8,251 research outputs found
NOViSE: a virtual natural orifice transluminal endoscopic surgery simulator
Purpose: Natural Orifice Transluminal Endoscopic Surgery (NOTES) is a novel technique in minimally invasive surgery whereby a flexible endoscope is inserted via a natural orifice to gain access to the abdominal cavity, leaving no external scars. This innovative use of flexible endoscopy creates many new challenges and is associated with a steep learning curve for clinicians. Methods: We developed NOViSE - the first force-feedback enabled virtual reality simulator for NOTES training supporting a flexible endoscope. The haptic device is custom built and the behaviour of the virtual flexible endoscope is based on an established theoretical framework – the Cosserat Theory of Elastic Rods. Results: We present the application of NOViSE to the simulation of a hybrid trans-gastric cholecystectomy procedure. Preliminary results of face, content and construct validation have previously shown that NOViSE delivers the required level of realism for training of endoscopic manipulation skills specific to NOTES Conclusions: VR simulation of NOTES procedures can contribute to surgical training and improve the educational experience without putting patients at risk, raising ethical issues or requiring expensive animal or cadaver facilities. In the context of an experimental technique, NOViSE could potentially facilitate NOTES development and contribute to its wider use by keeping practitioners up to date with this novel surgical technique. NOViSE is a first prototype and the initial results indicate that it provides promising foundations for further development
Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks
Social intelligence is an important requirement for enabling robots to
collaborate with people. In particular, human path prediction is an essential
capability for robots in that it prevents potential collision with a human and
allows the robot to safely make larger movements. In this paper, we present a
method for predicting the trajectory of a human who follows a haptic robotic
guide without using sight, which is valuable for assistive robots that aid the
visually impaired. We apply a deep learning method based on recurrent neural
networks using multimodal data: (1) human trajectory, (2) movement of the
robotic guide, (3) haptic input data measured from the physical interaction
between the human and the robot, (4) human depth data. We collected actual
human trajectory and multimodal response data through indoor experiments. Our
model outperformed the baseline result while using only the robot data with the
observed human trajectory, and it shows even better results when using
additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201
Tac-tiles: multimodal pie charts for visually impaired users
Tac-tiles is an accessible interface that allows visually impaired users to browse graphical information using tactile and audio feedback. The system uses a graphics tablet which is augmented with a tangible overlay tile to guide user exploration. Dynamic feedback is provided by a tactile pin-array at the fingertips, and through speech/non-speech audio cues. In designing the system, we seek to preserve the affordances and metaphors of traditional, low-tech teaching media for the blind, and combine this with the benefits of a digital representation. Traditional tangible media allow rapid, non-sequential access to data, promote easy and unambiguous access to resources such as axes and gridlines, allow the use of external memory, and preserve visual conventions, thus promoting collaboration with sighted colleagues. A prototype system was evaluated with visually impaired users, and recommendations for multimodal design were derived
Enabling collaboration in virtual reality navigators
In this paper we characterize a feature superset for Collaborative
Virtual Reality Environments (CVRE), and derive a component
framework to transform stand-alone VR navigators into full-fledged
multithreaded collaborative environments. The contributions of our
approach rely on a cost-effective and extensible technique for
loading software components into separate POSIX threads for
rendering, user interaction and network communications, and adding a
top layer for managing session collaboration. The framework recasts
a VR navigator under a distributed peer-to-peer topology for scene
and object sharing, using callback hooks for broadcasting remote
events and multicamera perspective sharing with avatar interaction.
We validate the framework by applying it to our own ALICE VR
Navigator. Experimental results show that our approach has good
performance in the collaborative inspection of complex models.Postprint (published version
Prevalence of haptic feedback in robot-mediated surgery : a systematic review of literature
© 2017 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in Journal of Robotic Surgery. The final authenticated version is available online at: https://doi.org/10.1007/s11701-017-0763-4With the successful uptake and inclusion of robotic systems in minimally invasive surgery and with the increasing application of robotic surgery (RS) in numerous surgical specialities worldwide, there is now a need to develop and enhance the technology further. One such improvement is the implementation and amalgamation of haptic feedback technology into RS which will permit the operating surgeon on the console to receive haptic information on the type of tissue being operated on. The main advantage of using this is to allow the operating surgeon to feel and control the amount of force applied to different tissues during surgery thus minimising the risk of tissue damage due to both the direct and indirect effects of excessive tissue force or tension being applied during RS. We performed a two-rater systematic review to identify the latest developments and potential avenues of improving technology in the application and implementation of haptic feedback technology to the operating surgeon on the console during RS. This review provides a summary of technological enhancements in RS, considering different stages of work, from proof of concept to cadaver tissue testing, surgery in animals, and finally real implementation in surgical practice. We identify that at the time of this review, while there is a unanimous agreement regarding need for haptic and tactile feedback, there are no solutions or products available that address this need. There is a scope and need for new developments in haptic augmentation for robot-mediated surgery with the aim of improving patient care and robotic surgical technology further.Peer reviewe
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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