilbron, Victor Escorcia and Juan Carlos Niebles. 2015. ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 961--970.Google Scholar
- Anna Maria Feit, Lukas Vordemann, Seonwook Park, Caterina Berube, and Otmar Hilliges. 2020. Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation. In ACM Symposium on Eye Tracking Research and Applications (Stuttgart, Germany) (ETRA '20 Full Papers). Association for Computing Machinery, New York, NY, USA, Article 10, 11 pages. https://doi.org/10.1145/3379155.3391321Google ScholarDigital Library
- Anna Maria Feit, Shane Williams, Arturo Toledo, Ann Paradiso, Harish Kulkarni, Shaun Kane, and Meredith Ringel Morris. 2017. Toward Everyday Gaze Input: Accuracy and Precision of Eye Tracking and Implications for Design. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 1118--1130. https://doi.org/10.1145/3025453.3025599Google ScholarDigital Library
- Pedro Figueiredo and Manuel J. Fonseca. 2018. EyeLinks: A Gaze-Only Click Alternative for Heterogeneous Clickables. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (Boulder, CO, USA) (ICMI '18). Association for Computing Machinery, New York, NY, USA, 307--314. https://doi.org/10.1145/3242969.3243021Google ScholarDigital Library
- Christoph Gebhardt, Brian Hecox, Bas van Opheusden, Daniel Wigdor, James Hillis, Otmar Hilliges, and Hrvoje Benko. 2019. Learning Cooperative Personalized Policies from Gaze Data. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (New Orleans, LA, USA) (UIST '19). Association for Computing Machinery, New York, NY, USA, 197--208. https://doi.org/10.1145/3332165.3347933Google ScholarDigital Library
- John Hansen, Anders Johansen, Dan Hansen, Kenji Ito, and Satoru Mashino. 2003. Command Without a Click: Dwell Time Typing by Mouse and Gaze Selections. In Proceedings of Human-Computer Interaction (INTERACTA '03). IFIP, 121--128.Google Scholar
- Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Human Mental Workload, Peter A. Hancock and Najmedin Meshkati (Eds.). Advances in Psychology, Vol. 52. North-Holland, 139--183. https://doi.org/10.1016/S0166-4115(08)62386-9Google Scholar
- Mary Hayhoe and Dana Ballard. 2005. Eye Movements in Natural Behavior. Trends in Cognitive Sciences 9, 4 (2005), 188--194. https://doi.org/10.1016/j.tics.2005.02.009Google ScholarCross Ref
- Eckhard H. Hess and James M. Polt. 1960. Pupil Size as Related to Interest Value of Visual Stimuli. Science 132 (1960), 349--350.Google ScholarCross Ref
- Anthony Hornof, Anna Cavender, and Rob Hoselton. 2004. EyeDraw: A System for Drawing Pictures with the Eyes. In Proceedings of the 2004 CHI Extended Abstracts on Human Factors in Computing Systems (Vienna, Austria) (CHI EA '04). Associati0on for Computing Machinery, New York, NY, USA, 1251--1254. https://doi.org/10.1145/985921.986036Google ScholarDigital Library
- Anthony J. Hornof and Anna Cavender. 2005. EyeDraw: Enabling Children with Severe Motor Impairments to Draw with Their Eyes. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Portland, Oregon, USA) (CHI '05). Association for Computing Machinery, New York, NY, USA, 161--170. https://doi.org/10.1145/1054972.1054995Google ScholarDigital Library
- Thomas E. Hutchinson, K. Preston White, Worthy N. Martin, Kelly C. Reichert, and Lisa A. Frey. 1989. Human-computer Interaction using Eye-gaze Input. IEEE Transactions on Systems, Man, and Cybernetics 19, 6 (1989), 1527--1534. https://doi.org/10.1109/21.44068Google ScholarCross Ref
- Toshiya Isomoto, Toshiyuki Ando, Buntarou Shizuki, and Shin Takahashi. 2018. Dwell Time Reduction Technique Using Fitts' Law for Gaze-Based Target Acquisition. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (Warsaw, Poland) (ETRA '18). Association for Computing Machinery, New York, NY, USA, 26:1-26:7. https://doi.org/10.1145/3204493.3204532Google ScholarDigital Library
- Toshiya Isomoto, Shota Yamanaka, and Buntarou Shizuki. 2020. Gaze-based Command Activation Technique Robust Against Unintentional Activation using Dwell-then-Gesture. In Proceedings of Graphics Interface 2020 (University of Toronto) (GI '20). Canadian Human-Computer Communications Society / Société canadienne du dialogue humain-machine, 256--266. https://doi.org/10.20380/GI2020.26Google Scholar
