Resolving Target Ambiguity in 3D Gaze Interaction through VOR Depth Estimation

Target disambiguation is a common problem in gaze interfaces, as eye tracking has accuracy and precision limitations. In 3D environments this is compounded by objects overlapping in the field of view, as a result of their positioning at different depth with partial occlusion. We introduce \textit{VOR depth estimation}, a method based on the vestibulo-ocular reflex of the eyes in compensation of head movement, and explore its application to resolve target ambiguity. The method estimates gaze depth by comparing the rotations of the eye and the head when the users look at a target and deliberately rotate their head. We show that VOR eye movement presents an alternative to vergence for gaze depth estimation, that is feasible also with monocular tracking. In an evaluation of its use for target disambiguation, our method outperforms vergence for targets presented at greater depth

Eye&Head:Synergetic Eye and Head Movement for Gaze Pointing and Selection

Eye gaze involves the coordination of eye and head movement to acquire gaze targets, but existing approaches to gaze pointing are based on eye-tracking in abstraction from head motion. We propose to leverage the synergetic movement of eye and head, and identify design principles for Eye&Head gaze interaction. We introduce three novel techniques that build on the distinction of head-supported versus eyes-only gaze, to enable dynamic coupling of gaze and pointer, hover interaction, visual exploration around pre-selections, and iterative and fast confirmation of targets. We demonstrate Eye&Head interaction on applications in virtual reality, and evaluate our techniques against baselines in pointing and confirmation studies. Our results show that Eye&Head techniques enable novel gaze behaviours that provide users with more control and flexibility in fast gaze pointing and selection

GazeSwitch : Automatic Eye-Head Mode Switching for Optimised Hands-Free Pointing

This paper contributes GazeSwitch, an ML-based technique that optimises the real-time switching between eye and head modes for fast and precise hands-free pointing. GazeSwitch reduces false positives from natural head movements and efficiently detects head gestures for input, resulting in an effective hands-free and adaptive technique for interaction. We conducted two user studies to evaluate its performance and user experience. Comparative analyses with baseline switching techniques, Eye+Head Pinpointing (manual) and BimodalGaze (threshold-based) revealed several trade-offs. We found that GazeSwitch provides a natural and effortless experience but trades off control and stability compared to manual mode switching, and requires less head movement compared to BimodalGaze. This work demonstrates the effectiveness of machine learning approach to learn and adapt to patterns in head movement, allowing us to better leverage the synergistic relation between eye and head input modalities for interaction in mixed and extended reality

Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input

Head movement is widely used as a uniform type of input for human-computer interaction. However, there are fundamental differences between head movements coupled with gaze in support of our visual system, and head movements performed as gestural expression. Both Head-Gaze and Head Gestures are of utility for interaction but differ in their affordances. To facilitate the treatment of Head-Gaze and Head Gestures as separate types of input, we developed HeadBoost as a novel classifier, achieving high accuracy in classifying gaze-driven versus gestural head movement (F1-Score: 0.89). We demonstrate the utility of the classifier with three applications: gestural input while avoiding unintentional input by Head-Gaze; target selection with Head-Gaze while avoiding Midas Touch by head gestures; and switching of cursor control between Head-Gaze for fast positioning and Head Gesture for refinement. The classification of Head-Gaze and Head Gesture allows for seamless head-based interaction while avoiding false activation

User Experience in Virtual Reality, conducting an evaluation on multiple characteristics of a Virtual Reality Experience

Virtual Reality applications are today numerous and cover a wide range of interests and tastes. As popularity of Virtual Reality increases, developers in industry are trying to create engrossing and exciting experiences that captivate the interest of users. User-Experience, a term used in the field of Human-Computer Interaction and Interaction Design, describes multiple characteristics of the experience of a person interacting with a product or a system. Evaluating User-Experience can provide valuable insight to developers and researchers on the thoughts and impressions of the end users in relation to a system. However, little information exists regarding on how to conduct User-Experience evaluations in the context of Virtual Reality. Consecutively, due to the numerous parameters that influence User-Experience in Virtual Reality, conducting and organizing evaluations can be overwhelming and challenging. The author of this thesis investigated how to conduct a User-Experience evaluation on multiple aspects of a Virtual Reality headset by identifying characteristics of the experience, and the methods that can be used to measure and evaluate them. The data collected was both qualitative and quantitative to cover a wide range of characteristics of the experience. Furthermore, the author applied usability testing, think-aloud protocol, questionnaires and semi-structured interview as methods to observe user behavior and collect information regarding the aspects of the Virtual Reality headset. The testing session described in this study included 14 participants. Data from this study showed that the combination of chosen methods were able to provide adequate information regarding the experience of the users despite encountered difficulties. Additionally, this thesis showcases which methods were used to evaluate specific aspects of the experience and the performance of each method as findings of the study

Eye, Head and Torso Coordination During Gaze Shifts in Virtual Reality

Humans perform gaze shifts naturally through a combination of eye, head and body movements. Although gaze has been long studied as input modality for interaction, this has previously ignored the coordination of the eyes, head and body. This article reports a study of gaze shifts in virtual reality (VR) aimed to address the gap and inform design. We identify general eye, head and torso coordination patterns and provide an analysis of the relative movements' contribution and temporal alignment. We quantify effects of target distance, direction and user posture, describe preferred eye-in-head motion ranges, and identify a high variability in head movement tendency. Study insights lead us to propose gaze zones that reflect different levels of contribution from eye, head and body. We discuss design implications for HCI and VR, and in conclusion argue to treat gaze as multimodal input, and eye, head and body movement as synergetic in interaction design

Assisting Navigation and Object Selection with Vibrotactile Cues

Our lives have been drastically altered by information technology in the last decades, leading to evolutionary mismatches between human traits and the modern environment. One particular mismatch occurs when visually demanding information technology overloads the perceptual, cognitive or motor capabilities of the human nervous system. This information overload could be partly alleviated by complementing visual interaction with haptics. The primary aim of this thesis was to investigate how to assist movement control with vibrotactile cues. Vibrotactile cues refer to technologymediated vibrotactile signals that notify users of perceptual events, propose users to make decisions, and give users feedback from actions. To explore vibrotactile cues, we carried out five experiments in two contexts of movement control: navigation and object selection. The goal was to find ways to reduce information load in these tasks, thus helping users to accomplish the tasks more effectively. We employed measurements such as reaction times, error rates, and task completion times. We also used subjective rating scales, short interviews, and free-form participant comments to assess the vibrotactile assisted interactive systems. The findings of this thesis can be summarized as follows. First, if the context of movement control allows the use of both feedback and feedforward cues, feedback cues are a reasonable first option. Second, when using vibrotactile feedforward cues, using low-level abstractions and supporting the interaction with other modalities can keep the information load as low as possible. Third, the temple area is a feasible actuation location for vibrotactile cues in movement control, including navigation cues and object selection cues with head turns. However, the usability of the area depends on contextual factors such as spatial congruency, the actuation device, and the pace of the interaction task