Fuzzy System-Based Target Selection for a NIR Camera-Based Gaze Tracker

Review and Evaluation of Eye Movement Event Detection Algorithms

Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods.

Significant Measures of Gaze and Pupil Movement for Evaluating Empathy between Viewers and Digital Content

The success of digital content depends largely on whether viewers empathize with stories and narratives. Researchers have investigated the elements that may elicit empathy from viewers. Empathic response involves affective and cognitive processes and is expressed through multiple verbal and nonverbal modalities. Specifically, eye movements communicate emotions and intentions and may reflect an empathic status. This study explores feature changes in eye movements when a viewer empathizes with the video’s content. Seven feature variables of eye movements (change of pupil diameter, peak pupil dilation, very short, mid, over long fixation duration, saccadic amplitude, and saccadic count) were extracted from 47 participants who viewed eight videos (four empathic videos and four non-empathic videos) distributed in a two-dimensional emotion axis (arousal and valence). The results showed that viewers’ saccadic amplitude and peak pupil dilation in the eigenvalues of eye movements increased in the empathic condition. The fixation time and pupil size change showed limited significance, and whether there were asymmetric pupil responses between the left and right pupils remained inconclusive. Our investigation suggests that saccadic amplitude and peak pupil dilation are reliable measures for recognizing whether viewers empathize with content. The findings provide physiological evidence based on eye movements that both affective and cognitive processes accompany empathy during media consumption.

On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions

Cognitive workload, being a quantitative measure of mental effort, draws significant interest of researchers, as it allows to monitor the state of mental fatigue. Estimation of cognitive workload becomes especially important for job positions requiring outstanding engagement and responsibility, e.g., air-traffic dispatchers, pilots, car or train drivers. Cognitive workload estimation finds its applications also in the field of education material preparation. It allows to monitor the difficulty degree for specific tasks enabling to adjust the level of education materials to typical abilities of students. In this study, we present the results of research conducted with the goal of examining the influence of various fuzzy or non-fuzzy aggregation functions upon the quality of cognitive workload estimation. Various classic machine learning models were successfully applied to the problem. The results of extensive in-depth experiments with over 2000 aggregation operators shows the applicability of the approach based on the aggregation functions. Moreover, the approach based on aggregation process allows for further improvement of classification results. A wide range of aggregation functions is considered and the results suggest that the combination of classical machine learning models and aggregation methods allows to achieve high quality of cognitive workload level recognition preserving low computational cost.

Computer Vision-based Methodology to Improve Interaction for People with Motor and Speech Impairment

Augmentative and Alternative Communication (AAC) aims to complement or replace spoken language to compensate for expression difficulties faced by people with speech impairments. Computing systems have been developed to support AAC; however, partially due to technical problems, poor interface, and limited interaction functions, AAC systems are not widespread, adopted, and used, therefore reaching a limited audience. This article proposes a methodology to support AAC for people with motor impairments, using computer vision and machine learning techniques to allow for personalized gestural interaction. The methodology was applied in a pilot system used by both volunteers without disabilities, and by volunteers with motor and speech impairments, to create datasets with personalized gestures. The created datasets and a public dataset were used to evaluate the technologies employed for gesture recognition, namely the Support Vector Machine (SVM) and Convolutional Neural Network (using Transfer Learning), and for motion representation, namely the conventional Motion History Image and Optical Flow-Motion History Image (OF-MHI). Results obtained from the estimation of prediction error using K-fold cross-validation suggest SVM associated with OF-MHI presents slightly better results for gesture recognition. Results indicate the technical feasibility of the proposed methodology, which uses a low-cost approach, and reveals the challenges and specific needs observed during the experiment with the target audience.

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control

The tracking of eye gesture movements using wearable technologies can undoubtedly improve quality of life for people with mobility and physical impairments by using spintronic sensors based on the tunnel magnetoresistance (TMR) effect in a human-machine interface. Our design involves integrating three TMR sensors on an eyeglass frame for detecting relative movement between the sensor and tiny magnets embedded in an in-house fabricated contact lens. Using TMR sensors with the sensitivity of 11 mV/V/Oe and ten <1 mm³ embedded magnets within a lens, an eye gesture system was implemented with a sampling frequency of up to 28 Hz. Three discrete eye movements were successfully classified when a participant looked up, right or left using a threshold-based classifier. Moreover, our proof-of-concept real-time interaction system was tested on 13 participants, who played a simplified Tetris game using their eye movements. Our results show that all participants were successful in completing the game with an average accuracy of 90.8%.

An emergency call system for patients in locked-in state using an SSVEP-based brain switch

Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated. In this study, we verified whether the SSVEP-based brain switch system can be practically used as an emergency call system for patients in LIS. The brain switch used for our system adopted a chromatic visual stimulus, which proved to be visually less stimulating than conventional checkerboard-type stimuli but could generate SSVEP responses strong enough to be used for brain-computer interface (BCI) applications. To verify the feasibility of our emergency call system, 14 healthy participants and 3 patients with severe ALS took part in online experiments. All three ALS patients successfully called their guardians to their bedsides in about 6.56 seconds. Furthermore, additional experiments with one of these patients demonstrated that our emergency call system maintains fairly good performance even up to 4 weeks after the first experiment without renewing initial calibration data. Our results suggest that our SSVEP-based emergency call system might be successfully used in practical scenarios.