HazARdSnap: Gazed-based Augmentation Delivery for Safe Information Access while Cycling.

During cycling activities, cyclists often monitor a variety of information such as heart rate, distance, and navigation using a bike-mounted phone or cyclocomputer. In many cases, cyclists also ride on sidewalks or paths that contain pedestrians and other obstructions such as potholes, so monitoring information on a bike-mounted interface can slow the cyclist down or cause accidents and injury. In this paper, we present HazARdSnap, an augmented reality-based information delivery approach that improves the ease of access to cycling information and at the same time preserves the user's awareness of hazards. To do so, we implemented real-time outdoor hazard detection using a combination of computer vision and motion and position data from a head mounted display (HMD). We then developed an algorithm that snaps information to detected hazards when they are also viewed so that users can simultaneously view both rendered virtual cycling information and the real-world cues such as depth, position, time to hazard, and speed that are needed to assess and avoid hazards. Results from a study with 24 participants that made use of real-world cycling and virtual hazards showed that both HazARdSnap and forward-fixed augmented reality (AR) user interfaces (UIs) can effectively help cyclists access virtual information without having to look down, which resulted in fewer collisions (51% and 43% reduction compared to baseline, respectively) with virtual hazards.

Mitigation of VR Sickness During Locomotion With a Motion-Based Dynamic Vision Modulator.

In virtual reality, VR sickness resulting from continuous locomotion via controllers or joysticks is still a significant problem. In this article, we present a set of algorithms to mitigate VR sickness that dynamically modulate the user's field of view by modifying the contrast of the periphery based on movement, color, and depth. In contrast with previous work, this vision modulator is a shader that is triggered by specific motions known to cause VR sickness, such as acceleration, strafing, and linear velocity. Moreover, the algorithm is governed by delta velocity, delta angle, and average color of the view. We ran two experiments with different washout periods to investigate the effectiveness of dynamic modulation on the symptoms of VR sickness, in which we compared this approach against a baseline and pitch-black field-of-view restrictors. Our first experiment made use of a just-noticeable-sickness design, which can be useful for building experiments with a short washout period.

The Influence of Label Design on Search Performance and Noticeability in Wide Field of View Augmented Reality Displays.

In Augmented Reality (AR), search performance for outdoor tasks is an important metric for evaluating the success of a large number of AR applications. Users must be able to find content quickly, labels and indicators must not be invasive but still clearly noticeable, and the user interface should maximize search performance in a variety of conditions. To address these issues, we have set up a series of experiments to test the influence of virtual characteristics such as color, size, and leader lines on the performance of search tasks and noticeability in both real and simulated environments. We evaluate two primary areas, including 1) the effects of peripheral field of view (FOV) limitations and labeling techniques on target acquisition during outdoor mobile search, and 2) the influence of local characteristics such as color, size, and motion on text labels over dynamic backgrounds. The first experiment showed that limited FOV will severely limit search performance, but that appropriate placement of labels and leaders within the periphery can alleviate this problem without interfering with walking or decreasing user comfort. In the second experiment, we found that different types of motion are more noticeable in optical versus video see-through displays, but that blue coloration is most noticeable in both. Results can aid in designing more effective view management techniques, especially for wider field of view displays.

Emulation of Physician Tasks in Eye-Tracked Virtual Reality for Remote Diagnosis of Neurodegenerative Disease.

For neurodegenerative conditions like Parkinson's disease, early and accurate diagnosis is still a difficult task. Evaluations can be time consuming, patients must often travel to metropolitan areas or different cities to see experts, and misdiagnosis can result in improper treatment. To date, only a handful of assistive or remote methods exist to help physicians evaluate patients with suspected neurological disease in a convenient and consistent way. In this paper, we present a low-cost VR interface designed to support evaluation and diagnosis of neurodegenerative disease and test its use in a clinical setting. Using a commercially available VR display with an infrared camera integrated into the lens, we have constructed a 3D virtual environment designed to emulate common tasks used to evaluate patients, such as fixating on a point, conducting smooth pursuit of an object, or executing saccades. These virtual tasks are designed to elicit eye movements commonly associated with neurodegenerative disease, such as abnormal saccades, square wave jerks, and ocular tremor. Next, we conducted experiments with 9 patients with a diagnosis of Parkinson's disease and 7 healthy controls to test the system's potential to emulate tasks for clinical diagnosis. We then applied eye tracking algorithms and image enhancement to the eye recordings taken during the experiment and conducted a short follow-up study with two physicians for evaluation. Results showed that our VR interface was able to elicit five common types of movements usable for evaluation, physicians were able to confirm three out of four abnormalities, and visualizations were rated as potentially useful for diagnosis.

ModulAR: eye-controlled vision augmentations for head mounted displays.

In the last few years, the advancement of head mounted display technology and optics has opened up many new possibilities for the field of Augmented Reality. However, many commercial and prototype systems often have a single display modality, fixed field of view, or inflexible form factor. In this paper, we introduce Modular Augmented Reality (ModulAR), a hardware and software framework designed to improve flexibility and hands-free control of video see-through augmented reality displays and augmentative functionality. To accomplish this goal, we introduce the use of integrated eye tracking for on-demand control of vision augmentations such as optical zoom or field of view expansion. Physical modification of the device's configuration can be accomplished on the fly using interchangeable camera-lens modules that provide different types of vision enhancements. We implement and test functionality for several primary configurations using telescopic and fisheye camera-lens systems, though many other customizations are possible. We also implement a number of eye-based interactions in order to engage and control the vision augmentations in real time, and explore different methods for merging streams of augmented vision into the user's normal field of view. In a series of experiments, we conduct an in depth analysis of visual acuity and head and eye movement during search and recognition tasks. Results show that methods with larger field of view that utilize binary on/off and gradual zoom mechanisms outperform snapshot and sub-windowed methods and that type of eye engagement has little effect on performance.