Examinando por Autor "Quintero, L."

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    Adaptive architecture to support context-aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
    (SPRINGER, 2014-01-01) Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session. © 2014 Springer International Publishing.
  • No hay miniatura disponible
    Ítem
    Adaptive architecture to support context-aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
    (SPRINGER, 2014-01-01) Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de Procesos
    Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session. © 2014 Springer International Publishing.
  • No hay miniatura disponible
    Ítem
    Adaptive architecture to support context-aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
    (SPRINGER, 2014-01-01) Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session. © 2014 Springer International Publishing.
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    Ítem
    An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
    (SPRINGER, 2017-01-01) Gómez, A.; Quintero, L.; López, N.; Castro, J.; Villa, L.; Mejía, G.; Gómez, A.; Quintero, L.; López, N.; Castro, J.; Villa, L.; Mejía, G.; Universidad EAFIT. Departamento de Ciencias; Matemáticas y Aplicaciones
    In this work, we perform an approach to emotion recognition from Electroencephalography (EEG) single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are decomposed by several types of wavelets and each subsignal are processed using several window sizes by performing a statistical analysis. Finally, three types of classifiers were used, obtaining accuracy rate between 50% to 87% for the emotional states such as happiness, sadness and neutrality. © Springer Nature Singapore Pte Ltd. 2017.
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    An approach to emotion recognition in single-channel EEG signals using stationarywavelet transform
    (SPRINGER, 2017-01-01) Gómez, A.; Quintero, L.; López, N.; Castro, J.; Villa, L.; Mejía, G.; Gómez, A.; Quintero, L.; López, N.; Castro, J.; Villa, L.; Mejía, G.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    In this work, we perform an approach to emotion recognition from Electroencephalography (EEG) single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are decomposed by several types of wavelets and each subsignal are processed using several window sizes by performing a statistical analysis. Finally, three types of classifiers were used, obtaining accuracy rate between 50% to 87% for the emotional states such as happiness, sadness and neutrality. © Springer Nature Singapore Pte Ltd. 2017.
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    An approach to emotion recognition in single-channel EEG signals: a mother child interaction
    (IOP Publishing, 2016) Gómez, A.; Quintero, L.; López, N.; Castro, J.; Gómez, A.; Quintero, L.; López, N.; Castro, J.; Mathematical Modeling Research Group, GRIMMAT, School of Sciences, Universidad EAFIT, Medellín, Colombia; Medical Technology Laboratory, Faculty of Engineering, Universidad Nacional de San Juan, Argentina; Psychology, Education and Culture Research Group Faculty of Social Science Politécnico Grancolombiano University Institution, Argentina; Universidad EAFIT. Escuela de Ciencias; agomez13@eafit.edu.co; oquinte1@eafit.edu.co; Modelado Matemático
    In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology -- Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains -- Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness
  • No hay miniatura disponible
    Ítem
    An approach to emotion recognition in single-channel EEG signals: A mother child interaction
    (IOP PUBLISHING LTD, 2016-01-01) Gómez, A.; Quintero, L.; López, N.; Castro, J.; Gómez, A.; Quintero, L.; López, N.; Castro, J.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains. Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness.
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    Ítem
    Interpolation Based Controller for Trajectory Tracking in Mobile Robots
    (SPRINGER, 2017-06-01) Serrano, M.E.; Godoy, S.A.; Quintero, L.; Scaglia, G.J.E.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    In this work, a novel algorithm for trajectory tracking in mobile robots is presented. For the purpose of tracking trajectory, a methodology based on the interpolation of trigonometric functions of the wheeled mobile robot kinematics is proposed. In addition, the convergence of the interpolation-based control systems is analysed. Furthermore, the optimal controller parameters are selected through Monte Carlo Experiments (MCE) in order to minimize a cost index. The MCE is able to find, the best set of gains that minimizes the tracking error. Experimental results over a mobile robot Pionner 3AT are conclusive and satisfactory. In addition, a comparative study of control performance is carried out against another controllers. © 2016, Springer Science+Business Media Dordrecht.