16,153 research outputs found
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
Computer- and robot-assisted Medical Intervention
Medical robotics includes assistive devices used by the physician in order to
make his/her diagnostic or therapeutic practices easier and more efficient.
This chapter focuses on such systems. It introduces the general field of
Computer-Assisted Medical Interventions, its aims, its different components and
describes the place of robots in that context. The evolutions in terms of
general design and control paradigms in the development of medical robots are
presented and issues specific to that application domain are discussed. A view
of existing systems, on-going developments and future trends is given. A
case-study is detailed. Other types of robotic help in the medical environment
(such as for assisting a handicapped person, for rehabilitation of a patient or
for replacement of some damaged/suppressed limbs or organs) are out of the
scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00
Externalising moods and psychological states in a cloud based system to enhance a pet-robot and child’s interaction
Background:This PATRICIA research project is about using pet robots to reduce pain
and anxiety in hospitalized children. The study began 2 years ago and it is believed that
the advances made in this project are significant. Patients, parents, nurses, psycholo-
gists, and engineers have adopted the Pleo robot, a baby dinosaur robotic pet, which
works in different ways to assist children during hospitalization.
Methods: Focus is spent on creating a wireless communication system with the Pleo
in order to help the coordinator, who conducts therapy with the child, monitor, under-
stand, and control Pleo’s behavior at any moment. This article reports how this techno-
logical function is being developed and tested.
Results: Wireless communication between the Pleo and an Android device is
achieved. The developed Android app allows the user to obtain any state of the robot
without stopping its interaction with the patient. Moreover, information is sent to a
cloud, so that robot moods, states and interactions can be shared among different
robots.
Conclusions: Pleo attachment was successful for more than 1 month, working with
children in therapy, which makes the investment capable of positive therapeutic
possibilities. This technical improvement in the Pleo addresses two key issues in social
robotics: needing an enhanced response to maintain the attention and engagement of
the child, and using the system as a platform to collect the states of the child’s progress
for clinical purposes.Peer ReviewedPostprint (published version
Outcomes of a virtual-reality simulator-training programme on basic surgical skills in robot-assisted laparoscopic surgery
Background The utility of the virtual-reality robotic simulator in training
programmes has not been clearly evaluated. Our aim was to evaluate the
impact of a virtual-reality robotic simulator-training programme on basic
surgical skills.
Methods A simulator-training programme in robotic surgery, using the da
Vinci Skills Simulator, was evaluated in a population including junior and seasoned
surgeons, and non-physicians. Their performances on robotic dots and
suturing-skin pod platforms before and after virtual-simulation training were
rated anonymously by surgeons experienced in robotics.
Results 39 participants were enrolled: 14 medical students and residents in
surgery, 14 seasoned surgeons, 11 non-physicians. Junior and seasoned
surgeons’ performances on platforms were not significantly improved after
virtual-reality robotic simulation in any of the skill domains, in contrast to
non-physicians.
Conclusions The benefits of virtual-reality simulator training on several
tasks to basic skills in robotic surgery were not obvious among surgeons
in our initial and early experience with the simulator
Design and Development of an Affordable Haptic Robot with Force-Feedback and Compliant Actuation to Improve Therapy for Patients with Severe Hemiparesis
The study describes the design and development of a single degree-of-freedom haptic robot, Haptic Theradrive, for post-stroke arm rehabilitation for in-home and clinical use. The robot overcomes many of the weaknesses of its predecessor, the TheraDrive system, that used a Logitech steering wheel as the haptic interface for rehabilitation. Although the original TheraDrive system showed success in a pilot study, its wheel was not able to withstand the rigors of use. A new haptic robot was developed that functions as a drop-in replacement for the Logitech wheel. The new robot can apply larger forces in interacting with the patient, thereby extending the functionality of the system to accommodate low-functioning patients. A new software suite offers appreciably more options for tailored and tuned rehabilitation therapies. In addition to describing the design of the hardware and software, the paper presents the results of simulation and experimental case studies examining the system\u27s performance and usability
Functional Electrical Stimulation mediated by Iterative Learning Control and 3D robotics reduces motor impairment in chronic stroke
Background: Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods: Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results: From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions: The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this
Ensuring the Service Quality of Long-Term Care Provided through Competitive Markets: The Experience of Care Workers' Training in Japan
Ensuring the service quality of long-term care provided through competitive markets is a major concern among the governments of OECD members. The public officials in these nations recognise the importance of care workers' training to address this issue. However, most of them have hesitated to introduce comprehensive training due to financial constraints. Analysing the experience of Japan, this paper reveals that governments can ensure the financial sustainability of care workers' training by aiming at the best possible long-term care.
Computational neurorehabilitation: modeling plasticity and learning to predict recovery
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity
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