- Toshiya Isomoto, Shota Yamanaka, and Buntarou Shizuki. 2021. Relationship between Dwell-Time and Model Human Processor for Dwell-based Image Selection. In Proceedings of the 2021 ACM Symposium on Applied Perception (virtual) (SAP '21). Association for Computing Machinery, Article 6, 5 pages. https://doi.org/10.1145/3474451.3476240Google ScholarDigital Library
- Robert J. K. Jacob. 1990. What You Look at is What You Get: Eye Movement-based Interaction Techniques. In Proceedings of the 1990 CHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA) (CHI '90). Association for Computing Machinery, New York, NY, USA, 11--18. https://doi.org/10.1145/97243.97246Google ScholarDigital Library
- Robert J. K. Jacob. 1991. The Use of Eye Movements in Human-computer Interaction Techniques: What You Look at is What You Get. ACM Transaction on Information Systems 9, 2 (1991), 152--169.Google ScholarDigital Library
- Robert. J. K. Jacob. 1993. Eye Movement-Based Human-Computer Interaction Techniques: Toward Non-Command Interfaces. Advances in Human-Computer Interaction 4 (1993), 151--190.Google Scholar
- Joaquin Jadue, Gino Slanzi, Luis Salas, and Juan D. Velásquez. 2015. Web User Click Intention Prediction by Using Pupil Dilation Analysis. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '15, Vol. 1). IEEE / Association for Computing Machinery, New York, NY, USA, 433--436. https://doi.org/10.1109/WI-IAT.2015.221Google ScholarDigital Library
- Dagmar Kern, Paul Marshall, and Albrecht Schmidt. 2010. Gazemarks: Gaze-Based Visual Placeholders to Ease Attention Switching. In Proceedings of the 2010 CHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI '10). Association for Computing Machinery, New York, NY, USA, 2093--2102. https://doi.org/10.1145/1753326.1753646Google ScholarDigital Library
- Mohamed Khamis, Florian Alt, Mariam Hassib, Emanuel von Zezschwitz, Regina Hasholzner, and Andreas Bulling. 2016. GazeTouchPass: Multimodal Authentication Using Gaze and Touch on Mobile Devices. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (San Jose, California, USA) (CHI EA '16). Association for Computing Machinery, New York, NY, USA, 2156--2164. https://doi.org/10.1145/2851581.2892314Google ScholarDigital Library
- Taejun Kim, Auejin Ham, Sunggeun Ahn, and Geehyuk Lee. 2022. Lattice Menu: A Low-Error Gaze-Based Marking Menu Utilizing Target-Assisted Gaze Gestures on a Lattice of Visual Anchors. In CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI '22). Association for Computing Machinery, New York, NY, USA, Article 277, 12 pages. https://doi.org/10.1145/3491102.3501977Google ScholarDigital Library
- Dominik Kirst and Andreas Bulling. 2016. On the Verge: Voluntary Convergences for Accurate and Precise Timing of Gaze Input. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (San Jose, California, USA) (CHI EA '16). Association for Computing Machinery, New York, NY, USA, 1519--1525. https://doi.org/10.1145/2851581.2892307Google ScholarDigital Library
- Shinya Kudo, Hiroyuki Okabe, Taku Hachisu, Michi Sato, Shogo Fukushima, and Hiroyuki Kajimoto. 2013. Input Method Using Divergence Eye Movement. In Proceedings of the 2013 CHI Extended Abstracts on Human Factors in Computing Systems (Paris, France) (CHI EA '13). Association for Computing Machinery, New York, NY, USA, 1335--1340. https://doi.org/10.1145/2468356.2468594Google ScholarDigital Library
- Michael F. Land and Mary Hayhoe. 2001. In what ways do eye movements contribute to everyday activities? Vision Research 41, 25 (2001), 3559--3565. https://doi.org/10.1016/S0042-6989(01)00102-XGoogle ScholarCross Ref
- Feiyu Lu, Shakiba Davari, and Doug Bowman. 2021. Exploration of Techniques for Rapid Activation of Glanceable Information in Head-Worn Augmented Reality. In Symposium on Spatial User Interaction (Virtual Event, USA) (SUI '21). Association for Computing Machinery, New York, NY, USA, Article 14, 11 pages. https://doi.org/10.1145/3485279.3485286Google ScholarDigital Library
- Christof Lutteroth, Moiz Penkar, and Gerald Weber. 2015. Gaze vs. Mouse: A Fast and Accurate Gaze-Only Click Alternative. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (Charlotte, NC, USA) (UIST '15). Association for Computing Machinery, New York, NY, USA, 385--394. https://doi.org/10.1145/2807442.2807461Google ScholarDigital Library
