A gaze-contingent framework for perceptually-enabled applications in healthcare

Patient safety and quality of care remain the focus of the smart operating room of the future. Some of the most influential factors with a detrimental effect are related to suboptimal communication among the staff, poor flow of information, staff workload and fatigue, ergonomics and sterility in the operating room. While technological developments constantly transform the operating room layout and the interaction between surgical staff and machinery, a vast array of opportunities arise for the design of systems and approaches, that can enhance patient safety and improve workflow and efficiency. The aim of this research is to develop a real-time gaze-contingent framework towards a "smart" operating suite, that will enhance operator's ergonomics by allowing perceptually-enabled, touchless and natural interaction with the environment. The main feature of the proposed framework is the ability to acquire and utilise the plethora of information provided by the human visual system to allow touchless interaction with medical devices in the operating room. In this thesis, a gaze-guided robotic scrub nurse, a gaze-controlled robotised flexible endoscope and a gaze-guided assistive robotic system are proposed. Firstly, the gaze-guided robotic scrub nurse is presented; surgical teams performed a simulated surgical task with the assistance of a robot scrub nurse, which complements the human scrub nurse in delivery of surgical instruments, following gaze selection by the surgeon. Then, the gaze-controlled robotised flexible endoscope is introduced; experienced endoscopists and novice users performed a simulated examination of the upper gastrointestinal tract using predominately their natural gaze. Finally, a gaze-guided assistive robotic system is presented, which aims to facilitate activities of daily living. The results of this work provide valuable insights into the feasibility of integrating the developed gaze-contingent framework into clinical practice without significant workflow disruptions.Open Acces

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

LIMBUSTRACK: STABLE EYE-TRACKING IN IMPERFECT LIGHT CONDITIONS

We are aware of only one serious effort at development of a cheap, accurate, wearable eye tracker: the open source openEyes project. However, its method of ocular feature detection is such that it is prone to failure in variable lighting conditions. To address this deficiency, we have developed a cheap wearable eye tracker. At the heart of our development are novel techniques that allow operation under variable illumination

Event Detection in Eye-Tracking Data for Use in Applications with Dynamic Stimuli

This doctoral thesis has signal processing of eye-tracking data as its main theme. An eye-tracker is a tool used for estimation of the point where one is looking. Automatic algorithms for classification of different types of eye movements, so called events, form the basis for relating the eye-tracking data to cognitive processes during, e.g., reading a text or watching a movie. The problems with the algorithms available today are that there are few algorithms that can handle detection of events during dynamic stimuli and that there is no standardized procedure for how to evaluate the algorithms. This thesis comprises an introduction and four papers describing methods for detection of the most common types of eye movements in eye-tracking data and strategies for evaluation of such methods. The most common types of eye movements are fixations, saccades, and smooth pursuit movements. In addition to these eye movements, the event post-saccadic oscillations, (PSO), is considered. The eye-tracking data in this thesis are recorded using both high- and low-speed eye-trackers. The first paper presents a method for detection of saccades and PSO. The saccades are detected using the acceleration signal and three specialized criteria based on directional information. In order to detect PSO, the interval after each saccade is modeled and the parameters of the model are used to determine whether PSO are present or not. The algorithm was evaluated by comparing the detection results to manual annotations and to the detection results of the most recent PSO detection algorithm. The results show that the algorithm is in good agreement with annotations, and has better performance than the compared algorithm. In the second paper, a method for separation of fixations and smooth pursuit movements is proposed. In the intervals between the detected saccades/PSO, the algorithm uses different spatial scales of the position signal in order to separate between the two types of eye movements. The algorithm is evaluated by computing five different performance measures, showing both general and detailed aspects of the discrimination performance. The performance of the algorithm is compared to the performance of a velocity and dispersion based algorithm, (I-VDT), to the performance of an algorithm based on principle component analysis, (I-PCA), and to manual annotations by two experts. The results show that the proposed algorithm performs considerably better than the compared algorithms. In the third paper, a method based on eye-tracking signals from both eyes is proposed for improved separation of fixations and smooth pursuit movements. The method utilizes directional clustering of the eye-tracking signals in combination with binary filters taking both temporal and spatial aspects of the eye-tracking signal into account. The performance of the method is evaluated using a novel evaluation strategy based on automatically detected moving objects in the video stimuli. The results show that the use of binocular information for separation of fixations and smooth pursuit movements is advantageous in static stimuli, without impairing the algorithm's ability to detect smooth pursuit movements in video and moving dot stimuli. The three first papers in this thesis are based on eye-tracking signals recorded using a stationary eye-tracker, while the fourth paper uses eye-tracking signals recorded using a mobile eye-tracker. In mobile eye-tracking, the user is allowed to move the head and the body, which affects the recorded data. In the fourth paper, a method for compensation of head movements using an inertial measurement unit, (IMU), combined with an event detector for lower sampling rate data is proposed. The event detection is performed by combining information from the eye-tracking signals with information about objects extracted from the scene video of the mobile eye-tracker. The results show that by introducing head movement compensation and information about detected objects in the scene video in the event detector, improved classification can be achieved. In summary, this thesis proposes an entire methodological framework for robust event detection which performs better than previous methods when analyzing eye-tracking signals recorded during dynamic stimuli, and also provides a methodology for performance evaluation of event detection algorithms

