In the eye of the beholder: A survey of gaze tracking techniques

Abstract Understanding user perceptions of interacting with the virtual world is one of the research focuses in recent years, given the rapid proliferation of virtual reality (VR) and driven to establish the metaverse. Users can generate a familiar connection between their bodies and the virtual world by being embodied in virtual hands, and hand representations can induce users’ embodiment in VR. The sense of embodiment represents the cognitive awareness of one's manifestation and includes three subcomponents: the sense of body ownership, agency and self-location. There is insufficient evidence in the literature about the effects of hand designs on the embodiment, especially based on studying its three subcomponents. This study investigates how virtual hand designs with five realism levels influence the three subcomponents of embodiment in VR. This research employs a self-report questionnaire commonly used in the literature to assess embodiment and evaluates agency and self-location by introducing implicit methods (intentional binding and proprioceptive measurement) derived from psychology. Besides, the objective data of eye tracking is used to explore the connection between embodiment and hand designs, and classifying participants’ eye tracking data to help analyze the link between embodiment and user attention. Overall, this research makes a major contribution through a systematic exploration of users’ embodied experience in VR and offers important evidence of the effects of virtual hand designs on body ownership, agency, and self-location, respectively. In addition, this study provides a valuable reference for further investigation of embodiment through implicit and objective methods, and practical design recommendations for virtual hand design in VR applications. 

Understanding the effects of hand design on embodiment in virtual reality

Teleoperated systems enable human control of robotic proxies and are particularly amenable to inaccessible environments unsuitable for autonomy. Examples include emergency response, underwater manipulation, and robot assisted minimally invasive surgery. However, teleoperation architectures have been predominantly employed in manipulation tasks, and are thus only useful when the robot is within reach of the task. This work introduces the idea of extending teleoperation to enable online human remote control of legged robots, or telelocomotion, to traverse challenging terrain. Traversing unpredictable terrain remains a challenge for autonomous legged locomotion, as demonstrated by robots commonly falling in high-profile robotics contests. Telelocomotion can reduce the risk of mission failure by leveraging the high-level understanding of human operators to command in real-time the gaits of legged robots. In this work, a haptic telelocomotion interface was developed. Two within-user studies validate the proof-of-concept interface: (i) The first compared basic interfaces with the haptic interface for control of a simulated hexapedal robot in various levels of traversal complexity; (ii) the second presents a physical implementation and investigated the efficacy of the proposed haptic virtual fixtures. Results are promising to the use of haptic feedback for telelocomotion for complex traversal tasks.

Telelocomotion—Remotely Operated Legged Robots

Haptic feedback can render real-time force interactions with computer simulated objects. In several telerobotic applications, it is desired that a haptic simulation reflects a physical task space or interaction accurately. This is particularly true when excessive applied force can result in disastrous consequences, as with the case of robot-assisted minimally invasive surgery (RMIS) and tissue damage. Since force cannot be directly measured in RMIS, non-contact methods are desired. A promising direction of non-contact force estimation involves the primary use of vision sensors to estimate deformation. However, the required fidelity of non-contact force rendering of deformable interaction to maintain surgical operator performance is not well established. This work attempts to empirically evaluate the degree to which haptic feedback may deviate from ground truth yet result in acceptable teleoperated performance in a simulated RMIS-based palpation task. A preliminary user-study is conducted to verify the utility of the simulation platform, and the results of this work have implications in haptic feedback for RMIS and inform guidelines for vision-based tool-tissue force estimation. An adaptive thresholding method is used to collect the minimum and maximum tolerable errors in force orientation and magnitude of presented haptic feedback to maintain sufficient performance.

Characterizing Limits of Vision-Based Force Feedback in Simulated Surgical Tool-Tissue Interaction

Legged locomotion offers flexibility and maneuverability over wheeled or tracked systems. The advantages allow legged robots to traverse a wide array of heterogeneous terrains that are otherwise inaccessible. Examples of such scenarios include search and rescue, field applications, and space exploration. On the other hand, generalized autonomous walking is difficult - step planning, dynamic analysis, balance control, gait execution and adaptive foothold selection must execute in concert for successful legged locomotion. This paper presents a method for leveraging human decision making and adaptability to control legged robot walking with a haptic interface. The magnitude and direction of force feedback as well as average step size were tracked during basic locomotion. Overall robot navigation trajectory was also enhanced with haptic feedback.