- Päivi Majaranta, Ulla-Kaija Ahola, and Oleg Špakov. 2009. Fast Gaze Typing with an Adjustable Dwell Time. In Proceedings of the 2009 CHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI '09). Association for Computing Machinery, New York, NY, USA, 357--360. https://doi.org/10.1145/1518701.1518758Google ScholarDigital Library
- Päivi Majaranta, Anne Aula, and Kari-Jouko Räihä. 2004. Effects of Feedback on Eye Typing with a Short Dwell Time. In Proceedings of the 2004 Symposium on Eye Tracking Research & Applications (San Antonio, Texas) (ETRA '04). Association for Computing Machinery, New York, NY, USA, 139--146. https://doi.org/10.1145/968363.968390Google ScholarDigital Library
- Päivi Majaranta, I. Scott MacKenzie, Anne Aula, and Kari-Jouko Räihä. 2006. Effects of Feedback and Dwell Time on Eye Typing Speed and Accuracy. Universal Access in the Information Society 5, 2 (2006), 199--208. https://doi.org/10.1007/s10209-006-0034-zGoogle ScholarDigital Library
- Sven Mayer, Gierad Laput, and Chris Harrison. 2020. Enhancing Mobile Voice Assistants with WorldGaze. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1--10. https://doi.org/10.1145/3313831.3376479Google ScholarDigital Library
- Pallavi Mohan, Wooi Boon Goh, Chi-Wing Fu, and Sai-Kit Yeung. 2018. DualGaze: Addressing the Midas Touch Problem in Gaze Mediated VR Interaction. In 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct '18). 79--84. https://doi.org/10.1109/ISMAR-Adjunct.2018.00039Google Scholar
- Martez E. Mott, Shane Williams, Jacob O. Wobbrock, and Meredith Ringel Morris. 2017. Improving Dwell-Based Gaze Typing with Dynamic, Cascading Dwell Times. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 2558--2570. https://doi.org/10.1145/3025453.3025517Google ScholarDigital Library
- Aanand Nayyar, Utkarsh Dwivedi, Karan Ahuja, Nitendra Rajput, Seema Nagar, and Kuntal Dey. 2017. OptiDwell: Intelligent Adjustment of Dwell Click Time. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (Limassol, Cyprus) (IUI '17). Association for Computing Machinery, New York, NY, USA, 193--204. https://doi.org/10.1145/3025171.3025202Google ScholarDigital Library
- Abdul Moiz Penkar, Christof Lutteroth, and Gerald Weber. 2013. Eyes Only: Navigating Hypertext with Gaze. In 14th IFIP TC 13 International Conference on Human-Computer Interaction - INTERACT 2013. Springer Berlin Heidelberg, Berlin, Heidelberg, 153--169.Google Scholar
- Ken Pfeuffer, Yasmeen Abdrabou, Augusto Esteves, Radiah Rivu, Yomna Abdelrahman, Stefanie Meitner, Amr Saadi, and Florian Alt. 2021. ARtention: A Design Space for Gaze-adaptive User Interfaces in Augmented Reality. Computers & Graphics 95 (2021), 1--12. https://doi.org/10.1016/j.cag.2021.01.001Google ScholarCross Ref
- Ken Pfeuffer, Jason Alexander, Ming Ki Chong, and Hans Gellersen. 2014. Gaze-touch: Combining Gaze with Multi-touch for Interaction on the Same Surface. In Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology (Honolulu, Hawaii, USA) (UIST '14). Association for Computing Machinery, New York, NY, USA, 509--518. https://doi.org/10.1145/2642918.2647397Google ScholarDigital Library
- Ken Pfeuffer, Jason Alexander, Ming Ki Chong, Yanxia Zhang, and Hans Gellersen. 2015. Gaze-Shifting: Direct-Indirect Input with Pen and Touch Modulated by Gaze. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (Charlotte, NC, USA) (UIST '15). Association for Computing Machinery, New York, NY, USA, 373--383. https://doi.org/10.1145/2807442.2807460Google ScholarDigital Library
- Ken Pfeuffer, Benedikt Mayer, Diako Mardanbegi, and Hans Gellersen. 2017. Gaze + Pinch Interaction in Virtual Reality. In Proceedings of the 5th Symposium on Spatial User Interaction (Brighton, United Kingdom) (SUI '17). Association for Computing Machinery, New York, NY, USA, 99--108. https://doi.org/10.1145/3131277.3132180Google ScholarDigital Library
- Jimin. Pi and Bertram. E. Shi. 2017. Probabilistic Ajustment of Dwell Time for Eye Typing. In 10th International Conference on Human System Interactions (HSI). IEEE, 251--257.Google Scholar
