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1. Positional head-eye tracking outside the lab: an open-source solution

   Hausamann, P; Sinnott, C; MacNeilage, P (January 2020, Symposium on Eye Tracking Research and Applications)

   Simultaneous head and eye tracking has traditionally been confined to a laboratory setting and real-world motion tracking limited to measuring linear acceleration and angular velocity. Recently available mobile devices such as the Pupil Core eye tracker and the Intel RealSense T265 motion tracker promise to deliver accurate measurements outside the lab. Here, the researchers propose a hard- and software framework that combines both devices into a robust, usable, low-cost head and eye tracking system. The developed software is open source and the required hardware modifications can be 3D printed. The researchers demonstrate the system’s ability to measure head and eye movements in two tasks: an eyes-fixed head rotation task eliciting the vestibulo-ocular reflex inside the laboratory, and a natural locomotion task where a subject walks around a building outside of the laboratory. The resultant head and eye movements are discussed, as well as future implementations of this system. 

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2. Positional head-eye tracking outside the lab: an open-source solution

   https://doi.org/10.1145/3379156.3391365  

   Hausamann, P.; Sinnott, C.; MacNeilage, P.R. (June 2020, ACM Symposium on Eye Tracking Research and Applications)

   Simultaneous head and eye tracking has traditionally been confined to a laboratory setting and real-world motion tracking limited to measuring linear acceleration and angular velocity. Recently available mobile devices such as the Pupil Core eye tracker and the Intel RealSense T265 motion tracker promise to deliver accurate measurements outside the lab. Here, the researchers propose a hard- and software framework that combines both devices into a robust, usable, low-cost head and eye tracking system. The developed software is open source and the required hardware modifications can be 3D printed. The researchers demonstrate the system’s ability to measure head and eye movements in two tasks: an eyes-fixed head rotation task eliciting the vestibulo-ocular reflex inside the laboratory, and a natural locomotion task where a subject walks around a building outside of the laboratory. The resultant head and eye movements are discussed, as well as future implementations of this system. 

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3. Neural 3D Gaze: 3D Pupil Localization and Gaze Tracking based on Anatomical Eye Model and Neural Refraction Correction

   https://doi.org/10.1109/ISMAR55827.2022.00053  

   Lu, Conny; Chakravarthula, Praneeth; Liu, Kaihao; Liu, Xixiang; Li, Siyuan; Fuchs, Henry (October 2022, Proccedings of the 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR))

   Eye tracking has already made its way to current commercial wearable display devices, and is becoming increasingly important for virtual and augmented reality applications. However, the existing model-based eye tracking solutions are not capable of conducting very accurate gaze angle measurements, and may not be sufficient to solve challenging display problems such as pupil steering or eyebox expansion. In this paper, we argue that accurate detection and localization of pupil in 3D space is a necessary intermediate step in model-based eye tracking. Existing methods and datasets either ignore evaluating the accuracy of 3D pupil localization or evaluate it only on synthetic data. To this end, we capture the first 3D pupilgaze-measurement dataset using a high precision setup with head stabilization and release it as the first benchmark dataset to evaluate both 3D pupil localization and gaze tracking methods. Furthermore, we utilize an advanced eye model to replace the commonly used oversimplified eye model. Leveraging the eye model, we propose a novel 3D pupil localization method with a deep learning-based corneal refraction correction. We demonstrate that our method outperforms the state-of-the-art works by reducing the 3D pupil localization error by 47.5% and the gaze estimation error by 18.7%. Our dataset and codes can be found here: link. 

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4. User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

   https://doi.org/10.3390/virtualworlds1010004  

   Asish, Sarker Monojit; Kulshreshth, Arun K.; Borst, Christoph W. (December 2022, Virtual Worlds)

   Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 

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5. How pipefitters obtain visual information from construction assembly drawings

   https://doi.org/10.36680/j.itcon.2022.015  

   Sears, Matthew; Alruwaythi, Omar; Goodrum, Paul M. (January 2022, Journal of Information Technology in Construction)

   An extensive framework has been developed for studying the behavior of motor vehicle drivers using eye tracking technology. Previous work has revealed strong relationships between driver eye movements and performance, which has resulted in widely accepted guidance within the transportation industry. In this work, the same eye tracking analysis methods were applied to investigate 20 professional pipefitters’ interactions with traditional isometric assembly drawings during a construction pipe model assembly task, in order to begin to understand the strategies that construction craft professionals use to gather visual information from engineering deliverables. A custom web application was developed to quantify and compare the pipefitters’ interactions with the assembly drawings through several visit metrics. Results indicated that the pipefitters’ interactions with the assembly drawings were associated with their performance and spatial cognition; however, the results did not suggest that the pipefitters were adhering to any particular visual information gathering strategies. The authors also investigated whether age or industry experience were associated with differences in visual information gathering strategies, but no significant relationships were observed. The primary contribution of this work is a demonstration of how existing eye tracking analysis methods can be applied to investigate how construction craft professionals extract visual information from engineering deliverables. 

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