Evaluation of Binocular Eye Trackers and Algorithms for 3D Gaze Interaction in Virtual Reality Environments

Tracking user’s visual attention is a fundamental aspect in novel human-computer interaction paradigms found in Virtual Reality. For example, multimodal interfaces or dialogue-based communications with virtual and real agents greatly benefit from the analysis of the user’s visual attention as a vital source for deictic references or turn-taking signals. Current approaches to determine visual attention rely primarily on monocular eye trackers. Hence they are restricted to the interpretation of two-dimensional fixations relative to a defined area of projection. The study presented in this article compares precision, accuracy and application performance of two binocular eye tracking devices. Two algorithms are compared which derive depth information as required for visual attention-based 3D interfaces. This information is further applied to an improved VR selection task in which a binocular eye tracker and an adaptive neural network algorithm is used during the disambiguation of partly occluded objects.

Eye-head coordination abnormalities in schizophrenia

Eye-movement abnormalities in schizophrenia are a well-established phenomenon that has been observed in many studies. In such studies, visual targets are usually presented in the center of the visual field, and the subject's head remains fixed. However, in every-day life, targets may also appear in the periphery. This study is among the first to investigate eye and head movements in schizophrenia by presenting targets in the periphery of the visual field.

Analysis of eye and head coordination in a visual peripheral recognition task

Coordinated eye and head movements simultaneously occur to scan the visual world for relevant targets. However, measuring both eye and head movements in experiments allowing natural head movements may be challenging. This paper provides an approach to study eye-head coordination: First, we demonstra- te the capabilities and limits of the eye-head tracking system used, and compare it to other technologies. Second, a beha- vioral task is introduced to invoke eye-head coordination. Third, a method is introduced to reconstruct signal loss in video- based oculography caused by cornea reflection artifacts in order to extend the tracking range. Finally, parameters of eye- head coordination are identified using EHCA (eye-head co- ordination analyzer), a MATLAB software which was developed to analyze eye-head shifts. To demonstrate the capabilities of the approach, a study with 11 healthy subjects was performed to investigate motion behavior. The approach presented here is discussed as an instrument to explore eye-head coordination, which may lead to further insights into attentional and motor symptoms of certain neurological or psychiatric diseases, e.g., schizophrenia.