- Robin Piening, Ken Pfeuffer, Augusto Esteves, Tim Mittermeier, Sarah Prange, Philippe Schröder, and Florian Alt. 2021. Looking for Info: Evaluation of Gaze Based Information Retrieval in Augmented Reality. In 18th IFIP TC 13 International Conference on Human-Computer Interaction - INTERACT 2021. Springer International Publishing, 544--565.Google ScholarDigital Library
- Panwar Prateek, Sarcar Sayan, and Samanta Debasis. 2012. EyeBoard: A Fast and Accurate Eye Gaze-Based Text Entry System. In 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI '12). 1--8. https://doi.org/10.1109/IHCI.2012.6481793Google Scholar
- Kari-Jouko Räihä and Saila Ovaska. 2012. An Exploratory Study of Eye Typing Fundamentals: Dwell Time, Text Entry Rate, Errors, and Workload. In Proceedings of the 2012 CHI Conference on Human Factors in Computing Systems (Austin, Texas, USA) (CHI '12). Association for Computing Machinery, New York, NY, USA, 3001--3010. https://doi.org/10.1145/2207676.2208711Google ScholarDigital Library
- Dario D. Salvucci and John R. Anderson. 2000. Intelligent Gaze-Added Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (The Hague, The Netherlands) (CHI '00). Association for Computing Machinery, New York, NY, USA, 273--280. https://doi.org/10.1145/332040.332444Google ScholarDigital Library
- Dario D. Salvucci and Joseph H. Goldberg. 2000. Identifying Fixations and Saccades in Eye-Tracking Protocols. In Proceedings of the 2000 Symposium on Eye Tracking Research & Applications (Palm Beach Gardens, Florida, USA) (ETRA '00). Association for Computing Machinery, New York, NY, USA, 71--78. https://doi.org/10.1145/355017.355028Google ScholarDigital Library
- Simon Schenk, Marc Dreiser, Gerhard Rigoll, and Michael Dorr. 2017. GazeEverywhere: Enabling Gaze-only User Interaction on an Unmodified Desktop PC in Everyday Scenarios. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 3034--3044. https://doi.org/10.1145/3025453.3025455Google ScholarDigital Library
- Asma Shakil, Christof Lutteroth, and Gerald Weber. 2019. CodeGazer: Making Code Navigation Easy and Natural With Gaze Input. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI '19). Association for Computing Machinery, New York, NY, USA, 1--12. https://doi.org/10.1145/3290605.3300306Google ScholarDigital Library
- Linda E. Sibert and Robert J. K. Jacob. 2000. Evaluation of Eye Gaze Interaction. In Proceedings of the 2000 CHI Conference on Human Factors in Computing Systems (The Hague, The Netherlands) (CHI '00). Association for Computing Machinery, New York, NY, USA, 281--288. https://doi.org/10.1145/332040.332445Google ScholarDigital Library
- Henrik Skovsgaard, Kari-Jouko Räihä, and Martin Tall. 2011. Computer Control by Gaze. In Gaze Interaction and Aplications of Eye Tracking: Advances in Assistive Technologies, Päivi Majaranta, Hirotaka Aoki, Mick Donegan, Witzner Hansen Dan, John Paulin Hansen, Aulikki Hyrskykari, and Kari-Jouko Räihä (Eds.). IGI Global, Hershey, PA, Chapter 9, 78--103.Google Scholar
- Sophie Stellmach and Raimund Dachselt. 2012. Look & Touch: Gaze-supported Target Acquisition. In Proceedings of the 2012 CHI Conference on Human Factors in Computing Systems (Austin, Texas, USA) (CHI '12). Association for Computing Machinery, New York, NY, USA, 2981--2990. https://doi.org/10.1145/2207676.2208709Google ScholarDigital Library
- Geoffrey Tien and M. Stella Atkins. 2008. Improving Hands-Free Menu Selection Using Eyegaze Glances and Fixations. In Proceedings of the 2008 Symposium on Eye Tracking Research & Applications (Savannah, Georgia) (ETRA '08). Association for Computing Machinery, New York, NY, USA, 47--50. https://doi.org/10.1145/1344471.1344482Google ScholarDigital Library
- Jayson Turner, Jason Alexander, Andreas Bulling, and Hans Gellersen. 2015. Gaze+RST: Integrating Gaze and Multitouch for Remote Rotate-Scale-Translate Tasks. In Proceedings of the 2015 CHI Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI '15). Association for Computing Machinery, New York, NY, USA, 4179--4188. https://doi.org/10.1145/2702123.2702355Google ScholarDigital Library
