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González Boticario, Jesús

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0000-0003-4949-9220
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González Boticario
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Jesús
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2013 - 201972020 - 20253
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Mostrando 1 - 10 de 11
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    Publicación
    Improving autonomous vehicle automation through human-system interaction
    (EUROSIS) Fernandez Matellan, Raul; Martin Gomez, David; Tena Gago, David; Puertas Ramírez, David; González Boticario, Jesús
    Self-driving cars (a.k.a. Autonomous Vehicles) have many challenges to tackle before having them fully deployed in our roads and cities. A critical one, which has been somehow neglected till recently, is to consider the driver in the system-user loop of vehicle performance. The purpose here is to tackle some of the current pending challenges involved in scaling up the level of autonomy of these systems. We have designed two user-vehicle experiences in two different sites with a common methodology that serves as an umbrella to collect all features required to model the driver-user. These two sites allow us to contrast and fine-tune this modelling issue. The approach consists in following a Learning Apprentice approach, where both the user behaviour and the system behaviour are learned and improved in a symbiotic ecosystem. This paper focuses on discussing the advantages of this approach and the main issues that require further research.
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    Aprendizaje basado en tareas compartidas: una metodología consolidada para fomentar la involucración en el aprendizaje "online"
    (Universidad Nacional de Educación a Distancia (España). Instituto Universitario de Educación a Distancia (IUED), 2019) González Boticario, Jesús
    Cualesquiera que sean los medios y circunstancias presentes en los procesos de enseñanza y aprendizaje, un objetivo siempre presente es lograr un aprendizaje en el que el estudiante se involucre realmente, a través de su trabajo y el de otros con los que comparte el proceso. Desde 2001 hasta la fecha se ha venido puliendo una estrategia de aprendizaje en cursos virtuales basada en tareas y en colaboración, aplicada en una gran variedad de escenarios, incluyendo asignaturas, cursos esporádicos y MOOC s. El planteamiento se basa en potenciar la adquisición de competencias de colaboración e interacción social. Se aprende construyendo documentación compartida y elaborando soluciones a partir de situaciones y casos reales de aplicación relacionados con la materia. Todo se realiza a través de la red y las fuentes de información abiertas son la base del proceso. Se resumen aquí algunos aspectos de interés en asignaturas de grado, máster y curso orientado a la investigación y doctorado. Los resultados son variados precisamente por la propia naturaleza de dichos cursos, siendo las asignaturas de grado en las que se observa mayor éxito.
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    Challenges for Inclusive Affective Detection in Educational Scenarios
    (Springer Nature, 2013) Santos, Olga C.; Rodríguez Ascaso, Alejandro; González Boticario, Jesús; Salmeron Majadas, Sergio; Quirós Expósito, Pilar; Cabestrero Alonso, Raúl
    There exist diverse challenges for inclusive emotions detection in educational scenarios. In order to gain some insight about the difficulties and limitations of them, we have analyzed requirements, accommodations and tasks that need to be adapted for an experiment where people with different functional profiles have taken part. Adaptations took into consideration logistics, tasks involved and user interaction techniques. The main aim was to verify to what extent the same approach, measurements and technological infrastructure already used in previous experiments were adequate for inducing emotions elicited from the execution of the experiment tasks. In the paper, we discuss the experiment arrangements needed to cope with people with different functional profiles, which include adaptations on the analysis and results. Such analysis was validated in a pilot experiment with 3 visually impaired participants.
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    Exploring arduino for building educational context-aware recommender systems that deliver affective recommendations in social ubiquitous networking environments
    (Springer, 2014-10-10) Santos, Olga C.; González Boticario, Jesús
    One of the most challenging context features to detect when making recommendations in educational scenarios is the learner’s affective state. Usually, this feature is explicitly gathered from the learner herself through questionnaires or self-reports. In this paper, we analyze if affective recommendations can be produced with a low cost approach using the open source electronics prototyping platform Arduino together with corresponding sensors and actuators. TORMES methodology (which combines user centered design methods and data mining techniques) can support the recommendations elicitation process by identifying new recommendation opportunities in these emerging social ubiquitous networking scenarios.
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    Should Conditional Self-Driving Cars Consider the State of the Human Inside the Vehicle?
    (ACM, 2021-06-22) Puertas Ramírez, David; Serrano Mamolar, Ana; Martín Gómez, David; González Boticario, Jesús
    Autonomous vehicles with conditional automation are said to be the next step in the development of self-driving cars. The human driver still performs a critical role in them, by taking over the control of the vehicle if prompted. As the technology is still facing pending challenges, the human drivers are also required to be able to detect and react in case of Autonomous Drive System (ADS) malfunctions. Within this context, in this work we argue that to assure safety during autonomous operation the user state should be measured all the time, which is intended to support a ”fallback ready state”. From an in-depth literature review, this article identifies the human factors involved in the aforementioned ”fallback ready state” that affect the personalization of human-vehicle interaction.
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    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, Jesús
    The 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.
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    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, Jesús
    Humans 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.
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    BIG-AFF: Exploring low cost and low intrusive infrastructures for affective computing in secondary schools
    (ACM, 2017-07-09) González Boticario, Jesús; Santos, Olga C.; Cabestrero Alonso, Raúl; Quirós Expósito, Pilar; Salmeron Majadas, Sergio; Uría Rivas, Raúl; Arevalillo Herráez, Miguel; Ferri, Francesc J.
    Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.
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    Towards multimodal affective detection in educational systems through mining emotional data sources
    (Springer Nature, 2015) Salmeron Majadas, Sergio; Santos, Olga C.; González Boticario, Jesús
    This paper introduces the work being carried out in an ongoing PhD research focused on the detection of the learners’ affective states by combining different available sources (from physiological sensors to keystroke analysis). Different data mining algorithms and data labeling techniques have been used generating 735 prediction models. Results so far show that predictive models on affective state detection from multimodal-based approaches provide better accuracy rates than single-based.
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    Supporting growers with recommendations in redvides: some human aspects involved
    (Springer Nature, 2014-10-10) Santos, Olga C.; Salmeron Majadas, Sergio; González Boticario, Jesús
    This 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.