Gaze-tracking-based interface for robotic chair guidance

This research focuses on finding solutions to enhance the quality of life for wheelchair users, specifically by applying a gaze-tracking-based interface for the guidance of a robotized wheelchair. For this purpose, the interface was applied in two different approaches for the wheelchair control system. The first one was an assisted control in which the user was continuously involved in controlling the movement of the wheelchair in the environment and the inclination of the different parts of the seat through the user’s gaze and eye blinks obtained with the interface. The second approach was to take the first steps to apply the device to an autonomous wheelchair control in which the wheelchair moves autonomously avoiding collisions towards the position defined by the user. To this end, the basis for obtaining the gaze position relative to the wheelchair and the object detection was developed in this project to be able to calculate in the future the optimal route to which the wheelchair should move. In addition, the integration of a robotic arm in the wheelchair to manipulate different objects was also considered, obtaining in this work the object of interest indicated by the user's gaze within the detected objects so that in the future the robotic arm could select and pick up the object the user wants to manipulate. In addition to the two approaches, an attempt was also made to estimate the user's gaze without the software interface. For this purpose, the gaze is obtained from pupil detection libraries, a calibration and a mathematical model that relates pupil positions to gaze. The results of the implementations have been analysed in this work, including some limitations encountered. Nevertheless, future improvements are proposed, with the aim of increasing the independence of wheelchair user

Peer Attention Modeling with Head Pose Trajectory Tracking Using Temporal Thermal Maps

Human head pose trajectories can represent a wealth of implicit information such as areas of attention, body language, potential future actions, and more. This signal is of high value for use in Human-Robot teams due to the implicit information encoded within it. Although team-based tasks require both explicit and implicit communication among peers, large team sizes, noisy environments, distance, and mission urgency can inhibit the frequency and quality of explicit communication. The goal for this thesis is to improve the capabilities of Human-Robot teams by making use of implicit communication. In support of this goal, the following hypotheses are investigated: ● Implicit information about a human subject’s attention can be reliably extracted with software by tracking the subject’s head pose trajectory, and ● Attention can be represented with a 3D temporal thermal map for implicitly determining a subject’s Objects Of Interest (OOIs). These hypotheses are investigated by experimentation with a new tool for peer attention modeling by Head Pose Trajectory Tracking using Temporal Thermal Maps (HPT4M). This system allows a robot Observing Agent (OA) to view a human teammate and temporally model their Regions Of Interest (ROIs) by generating a 3D thermal map based on the subject’s head pose trajectory. The findings in this work are that HPT4M can be used by an OA to contribute to a team search mission by implicitly discovering a human subject’s OOI type, mapping the item’s location within the searched space, and labeling the item’s discovery state. Furthermore, this work discusses some of the discovered limitations of this technology and hurdles that must be overcome before implementing HPT4M in a reliable real-world system. Finally, the techniques used in this work are provided as an open source Robot Operating System (ROS) node at github.com/HPT4M with the intent that it will aid other developers in the robotics community with improving Human-Robot teams. Furthermore, the proofs of principle and tools developed in this thesis are a foundational platform for deeper investigation in future research on improving Human-Robot teams via implicit communication techniques

3D head motion, point-of-regard and encoded gaze fixations in real scenes: next-generation portable video-based monocular eye tracking

Portable eye trackers allow us to see where a subject is looking when performing a natural task with free head and body movements. These eye trackers include headgear containing a camera directed at one of the subject\u27s eyes (the eye camera) and another camera (the scene camera) positioned above the same eye directed along the subject\u27s line-of-sight. The output video includes the scene video with a crosshair depicting where the subject is looking -- the point-of-regard (POR) -- that is updated for each frame. This video may be the desired final result or it may be further analyzed to obtain more specific information about the subject\u27s visual strategies. A list of the calculated POR positions in the scene video can also be analyzed. The goals of this project are to expand the information that we can obtain from a portable video-based monocular eye tracker and to minimize the amount of user interaction required to obtain and analyze this information. This work includes offline processing of both the eye and scene videos to obtain robust 2D PORs in scene video frames, identify gaze fixations from these PORs, obtain 3D head motion and ray trace fixations through volumes-of-interest (VOIs) to determine what is being fixated, when and where (3D POR). To avoid the redundancy of ray tracing a 2D POR in every video frame and to group these POR data meaningfully, a fixation-identification algorithm is employed to simplify the long list of 2D POR data into gaze fixations. In order to ray trace these fixations, the 3D motion -- position and orientation over time -- of the scene camera is computed. This camera motion is determined via an iterative structure and motion recovery algorithm that requires a calibrated camera and knowledge of the 3D location of at least four points in the scene (that can be selected from premeasured VOI vertices). The subjects 3D head motion is obtained directly from this camera motion. For the final stage of the algorithm, the 3D locations and dimensions of VOIs in the scene are required. This VOI information in world coordinates is converted to camera coordinates for ray tracing. A representative 2D POR position for each fixation is converted from image coordinates to the same camera coordinate system. Then, a ray is traced from the camera center through this position to determine which (if any) VOI is being fixated and where it is being fixated -- the 3D POR in the world. Results are presented for various real scenes. Novel visualizations of portable eye tracker data created using the results of our algorithm are also presented