Haptic Interface for Hexapod Gait Execution

Gaze tracking presents an intuitive interface for technology in today’s society, with its application focus in controlling electronic devices. This paper concentrates on the design and application of an automatic gaze tracking system utilizing commodity equipment. Compared to preceding low-cost methods, the proposed method is significantly simpler, lowering the barrier of entry for this type of device, and can potentially afford more accurate tracking. Through the careful placement of the infrared (IR) light-emitting-diodes (LEDs) on the monitor and coaxially to the optical axis of the camera, the pupil was illuminated and reference glints became visible on the cornea. These glints were captured by a camera capable of detecting IR light, and were used to determine the users line of sight relative to the monitor. A linear model was used to address the horizontal and vertical components of the glints in the users eye and match them to the corresponding location point on the monitor. K-means clustering was utilized to classify the separate gaze regions with promising results.

A Data-Driven Approach for Gaze Tracking

Teleoperation of robotic proxies can extend human control to spaces, tasks and constraints that would otherwise be intractable or unattainable. The robustness to harsh environments, scalability, precision and repeatability of robots make them ideal for dangerous or difficult missions, and their use oftentimes reduces monetary and human cost. Maneuverable robot devices can provide more flexibility and articulation, but come at the cost of kinematic complexity. The kinematics and workspace of the remote device is oftentimes dissimilar to the input device, leading to potential confusion and frustration of the human operator. One solution is to constrain the input device motion to a scaled version of remote device joint ranges. This paper presents a method for doing so with 3 degree of freedom (DOF) manipulators and input devices with kinematic dissimilarities. The approach utilizes a simple tree structure, whereby a local Cartesian workspace limit is sampled and indexed by joint. This generates a locally sampled joint limit surface, represented as a point cloud. This local point cloud is then used to provide 3DOF haptic feedback to the operator as an indication that a joint limit has been reached, and provides kinesthetic force feedback to efficiently remove the operator from that joint limit. This work can improve usability of human-robot interfaces for teleoperation by allowing users to naturally intuit remote device joint limits and avoid confusion.

Sampling of 3DOF Robot Manipulator Joint-Limits for Haptic Feedback

Applications that use human gaze have become increasingly more popular in the domain of human-computer interfaces, and advances in eye gaze tracking technology over the past few decades have led to the development of promising gaze estimation techniques. In this paper, a low-cost, in-house video camera-based gaze tracking system was developed, trained and evaluated. Seminal gaze detection methods constrained the application space to indoor conditions, and in most cases techniques required intrusive hardware. More modern gaze detection techniques try to eliminate the use of any additional hardware to reduce monetary cost as well as undue burden to the user, all the while maintaining accuracy of detection. In this work, image acquisition was achieved using a low-cost USB web camera mounted at a fixed position on the viewing screen or laptop. In order to determine the point of gaze, the Viola Jones face detection algorithm is used to extract facial features from the image frame. The gaze is then calculated using image processing techniques to extract gaze features, namely related to the image position of the pupil. Thousands of images are classified and labeled to form an in-house database. A multi-class Support Vector Machine (SVM) was trained and tested on this data set to distinguish point of gaze from input face image. Cross validation was used to train the model. Confusion matrices, accuracy, precision, and recall are used to evaluate the performance of the classification model. Evaluation of the proposed appearance-based technique using two different kernel functions is also assessed in detail.

Appearance-Based Gaze Tracking Through Supervised Machine Learning

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A Data-Driven Approach for Gaze Tracking

Sampling of 3DOF Robot Manipulator Joint-Limits for Haptic Feedback

Appearance-Based Gaze Tracking Through Supervised Machine Learning