77,913 research outputs found

    Gesture Recognition in Robotic Surgery: a Review

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    OBJECTIVE: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. METHODS: An article search was performed on 5 bibliographic databases with combinations of the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. RESULTS: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. CONCLUSION: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. SIGNIFICANCE: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field

    TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS

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    This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies

    A vision for global monitoring of biological invasions

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    Managing biological invasions relies on good global coverage of species distributions. Accurate information on alien species distributions, obtained from international policy and cross-border co-operation, is required to evaluate trans-boundary and trading partnership risks. However, a standardized approach for systematically monitoring alien species and tracking biological invasions is still lacking. This Perspective presents a vision for global observation and monitoring of biological invasions. We show how the architecture for tracking biological invasions is provided by a minimum information set of Essential Variables, global collaboration on data sharing and infrastructure, and strategic contributions by countries. We show how this novel, synthetic approach to an observation system for alien species provides a tangible and attainable solution to delivering the information needed to slow the rate of new incursions and reduce the impacts of invaders. We identify three Essential Variables for Invasion Monitoring; alien species occurrence, species alien status and alien species impact. We outline how delivery of this minimum information set by joint, complementary contributions from countries and global community initiatives is possible. Country contributions are made feasible using a modular approach where all countries are able to participate and strategically build their contributions to a global information set over time. The vision we outline will deliver wide-ranging benefits to countries and international efforts to slow the rate of biological invasions and minimize their environmental impacts. These benefits will accrue over time as global coverage and information on alien species increases

    Mercury: using the QuPreSS reference model to evaluate predictive services

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    Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft
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