- Mario H. Urbina, Maike Lorenz, and Anke Huckauf. 2010. Pies with EYEs: The Limits of Hierarchical Pie Menus in Gaze Control. In Proceedings of the 2010 ACM Symposium on Eye-Tracking Research & Applications (Austin, Texas) (ETRA '10). Association for Computing Machinery, New York, NY, USA, 93--96. https://doi.org/10.1145/1743666.1743689Google ScholarDigital Library
- Eduardo Velloso, Flavio Luiz Coutinho, Andrew Kurauchi, and Carlos H Morimoto. 2018. Circular Orbits Detection for Gaze Interaction Using 2D Correlation and Profile Matching Algorithms. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (Warsaw, Poland) (ETRA '18). Association for Computing Machinery, New York, NY, USA, Article 25, 9 pages. https://doi.org/10.1145/3204493.3204524Google ScholarDigital Library
- Mélodie Vidal, Andreas Bulling, and Hans Gellersen. 2013. Pursuits: Spontaneous Interaction with Displays Based on Smooth Pursuit Eye Movement and Moving Targets. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Zurich, Switzerland) (UbiComp '13). Association for Computing Machinery, New York, NY, USA, 439--448. https://doi.org/10.1145/2493432.2493477Google ScholarDigital Library
- Oleg Špakov, Poika Isokoski, Jari Kangas, Deepak Akkil, and Päivi Majaranta. 2016. PursuitAdjuster: An Exploration into the Design Space of Smooth Pursuit-based Widgets. In Proceedings of the 2016 ACM Symposium on Eye Tracking Research & Applications (Charleston, South Carolina) (ETRA '16). Association for Computing Machinery, New York, NY, USA, 287--290. https://doi.org/10.1145/2857491.2857526Google ScholarDigital Library
- Colin Ware and Harutune H. Mikaelian. 1987. An Evaluation of an Eye Tracker As a Device for Computer Input. In Proceedings of the 1987 CHI/GI Conference on Human Factors in Computing Systems and Graphics Interface (Toronto, Ontario, Canada) (CHI '87). Association for Computing Machinery, New York, NY, USA, 183--188. https://doi.org/10.1145/29933.275627Google ScholarDigital Library
- Pingmei Xu, Yusuke Sugano, and Andreas Bulling. 2016. Spatio-Temporal Modeling and Prediction of Visual Attention in Graphical User Interfaces. Association for Computing Machinery, New York, NY, USA, 3299--3310. https://doi.org/10.1145/2858036.2858479Google ScholarDigital Library
- Xinyong Zhang, Xiangshi Ren, and Hongbin Zha. 2008. Improving Eye Cursor's Stability for Eye Pointing Tasks. In Proceedings of the 2008 CHI Conference on Human Factors in Computing Systems (Florence, Italy) (CHI '08). Association for Computing Machinery, New York, NY, USA, 525--534. https://doi.org/10.1145/1357054.1357139Google ScholarDigital Library
- Xinyong Zhang, Pianpian Xu, Qing Zhang, and Hongbin Zha. 2011. Speed-Accuracy Trade-off in Dwell-Based Eye Pointing Tasks at Different Cognitive Levels. In Proceedings of the 1st International Workshop on Pervasive Eye Tracking & Mobile Eye-Based Interaction (Beijing, China) (PETMEI '11). Association for Computing Machinery, New York, NY, USA, 37--42. https://doi.org/10.1145/2029956.2029967Google ScholarDigital Library
Index Terms
- Dwell Selection with ML-based Intent Prediction Using Only Gaze Data
Recommendations
Predicting Gaze-based Target Selection in Augmented Reality Headsets based on Eye and Head Endpoint Distributions
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing SystemsTarget selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based ...
Hide my Gaze with EOG!: Towards Closed-Eye Gaze Gesture Passwords that Resist Observation-Attacks with Electrooculography in Smart Glasses
MoMM2019: Proceedings of the 17th International Conference on Advances in Mobile Computing & MultimediaSmart glasses allow for gaze gesture passwords as a hands-free form of mobile authentication. However, pupil movements for password input are easily observed by attackers, who thereby can derive the password. In this paper we investigate closed-eye gaze ...
Control prediction based on cumulative gaze dwell time while browsing contents
ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and ApplicationsThe utilization of gaze behavior for control has been studied as one of the hands-free control methods. With the recent spread of Head Mounted Display devices, it has become a vital issue to establish hands-free control methods. Previously proposed ...
Comments