Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules

Occlusion reasoning for multiple object visual tracking

Thesis (Ph.D.)--Boston UniversityOcclusion reasoning for visual object tracking in uncontrolled environments is a challenging problem. It becomes significantly more difficult when dense groups of indistinguishable objects are present in the scene that cause frequent inter-object interactions and occlusions. We present several practical solutions that tackle the inter-object occlusions for video surveillance applications. In particular, this thesis proposes three methods. First, we propose "reconstruction-tracking," an online multi-camera spatial-temporal data association method for tracking large groups of objects imaged with low resolution. As a variant of the well-known Multiple-Hypothesis-Tracker, our approach localizes the positions of objects in 3D space with possibly occluded observations from multiple camera views and performs temporal data association in 3D. Second, we develop "track linking," a class of offline batch processing algorithms for long-term occlusions, where the decision has to be made based on the observations from the entire tracking sequence. We construct a graph representation to characterize occlusion events and propose an efficient graph-based/combinatorial algorithm to resolve occlusions. Third, we propose a novel Bayesian framework where detection and data association are combined into a single module and solved jointly. Almost all traditional tracking systems address the detection and data association tasks separately in sequential order. Such a design implies that the output of the detector has to be reliable in order to make the data association work. Our framework takes advantage of the often complementary nature of the two subproblems, which not only avoids the error propagation issue from which traditional "detection-tracking approaches" suffer but also eschews common heuristics such as "nonmaximum suppression" of hypotheses by modeling the likelihood of the entire image. The thesis describes a substantial number of experiments, involving challenging, notably distinct simulated and real data, including infrared and visible-light data sets recorded ourselves or taken from data sets publicly available. In these videos, the number of objects ranges from a dozen to a hundred per frame in both monocular and multiple views. The experiments demonstrate that our approaches achieve results comparable to those of state-of-the-art approaches

The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

Real-time robotic tasks for cyber-physical avatars

Although modern robots can perform complex tasks using sophisticated algorithms that are specialized to a particular task and environment, creating robots capable of completing tasks in unstructured environments without human guidance (e.g., through teleoperation) remains a challenge. In this research, we present a framework to meet this challenge for a "cyberphysical avatar," which is defined to be a semi-autonomous robotic system that adjusts to an unstructured environment and performs physical tasks subject to critical timing constraints while under human supervision. This thesis first realizes a cyberphysical avatar that integrates three key technologies: (1) whole body-compliant control, (2) skill acquisition from machine learning (neuroevolution methods and deep learning), and (3) vision-based control through visual servoing. Body-compliant control is essential for operator safety because avatars perform cooperative tasks in close proximity to humans; machine learning enables "programming" avatars such that they can be used by non-experts for a large array of tasks, some unforeseen, in an unstructured environment; the visual servoing technique is indispensable for facilitating feedback control in human avatar interaction. This thesis proposes and demonstrates a systematically incremental approach to automating robotic tasks by decomposing a non-trivial task into stages, each of which may be automated by integrating the aforementioned techniques. We design and implement the controllers for two semi-autonomous robots that integrate three key techniques for grasping and pick-and-place tasks. While a general theory is beyond reach, we present a study on the tradeoffs between three design metrics for robotic task systems: (1) the amount of training effort for the robots to perform the task, (2) the time available to complete the task when the command is given, and (3) the quality of the result of the performed task. The tradeoff study in this design space uses the imprecise computation model as a framework to evaluate specific types of tasks: (1) grasping an unknown object and (2) placing the object in a target position. We demonstrate the generality of our integration methodology by applying it to two different robots, Dreamer and Hoppy. Our approach is evaluated by the performance of the robots in trading off between task completion time, training time and task completion success rate, in an environment similar to those in the recent Amazon Picking Challenge.Computer Science

RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics