Exploring the Front Touch Interface for Virtual Reality Headsets

In this paper, we propose a new interface for virtual reality headset: a touchpad in front of the headset. To demonstrate the feasibility of the front touch interface, we built a prototype device, explored VR UI design space expansion, and performed various user studies. We started with preliminary tests to see how intuitively and accurately people can interact with the front touchpad. Then, we further experimented various user interfaces such as a binary selection, a typical menu layout, and a keyboard. Two-Finger and Drag-n-Tap were also explored to find the appropriate selection technique. As a low-cost, light-weight, and in low power budget technology, a touch sensor can make an ideal interface for mobile headset. Also, front touch area can be large enough to allow wide range of interaction types such as multi-finger interactions. With this novel front touch interface, we paved a way to new virtual reality interaction methods

Ocular attention-sensing interface system

The purpose of the research was to develop an innovative human-computer interface based on eye movement and voice control. By eliminating a manual interface (keyboard, joystick, etc.), OASIS provides a control mechanism that is natural, efficient, accurate, and low in workload

Human Performance Modeling For Two-Dimensional Dwell-Based Eye Pointing

Recently, Zhang et al. (2010) proposed an effective performance model for dwell-based eye pointing. However, their model was based on a specific circular target condition, without the ability to predict the performance of acquiring conventional rectangular targets. Thus, the applicability of such a model is limited. In this paper, we extend their one-dimensional model to two-dimensional (2D) target conditions. Carrying out two experiments, we have evaluated the abilities of different model candidates to find out the most appropriate one. The new index of difficulty we redefine for 2D eye pointing (IDeye) can properly reflect the asymmetrical impact of target width and height, which the later exceeds the former, and consequently the IDeyemodel can accurately predict the performance for 2D targets. Importantly, we also find that this asymmetry still holds for varying movement directions. According to the results of our study, we provide useful implications and recommendations for gaze-based interactions

Speed and Accuracy of Gaze Gestures

We conducted an experiment where participants carried out six gaze gesture tasks. The gaze paths were analyzed to find out the speed and accuracy of the gaze gestures. As a result, the gaze gestures took more time than we anticipated and only the very fastest participants got close to what was expected. There was not much difference in performance times between small and large gaze gestures, because the participants reached significantly faster speed when making large gestures than small gestures. Curved shapes were found difficult to follow and time-consuming when followed properly. In general, the accuracy in following shapes was sometimes very poor. We believe that to improve the speed and accuracy of gaze gestures, proper feedback must be used more than in our experiment

GraFIX: a semiautomatic approach for parsing low- and high-quality eye-tracking data

Fixation durations (FD) have been used widely as a measurement of information processing and attention. However, issues like data quality can seriously influence the accuracy of the fixation detection methods and, thus, affect the validity of our results (Holmqvist, Nyström, & Mulvey, 2012). This is crucial when studying special populations such as infants, where common issues with testing (e.g., high degree of movement, unreliable eye detection, low spatial precision) result in highly variable data quality and render existing FD detection approaches highly time consuming (hand-coding) or imprecise (automatic detection). To address this problem, we present GraFIX, a novel semiautomatic method consisting of a two-step process in which eye-tracking data is initially parsed by using velocity-based algorithms whose input parameters are adapted by the user and then manipulated using the graphical interface, allowing accurate and rapid adjustments of the algorithms’ outcome. The present algorithms (1) smooth the raw data, (2) interpolate missing data points, and (3) apply a number of criteria to automatically evaluate and remove artifactual fixations. The input parameters (e.g., velocity threshold, interpolation latency) can be easily manually adapted to fit each participant. Furthermore, the present application includes visualization tools that facilitate the manual coding of fixations. We assessed this method by performing an intercoder reliability analysis in two groups of infants presenting low- and high-quality data and compared it with previous methods. Results revealed that our two-step approach with adaptable FD detection criteria gives rise to more reliable and stable measures in low- and high-quality data

Development of an Eye-Gaze Input System With High Speed and Accuracy through Target Prediction Based on Homing Eye Movements

In this study, a method to predict a target on the basis of the trajectory of eye movements and to increase the pointing speed while maintaining high predictive accuracy is proposed. First, a predictive method based on ballistic (fast) eye movements (Approach 1) was evaluated in terms of pointing speed and predictive accuracy. In Approach 1, the so-called Midas touch problem (pointing to an unintended target) occurred, particularly when a small number of samples was used to predict a target. Therefore, to overcome the poor predictive accuracy of Approach 1, we developed a new predictive method (Approach 2) using homing (slow) eye movements rather than ballistic (fast) eye movements. Approach 2 overcame the disadvantage (inaccurate prediction) of Approach 1 by shortening the pointing time while maintaining high predictive accuracy

Adaptive eye-gaze tracking using neural-network-based user profiles to assist people with motor disability

This study developed an adaptive real-time humancomputer interface (HCI) that serves as an assistive technology tool for people with severe motor disability. The proposed HCI design uses eye gaze as the primary computer input device. Controlling the mouse cursor with raw eye coordinates results in sporadic motion of the pointer because of the saccadic nature of the eye. Even though eye movements are subtle and completely imperceptible under normal circumstances, they considerably affect the accuracy of an eye-gaze-based HCI. The proposed HCI system is novel because it adapts to each specific user’s different and potentially changing jitter characteristics through the configuration and training of an artificial neural network (ANN) that is structured to minimize the mouse jitter. This task is based on feeding the ANN a user’s initially recorded eye-gaze behavior through a short training session. The ANN finds the relationship between the gaze coordinates and the mouse cursor position based on the multilayer perceptron model. An embedded graphical interface is used during the training session to generate user profiles that make up these unique ANN configurations. The results with 12 subjects in test 1, which involved following a moving target, showed an average jitter reduction of 35%; the results with 9 subjects in test 2, which involved following the contour of a square object, showed an average jitter reduction of 53%. For both results, the outcomes led to trajectories that were significantly smoother and apt at reaching fixed or moving targets with relative ease and within a 5% error margin or deviation from desired trajectories. The positive effects of such jitter reduction are presented graphically for visual appreciation

Keeping an eye on the game: Eye gaze interaction with massively multiplayer online games and virtual communities for motor impaired users.

Online virtual communities are becoming increasingly popular both within the able-bodied and disabled user communities. These games assume the use of keyboard and mouse as standard input devices, which in some cases is not appropriate for users with a disability. This paper explores gaze-based interaction methods and highlights the problems associated with gaze control of online virtual worlds. The paper then presents a novel ‘Snap Clutch’ software tool that addresses these problems and enables gaze control. The tool is tested with an experiment showing that effective gaze control is possible although task times are longer. Errors caused by gaze control are identified and potential methods for reducing these are discussed. Finally, the paper demonstrates that gaze driven locomotion can potentially achieve parity with mouse and keyboard driven locomotion, and shows that gaze is a viable modality for game based locomotion both for able-bodied and disabled users alike