Visual impairments, such as cataracts, pose a global challenge with preventable cases and unaddressed issues due to limited eye care understanding. In this work, we investigate cataracts using a virtual reality simulation, exploring its impact on a visual search task and investigating the correlations between eye movement features and the severity of the simulation. We simulated cataract progression in one or both eyes through two studies utilizing virtual reality and eye tracking. We analyzed the impact on task performance and eye movements and found that mild cataract progression may be hard to detect. However, more severe cataract simulation revealed significant differences in eye movement characteristics and task performance. These results indicate that eye movements could serve as early diagnostic tools.

Currently, interactive systems use physiological sensing to enable advanced functionalities. While eye tracking is a promising means to understand the user, eye tracking data inherently suffers from missing data due to blinks, which may result in reduced system performance. We conducted a literature review to understand how researchers deal with this issue. We uncovered that researchers often implemented their use-case-specific pipeline to overcome the issue, ranging from ignoring missing data to artificial interpolation. With these first insights, we run a large-scale analysis on 11 publicly available datasets to understand the impact of the various approaches on data quality and accuracy. By this, we highlight the pitfalls in data processing and which methods work best. Based on our results, we provide guidelines for handling eye tracking data for interactive systems. Further, we propose a standard data processing pipeline that allows researchers and practitioners to pre-process and standardize their data efficiently.

Physiological sensing enables us to use advanced adaptive functionalities through physiological data (e.g., eye tracking) to change conditions. In this work, we investigate the impact of infilling methods on LSTM models’ performance in handling missing eye tracking data, specifically during blinks and gaps in recording. We conducted experiments using recommended infilling techniques from previous work on an openly available eye tracking dataset and LSTM model structure. Our findings indicate that the infilling method significantly influences LSTM prediction accuracy. These results underscore the importance of standardized infilling approaches for enhancing the reliability and reproducibility of LSTM-based eye tracking applications on a larger scale. Future work should investigate the impact of these infilling methods in larger datasets to investigate generalizability.

Rapid Serial Visual Presentation (RSVP) improves the reading speed for optimizing the user's information processing capabilities on Virtual Reality (VR) devices. Yet, the user's RSVP reading performance changes over time while the reading speed remains static. In this paper, we evaluate pupil dilation as a physiological metric to assess the mental workload of readers in real-time. We assess mental workload under different background lighting and RSVP presentation speeds to estimate the optimal color that discriminates the pupil diameter varying RSVP presentation speeds. We discovered that a gray background provides the best contrast for reading at various presentation speeds. Then, we conducted a second study to evaluate the classification accuracy of mental workload for different presentation speeds. We find that pupil dilation relates to mental workload when reading with RSVP. We discuss how pupil dilation can be used to adapt the RSVP speed in future VR applications to optimize information intake.

The proliferation of mobile Virtual Reality (VR) headsets shifts our interaction with virtual worlds beyond our living rooms into shared spaces. Consequently, we are entrusting more and more personal data to these devices, calling for strong security measures and authentication. However, the standard authentication method of such devices - entering PINs via virtual keyboards - is vulnerable to shoulder-surfing, as movements to enter keys can be monitored by an unnoticed observer. To address this, we evaluated masking techniques to obscure VR users' input during PIN authentication by diverting their hand movements. Through two experimental studies, we demonstrate that these methods increase users' security against shoulder-surfing attacks from observers without excessively impacting their experience and performance. With these discoveries, we aim to enhance the security of future VR authentication without disrupting the virtual experience or necessitating additional hardware or training of users.

Eye tracking is the basis for many intelligent systems to predict user actions. A core challenge with eye-tracking data is that it inherently suffers from missing data due to blinks. Approaches such as intent prediction and user state recognition process gaze data using neural networks; however, they often have difficulty handling missing information. In an effort to understand how prior work dealt with missing data, we found that researchers often simply ignore missing data or adopt use-case-specific approaches, such as artificially filling in missing data. This inconsistency in handling missing data in eye tracking hinders the development of effective intelligent systems for predicting user actions and limits reproducibility. Furthermore, this can even lead to incorrect results. Thus, this lack of standardization calls for investigating possible solutions to improve the consistency and effectiveness of processing eye-tracking data for user action prediction.

Eye tracking is increasingly becoming prevalent for health-related interactive systems. Eye tracking can automatically reveal the presence of Central Field Loss (CFL), a dysfunctional visual behavior requiring time-intensive medical assessments. Since CFL typically results in poor fixation stability and more frequent saccades, this work investigates the use of machine learning to estimate the likelihood of CFL based on eye-movement data. We compared random forests, support vector machines, and long-short-term memory (LSTM) neural networks for their ability to discriminate between the presence or absence of an experimentally-induced CFL. We found that the estimation accuracy increases with larger samples of eye-tracking data. However, the computational costs outweigh any increase in accuracy after classifying window sizes of 1600 msec. Here, traditional machine learning approaches outperform the LSTM neural network. We discuss implications for continuous end-user CFL monitoring and processing power to provide an outlook for gaze-based wearable health devices in human-computer interaction.