Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching

In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g- node.org/ioannis.agtzidis/hollywood2_em

Assessment of saccadic eye movements in healthy subjects using consumer-grade mobile devices

Assessing eye movement features may provide insight into neurological health, inform diagnoses, and guide clinical intervention. The potential to utilize saccadic eye movement latency is especially promising as a clinical biomarker in identifying and treating neurodegenerative disease. Artificial intelligence and deep learning technology have improved the feasibility of eye-tracking methodology and scalability in research studies. Tablet and smartphone-based tracking equipment have been shown to provide quantitative data of comparable accuracy to more costly, special-built equipment while reducing cost and complexity in experimental procedures. Establishing an efficient and accurate measurement tool to aid the detection and tracking of diseases may benefit the development of comprehensive treatment and monitoring strategies. This study, therefore, seeks to examine oculomotor function through saccade latency and error rate in healthy adults with respect to age, demonstrating a mobile device’s efficacy in assessing subtle eye movements and establishing a dataset upon which to guide further investigation

AUTOMATIC DETECTION OF NYSTAGMUS IN BEDSIDE VOG RECORDINGS FROM PATIENTS WITH VERTIGO

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of vertigo. It can be diagnosed and treated using simple maneuvers done by vestibular experts. However, patients with this condition presenting to the emergency department have high chance of being misdiagnosed. Such high rate of misdiagnosis results in significant morbidity to the patient and also incurs huge medical costs from unnecessary neuroimaging tests. Hence, automatic medical diagnosis is the next step to aid ED practitioners to reduce diagnostic errors. However, current software employed for this diagnosis has been found to have very low specificity. This can be attributed to factors such as low sampling frequency of recording device and the fact that bedside recordings from patients are susceptible to noise and artifacts. This study aims to improve methods for automatic quantification of nystagmus, a key sign of BPPV. Testing the current method using eye movement data recorded in patients during the diagnostic maneuver yielded better results than the commercial software