Collaborative Gaze Channelling for Improved Cooperation During Robotic Assisted Surgery

The use of multiple robots for performing complex tasks is becoming a common practice for many robot applications. When different operators are involved, effective cooperation with anticipated manoeuvres is important for seamless, synergistic control of all the end-effectors. In this paper, the concept of Collaborative Gaze Channelling (CGC) is presented for improved control of surgical robots for a shared task. Through eye tracking, the fixations of each operator are monitored and presented in a shared surgical workspace. CGC permits remote or physically separated collaborators to share their intention by visualising the eye gaze of their counterparts, and thus recovers, to a certain extent, the information of mutual intent that we rely upon in a vis-à-vis working setting. In this study, the efficiency of surgical manipulation with and without CGC for controlling a pair of bimanual surgical robots is evaluated by analysing the level of coordination of two independent operators. Fitts' law is used to compare the quality of movement with or without CGC. A total of 40 subjects have been recruited for this study and the results show that the proposed CGC framework exhibits significant improvement (p<0.05) on all the motion indices used for quality assessment. This study demonstrates that visual guidance is an implicit yet effective way of communication during collaborative tasks for robotic surgery. Detailed experimental validation results demonstrate the potential clinical value of the proposed CGC framework. © 2012 Biomedical Engineering Society.link_to_subscribed_fulltex

Control theoretic models of pointing

This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer’s Crossover, Costello’s Surge, second-order lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data. We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts’ law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design

Evaluating 3D pointing techniques

"This dissertation investigates various issues related to the empirical evaluation of 3D pointing interfaces. In this context, the term ""3D pointing"" is appropriated from analogous 2D pointing literature to refer to 3D point selection tasks, i.e., specifying a target in three-dimensional space. Such pointing interfaces are required for interaction with virtual 3D environments, e.g., in computer games and virtual reality. Researchers have developed and empirically evaluated many such techniques. Yet, several technical issues and human factors complicate evaluation. Moreover, results tend not to be directly comparable between experiments, as these experiments usually use different methodologies and measures. Based on well-established methods for comparing 2D pointing interfaces this dissertation investigates different aspects of 3D pointing. The main objective of this work is to establish methods for the direct and fair comparisons between 2D and 3D pointing interfaces. This dissertation proposes and then validates an experimental paradigm for evaluating 3D interaction techniques that rely on pointing. It also investigates some technical considerations such as latency and device noise. Results show that the mouse outperforms (between 10% and 60%) other 3D input techniques in all tested conditions. Moreover, a monoscopic cursor tends to perform better than a stereo cursor when using stereo display, by as much as 30% for deep targets. Results suggest that common 3D pointing techniques are best modelled by first projecting target parameters (i.e., distance and size) to the screen plane.

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

A Software Framework For Task Based Performance Evaluation

It is difficult to objectively measure performance of complex tasks such as a surgical operation and surgical simulators require the ability to evaluate performance whether to predict surgical outcome, determine competence, provide learning feedback, etc. With no standard software framework for collecting, analyzing and evaluating performance data for complex tasks in simulations, it is investigated whether a solution can be implemented that allows for custom data collection schemes, all while being general enough to be used across many simulation platforms and can be used in a simple simulator.It is also investigated whether the implemented framework can perform its functionality while leaving a small performance footprint on the simulator. Hierarchical task analysis is investigated as a means to decompose complex tasks into their simpler sub-tasks, where data can be collected for each task and evaluated.The framework is based on hierarchical task representation to allow robust performance data of a complex task to be collected and evaluated for any type of application.A client application is developed and allows for the generation of custom scenario parameters for the task, robust performance data collection and the ability to playback previous performances for evaluation purposes.It is shown that the implemented framework has a small peformance footprint and does not affect the performance of the simulator that is using the framework for performance data collection and evaluation

The Investigation of Laparoscopic Instrument Movement Control and Learning Effect

Laparoscopic surgery avoids large incisions for intra-abdominal operations as required in conventional open surgery. Whereas the patient benefits from laparoscopic techniques, the surgeon encounters new difficulties that were not present during open surgery procedures. However, limited literature has been published in the essential movement characteristics such as magnification, amplitude, and angle. For this reason, the present study aims to investigate the essential movement characteristics of instrument manipulation via Fitts' task and to develop an instrument movement time predicting model. Ten right-handed subjects made discrete Fitts' pointing tasks using a laparoscopic trainer. The experimental results showed that there were significant differences between the three factors in movement time and in throughput. However, no significant differences were observed in the improvement rate for movement time and throughput between these three factors. As expected, the movement time was rather variable and affected markedly by direction to target. The conventional Fitts' law model was extended by incorporating a directional parameter into the model. The extended model was shown to better fit the data than the conventional model. These findings pointed to a design direction for the laparoscopic surgery training program, and the predictive model can be used to establish standards in the training procedure

Adaptive human force scaling via admittance control for physical human-robot interaction

The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.WOS:000731146900006Scopus - Affiliation ID: 60105072Q2ArticleUluslararası işbirliği ile yapılan - EVETOctober2021YÖK - 2021-22Eki