Persona: González Boticario, Jesús
Cargando...
Dirección de correo electrónico
jgb@dia.uned.es
ORCID
0000-0003-4949-9220
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
González Boticario
Nombre de pila
Jesús
Nombre
24 resultados
Resultados de la búsqueda
Mostrando 1 - 10 de 24
Publicación Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios(MDPI, 2019-10-17) Uría Rivas, Raúl; Rodriguez Sanchez, Cristina; Santos, Olga C.; Vaquero, Joaquin; Jesus G. Boticario; González Boticario, Jesús; https://orcid.org/0000-0001-9243-2166; https://orcid.org/0000-0002-9281-4209; https://orcid.org/0000-0002-6976-0564Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional statesPublicación Inclusive personalized e-Learning based on affective adaptive support(Springer Nature, 2013) Salmeron Majadas, Sergio; Santos, Olga C.; González Boticario, JesúsEmotions and learning are closely related. In the PhD research presented in this paper, that relation has to be taken advantage of. With this aim, within the framework of affective computing, the main goal proposed is modeling learner’s affective state in order to support adaptive features and provide an inclusive personalized e-learning experience. At the first stage of this research, emotion detection is the principal issue to cope with. A multimodal approach has been proposed, so gathering data from diverse sources to feed data mining systems able to supply emotional information is being the current ongoing work. On the next stages, the results of these data mining systems will be used to enhance learner models and based on these, offer a better e-learning experience to improve learner’s results.Publicación Stakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education(Athabasca University, 2023) Holmes, Wayne; Iniesto, Francisco; Anastopoulou, Stamatina; González Boticario, Jesús; https://orcid.org/0000-0002-8352-1594; https://orcid.org/0000-0003-3946-3056; https://orcid.org/0000-0003-2550-2237Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from (a) the breadth and detail of the issues that have been identified, and (b) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice.Publicación An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations(MDPI, 2021-03-04) Serrano Mamolar, Ana; Arevalillo Herráez, Miguel; Chicote Huete, Guillermo; González Boticario, Jesús; https://orcid.org/0000-0002-0027-7128; https://orcid.org/0000-0002-0350-2079; https://orcid.org/0000-0002-7736-5572Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.Publicación Supporting growers with recommendations in redvides: some human aspects involved(Springer Nature, 2014-10-10) Santos, Olga C.; Salmeron Majadas, Sergio; González Boticario, JesúsThis paper discusses some human aspects that are to be considered when designing recommendations for RedVides, a cloud based networking environment that collects the status of the crop with sensors and can take decisions through corresponding actuators. The goal behind is to support growers in decision making processes, which can be benefited from collaborations among growers and with other stakeholders.Publicación Fusion of physiological signals for modeling driver awareness levels in conditional autonomous vehicles using semi-supervised learning(IEEE, 2024-10-11) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, JesúsThe evolution of autonomous vehicles (AVs) requires a paradigm shift towards the integration of human factors to improve safety and efficiency at levels 2,3 and 4 of automation. This paper presents a comparison of three different fusion technologies (Low-Level fusion, Medium-Level fusion, and a hybrid fusion), highlighting the critical role of multimodal data integration and semi-supervised learning in predicting and adapting to levels of driver awareness. Our approach uses semi-supervised learning to deal with the data labelling problem, using unlabelled data to train an autoencoder and sparsely labelled data to train a 4-state classifier. Our model facilitates the fusion of data from different physiological signals, including skin electrodermal activity, heart rate, body temperature and acceleration. Using real driving data, the Medium-Level fusion approach gives the best performance, achieving 84% accuracy in predicting situations where the user may not be aware enough to take control of the vehicle. This research highlights the essential nature of fusion technologies to create adaptive and user-centred AV systems.Publicación A domain-independent, transferable and timely analysis approach to assess student collaboration(World Scientific Publishing, 2013) Rodríguez Anaya, Antonio; González Boticario, JesúsCollaborative learning environments require intensive, regular and frequent analysis of the increasing amount of interaction data generated by students to assess that collaborative learning takes place. To support timely assessments that may benefit students and teachers the method of analysis must provide meaningful evaluations while the interactions take place. This research proposes machine learning-based techniques to infer the relationship between student collaboration and some quantitative domain-independent statistical indicators derived from large-scale evaluation analysis of student interactions. This paper (i) compares a set of metrics to identify the most suitable to assess student collaboration, (ii) reports on student evaluations of the metacognitive tools that display collaboration assessments from a new collaborative learning experience and (iii) extends previous findings to clarify modeling and usage issues. The advantages of the approach are: (1) it is based on domain-independent and generally observable features, (2) it provides regular and frequent data mining analysis with minimal teacher or student intervention, thereby supporting metacognition for the learners and corrective actions for the teachers, and (3) it can be easily transferred to other e-learning environments and include transferability features that are intended to facilitate its usage in other collaborative and social learning tools.Publicación Comparison of physiological data acquisition for modeling of drivers in autonomous vehicles(Springer Nature, 2025-04-24) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, JesúsHumans can undergo rapid emotional changes and these changes can significantly affect their ability to perform tasks. Consequently, when we develop Human-Centred Symbiotic Artificial Intelligence (HCSAI) systems to support the interaction between autonomous systems and drivers, the intelligent system controlling the vehicle must adapt to the state of the user. This symbiotic relationship highlights the importance of collaboration and cooperation between humans and agents of artificial intelligence (AI). In the field of Autonomous Vehicles (AV), measurements must be made using non-invasive devices that do not interfere with the driving task. We have therefore used wristbands to measure physiological signals. This comparison is used to select the right equipment for setting up user modelling in different levels of autonomous vehicles. We compared the accuracy, precision and ease of use of three different wristbands: Fitbit Sense2, Empatica E4 and Emotibit. We tested the performance of the bands in two different driving scenarios: SAE Level 4 environment using autonomous golf carts (iCab), and a real-world SAE Level 2 driving environment in Scotland using a Toyota Prius equipped with Comma OpenPilot technology. The Fitbit Sense2 does not allow researchers to access raw data. The Emotibit and the Empatica E4 are designed for research, so they provide access to raw data, while the Empatica E4 is easier to use than the Emotibit. The comparison calls for the development of open source codes that will facilitate integration with different operating systems and other devices, as well as an easy way to use the devices in real time.Publicación Red de Innovación Docente Accesibilidad y Diversidad Funcional en la Educación Superior: Análisis y desarrollo de los Servicios TIC requeridos(UNED, 2009-09) Campo, Elena del; González Boticario, Jesús; Saneiro Silva, María del Mar; Rodríguez Ascaso, Alejandro; Finat Walford, Cecilia RosaPublicación Problemas metodológicos de la enseñanza de las Ciencias: Fundamentos de la virtualización: experiencia en investigación y formación del profesorado(Universidad Nacional de Educación a Distancia (España). Facultad de Ciencias, 2001-01-01) González Boticario, Jesús
- «
- 1 (current)
- 2
- 3
- »