"Wink to grasp" – comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves

The ability of robotic rehabilitation devices to support paralysed end-users is ultimately limited by the degree to which human-machine-interaction is designed to be effective and efficient in translating user intention into robotic action. Specifically, we evaluate the novel possibility of binocular eye-tracking technology to detect voluntary winks from involuntary blink commands, to establish winks as a novel low-latency control signal to trigger robotic action. By wearing binocular eye-tracking glasses we enable users to directly observe their environment or the actuator and trigger movement actions, without having to interact with a visual display unit or user interface. We compare our novel approach to two conventional approaches for controlling robotic devices based on electromyo-graphy (EMG) and speech-based human-computer interaction technology. We present an integrated software framework based on ROS that allows transparent integration of these multiple modalities with a robotic system. We use a soft-robotic SEM glove (Bioservo Technologies AB, Sweden) to evaluate how the 3 modalities support the performance and subjective experience of the end-user when movement assisted. All 3 modalities are evaluated in streaming, closed-loop control operation for grasping physical objects. We find that wink control shows the lowest error rate mean with lowest standard deviation of (0.23 ± 0.07, mean ± SEM) followed by speech control (0.35 ± 0. 13) and EMG gesture control (using the Myo armband by Thalamic Labs), with the highest mean and standard deviation (0.46 ± 0.16). We conclude that with our novel own developed eye-tracking based approach to control assistive technologies is a well suited alternative to conventional approaches, especially when combined with 3D eye-tracking based robotic end-point control

Hand eye coordination in surgery

The coordination of the hand in response to visual target selection has always been regarded as an essential quality in a range of professional activities. This quality has thus far been elusive to objective scientific measurements, and is usually engulfed in the overall performance of the individuals. Parallels can be drawn to surgery, especially Minimally Invasive Surgery (MIS), where the physical constraints imposed by the arrangements of the instruments and visualisation methods require certain coordination skills that are unprecedented. With the current paradigm shift towards early specialisation in surgical training and shortened focused training time, selection process should identify trainees with the highest potentials in certain specific skills. Although significant effort has been made in objective assessment of surgical skills, it is only currently possible to measure surgeons’ abilities at the time of assessment. It has been particularly difficult to quantify specific details of hand-eye coordination and assess innate ability of future skills development. The purpose of this thesis is to examine hand-eye coordination in laboratory-based simulations, with a particular emphasis on details that are important to MIS. In order to understand the challenges of visuomotor coordination, movement trajectory errors have been used to provide an insight into the innate coordinate mapping of the brain. In MIS, novel spatial transformations, due to a combination of distorted endoscopic image projections and the “fulcrum” effect of the instruments, accentuate movement generation errors. Obvious differences in the quality of movement trajectories have been observed between novices and experts in MIS, however, this is difficult to measure quantitatively. A Hidden Markov Model (HMM) is used in this thesis to reveal the underlying characteristic movement details of a particular MIS manoeuvre and how such features are exaggerated by the introduction of rotation in the endoscopic camera. The proposed method has demonstrated the feasibility of measuring movement trajectory quality by machine learning techniques without prior arbitrary classification of expertise. Experimental results have highlighted these changes in novice laparoscopic surgeons, even after a short period of training. The intricate relationship between the hands and the eyes changes when learning a skilled visuomotor task has been previously studied. Reactive eye movement, when visual input is used primarily as a feedback mechanism for error correction, implies difficulties in hand-eye coordination. As the brain learns to adapt to this new coordinate map, eye movements then become predictive of the action generated. The concept of measuring this spatiotemporal relationship is introduced as a measure of hand-eye coordination in MIS, by comparing the Target Distance Function (TDF) between the eye fixation and the instrument tip position on the laparoscopic screen. Further validation of this concept using high fidelity experimental tasks is presented, where higher cognitive influence and multiple target selection increase the complexity of the data analysis. To this end, Granger-causality is presented as a measure of the predictability of the instrument movement with the eye fixation pattern. Partial Directed Coherence (PDC), a frequency-domain variation of Granger-causality, is used for the first time to measure hand-eye coordination. Experimental results are used to establish the strengths and potential pitfalls of the technique. To further enhance the accuracy of this measurement, a modified Jensen-Shannon Divergence (JSD) measure has been developed for enhancing the signal matching algorithm and trajectory segmentations. The proposed framework incorporates high frequency noise filtering, which represents non-purposeful hand and eye movements. The accuracy of the technique has been demonstrated by quantitative measurement of multiple laparoscopic tasks by expert and novice surgeons. Experimental results supporting visual search behavioural theory are presented, as this underpins the target selection process immediately prior to visual motor action generation. The effects of specialisation and experience on visual search patterns are also examined. Finally, pilot results from functional brain imaging are presented, where the Posterior Parietal Cortical (PPC) activation is measured using optical spectroscopy techniques. PPC has been demonstrated to involve in the calculation of the coordinate transformations between the visual and motor systems, which establishes the possibilities of exciting future studies in hand-eye coordination

A.Eye Drive: gaze-based semi-autonomous wheelchair interface

Existing wheelchair control interfaces, such as sip & puff or screen based gaze-controlled cursors, are challenging for the severely disabled to navigate safely and independently as users continuously need tointeract with an interface during navigation. This putsa significant cognitive load on users and prevents them from interacting with the environment in other forms during navigation. We have combined eyetracking/gaze-contingent intention decoding with computervision context-awarealgorithms and autonomous navigation drawn fromself-driving vehicles to allow paralysed users to drive by eye, simply by decoding natural gaze about where the user wants to go: A.Eye Drive. Our “Zero UI” driving platform allows users to look and interact visually with at an objector destination of interest in their visual scene, and the wheelchairautonomously takes the user to the intended destination, while continuously updating the computed path for static and dynamic obstacles. This intention decoding technology empowers the end-user by promising more independence through their own agency

FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping

Wearable and Assistive robotics for human grasp support are broadly either tele-operated robotic arms or act through orthotic control of a paralyzed user's hand. Such devices require correct orientation for successful and efficient grasping. In many human-robot assistive settings, the end-user is required to explicitly control the many degrees of freedom making effective or efficient control problematic. Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping. This paper describes a compact algorithm implementing fast computer vision techniques to obtain the orientation of the target object to be grasped, by segmenting the images acquired with a camera positioned on top of the end-effector of the robotic device. The rotation needed that optimises grasping is directly computed from the object's orientation. The algorithm has been evaluated in 6 different scene backgrounds and end-effector approaches to 26 different objects. 94.8% of the objects were detected in all backgrounds. Grasping of the object was achieved in 91.1% of the cases and has been evaluated with a robot simulator confirming the performance of the algorithm

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

Multimodality with Eye tracking and Haptics: A New Horizon for Serious Games?

The goal of this review is to illustrate the emerging use of multimodal virtual reality that can benefit learning-based games. The review begins with an introduction to multimodal virtual reality in serious games and we provide a brief discussion of why cognitive processes involved in learning and training are enhanced under immersive virtual environments. We initially outline studies that have used eye tracking and haptic feedback independently in serious games, and then review some innovative applications that have already combined eye tracking and haptic devices in order to provide applicable multimodal frameworks for learning-based games. Finally, some general conclusions are identified and clarified in order to advance current understanding in multimodal serious game production as well as exploring possible areas for new applications