Robocatch: Design and Making of a Hand-Held Spillage-Free Specimen Retrieval Robot for Laparoscopic Surgery

Specimen retrieval is an important step in laparoscopy, a minimally invasive surgical procedure performed to diagnose and treat a myriad of medical pathologies in fields ranging from gynecology to oncology. Specimen retrieval bags (SRBs) are used to facilitate this task, while minimizing contamination of neighboring tissues and port-sites in the abdominal cavity. This manual surgical procedure requires usage of multiple ports, creating a traffic of simultaneous operations of multiple instruments in a limited shared workspace. The skill-demanding nature of this procedure makes it time-consuming, leading to surgeons’ fatigue and operational inefficiency. This thesis presents the design and making of RoboCatch, a novel hand-held robot that aids a surgeon in performing spillage-free retrieval of operative specimens in laparoscopic surgery. The proposed design significantly modifies and extends conventional instruments that are currently used by surgeons for the retrieval task: The core instrumentation of RoboCatch comprises a webbed three-fingered grasper and atraumatic forceps that are concentrically situated in a folded configuration inside a trocar. The specimen retrieval task is achieved in six stages: 1) The trocar is introduced into the surgical site through an instrument port, 2) the three webbed fingers slide out of the tube and simultaneously unfold in an umbrella like-fashion, 3) the forceps slide toward, and grasp, the excised specimen, 4) the forceps retract the grasped specimen into the center of the surrounding grasper, 5) the grasper closes to achieve a secured containment of the specimen, and 6) the grasper, along with the contained specimen, is manually removed from the abdominal cavity. The resulting reduction in the number of active ports reduces obstruction of the port-site and increases the procedure’s efficiency. The design process was initiated by acquiring crucial parameters from surgeons and creating a design table, which informed the CAD modeling of the robot structure and selection of actuation units and fabrication material. The robot prototype was first examined in CAD simulation and then fabricated using an Objet30 Prime 3D printer. Physical validation experiments were conducted to verify the functionality of different mechanisms of the robot. Further, specimen retrieval experiments were conducted with porcine meat samples to test the feasibility of the proposed design. Experimental results revealed that the robot was capable of retrieving masses of specimen ranging from 1 gram to 50 grams. The making of RoboCatch represents a significant step toward advancing the frontiers of hand-held robots for performing specimen retrieval tasks in minimally invasive surgery

Complementary Situational Awareness for an Intelligent Telerobotic Surgical Assistant System

Robotic surgical systems have contributed greatly to the advancement of Minimally Invasive Surgeries (MIS). More specifically, telesurgical robots have provided enhanced dexterity to surgeons performing MIS procedures. However, current robotic teleoperated systems have only limited situational awareness of the patient anatomy and surgical environment that would typically be available to a surgeon in an open surgery. Although the endoscopic view enhances the visualization of the anatomy, perceptual understanding of the environment and anatomy is still lacking due to the absence of sensory feedback. In this work, these limitations are addressed by developing a computational framework to provide Complementary Situational Awareness (CSA) in a surgical assistant. This framework aims at improving the human-robot relationship by providing elaborate guidance and sensory feedback capabilities for the surgeon in complex MIS procedures. Unlike traditional teleoperation, this framework enables the user to telemanipulate the situational model in a virtual environment and uses that information to command the slave robot with appropriate admittance gains and environmental constraints. Simultaneously, the situational model is updated based on interaction of the slave robot with the task space environment. However, developing such a system to provide real-time situational awareness requires that many technical challenges be met. To estimate intraoperative organ information continuous palpation primitives are required. Intraoperative surface information needs to be estimated in real-time while the organ is being palpated/scanned. The model of the task environment needs to be updated in near real-time using the estimated organ geometry so that the force-feedback applied on the surgeon's hand would correspond to the actual location of the model. This work presents a real-time framework that meets these requirements/challenges to provide situational awareness of the environment in the task space. Further, visual feedback is also provided for the surgeon/developer to view the near video frame rate updates of the task model. All these functions are executed in parallel and need to have a synchronized data exchange. The system is very portable and can be incorporated to any existing telerobotic platforms with minimal overhead

Design, analysis and trajectory tracking control of underactuated mobile capsule robots.

The research on capsule robots (capsubots) has received attraction in recent years because of their compactness, simple structure and their potential use in medical diagnosis (e.g. capsule endoscopy), treatment and surgical assistance. The medical diagnostic capability of a capsule endoscope - which moves with the aid of visceral peristalsis - in the GI (gastro-intestinal) tract can be improved by adding propulsion to it e.g. legged, magnetic or capsubot-type propulsion. Driven by the above needs this thesis presents the design, analysis, trajectory tracking control and implementation of underactuated mobile capsule robots. These capsule robots can be modified and used in in-vivo medical applications. Researches on the capsubottype underactuated system focus on the stabilization of the robot and tracking the actuated configuration. However trajectory tracking control of an unactuated configuration (i.e. the robotmotion)was not considered in the literature though it is the primary requirement of any mobile robot and also crucial for many applications such as in-vivo inspection. Trajectory tracking control for this class of underactuated mechanical systems is still an open issue. This thesis presents a strategy to solve this issue. This thesis presents three robots namely a one-dimensional (1D) capsule robot, a 2D capsule robot and a 2D hybrid capsule robot with incremental capability. Two new acceleration profiles (utroque and contrarium) for the inner mass (IM) - internal moving part of the capsule robot - are proposed, analysed and implemented for the motion generation of the capsule robots. This thesis proposes a two-stage control strategy for the motion control of an underactuated capsule robot. A segment-wise trajectory tracking algorithm is developed for the 1D capsule robot. Theoretical analysis of the algorithm is presented and simulation is performed in the Matlab/Simulink environment based on the theoretical analysis. The algorithm is implemented in the developed capsule robot, the experimentation is performed and the results are critically analyzed. A trajectory tracking control algorithm combining segment-wise and behaviour-based control is proposed for the 2D capsule robot. Detailed theoretical analysis is presented and the simulation is performed to investigate the robustness of the trajectory tracking algorithm to friction uncertainties. A 2D capsule robot prototype is developed and the experimentation is performed. A novel 2D hybrid robot with four modes of operation - legless motion mode, legged motion mode, hybrid motion mode and anchoring mode - is also designed which uses one set of actuators in all operating modes. The theoretical analysis, modelling and simulation is performed. This thesis demonstrates effective ways of propulsion for in-vivo applications. The outer-shape of the 1D and 2D capsule robots can be customized according to the requirement of the applications, as the propulsion mechanisms are completely internal. These robots are also hermetically sealable (enclosed) which is a safety feature for the in-vivo robots. This thesis addresses the trajectory tracking control of the capsubot-type robot for the first time. During the experimentation the 1D robot prototype tracks the desired position trajectory with some error (relative mean absolute error: 16%). The trajectory tracking performance for the 2D capsubot improves as the segment time decreases whereas tracking performance declines as the friction uncertainty increases. The theoretical analysis, simulation and experimental results validate the proposed acceleration profiles and trajectory tracking control algorithms. The designed hybrid robot combines the best aspects of the legless and legged motions. The hybrid robot is capable of stopping in a suspected region and remain stationary for a prolonged observation for the in-vivo applications while withstanding the visceral peristalsis

On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered