Graphical user interface

The graphical user interface, or GUI (ˌdʒi:juːˈaɪ or ˈɡu:i ), is a form of user interface that allows users to interact with electronic devices through graphical icons and audio indicators such as primary notation, instead of text-based UIs, typed command labels or text navigation. GUIs were introduced in reaction to the perceived steep learning curve of command-line interfaces (CLIs), which require commands to be typed on a computer keyboard. The actions in a GUI are usually performed through direct manipulation of the graphical elements. Beyond computers, GUIs are used in many handheld mobile devices such as MP3 players, portable media players, gaming devices, smartphones and smaller household, office and industrial controls. The term GUI tends not to be applied to other lower-display resolution types of interfaces, such as video games (where head-up displays (HUDs) are preferred), or not including flat screens like volumetric displays because the term is restricted to the scope o

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An Chi Chen, Mackenzie Mathis, Alexander Mathis, Tanmay Nath

Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1–12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory.

2019

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

An Chi Chen, Mackenzie Mathis, Alexander Mathis, Tanmay Nath

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

Neuroscience2018

New horizons for patient safety: LIGRA (LIfe Guard for Robotic surgery Assistance) An interactive platform centralizing information and control in robotic surgery Operating Rooms

Hannes Bleuler

The present paper describes LIGRA, an innovative technological solution developed in the context of the SAFROS co-funded EU project. Its general goal is to improve patient safety during minimal invasive robotic surgery. Focusing on the risks introduced in the surgical workflow by new robotic technologies, the LIGRA project consists in the design and implementation of a graphical user interface (GUI), on top of a dedicated middleware for message transport. This GUI is intuitive, non-invasive and versatile. Being visible to the whole surgical staff and easily understandable, the interface monitors the status of all robotic components, informs about any potential dangerous situation and displays the needed steps to recover. In addition, LIGRA records the data sent by the components, allowing retrieving technical and procedural information about the surgery and the staff afterwards. Furthermore, it gives access to an interactive version of the World Health Organization Patient Safety Checklist that recalls the essential checks and verifications to be made prior, during, and after the surgery. The approach adopted during this contribution, justified by the lack of a similar tool in OR nowadays, is to establish a common framework, which could have an important positive impact on patient safety.

Ieee2013

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