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Every day, people need to be trained, either to complement their professional skills or because of personal interests that lead to their well-being. Online education, especially MOOC and SPOC are good alternatives for carrying out continuing educational processes, but are not enough when it comes to learning motor skills. This paper shows a research approach on how MOOC can expand its functionalities so that it can be applied to learning motor skills. It also proposes the development of a project to integrate three components: the integration component, the wearable component and the Open edX component for the future development of a “salsa” course. Finally, it proposes an architecture based on the 4+1 view model to show the functionality in different areas of the design. Resumen Artículo Derivado de Conferencia. Cada día, las personas necesitan ser capacitadas, ya sea para complementar sus habilidades profesionales o debido a intereses personales que conducen a su bienestar. La educación en línea, especialmente el MOOC y el SPOC, son buenas alternativas para llevar a cabo procesos educativos continuos pero no son suficientes cuando se trata de aprender habilidades motoras. Este artículo muestra un enfoque de investigación sobre cómo MOOC puede ampliar sus funcionalidades para que pueda aplicarse al aprendizaje de las habilidades motoras. También propone el desarrollo de un proyecto para integrar tres componentes: el componente de integración, el componente vestible y el componente abierto de edX para el desarrollo futuro de un curso de “salsa”. Finalmente, propone una arquitectura basada en el modelo de vista 4+1 donde se muestra la funcionalidad en diferentes áreas del diseño.

Memorias en formato póster de los proyectos que han obtenido reconocimiento en la 19ª Convocatoria de Apoyo a Experiencias de Innovación Docente. Curso 2021-2022. Presentadas en la Jornada de Innovación docente 2022 celebrada en Getafe el 23 de junio de 2022.

Despite the many technological advances in sensor devices, there are still many challenges that hinder their end to end deployment and use in health and wellbeing monitoring and selfmanagement systems. This talk provides an overview of the different pieces in such a system and identifies some of the major challenges that have to be addressed before their mass adoption by the national health services.

En este artículo se pasa revista a las líneas de investigación en torno a la tecnología educativa que se realiza en el grupo de investigación GAST-UC3M del Departamento de Ingeniería Telemática de la Universidad Carlos III de Madrid, en concreto en el laboratorio GRADIENT, que es uno de los tres laboratorios de este grupo de investigación. La descripción se organiza en tres temas: (1) MOOCs, SPOCs y blended learning, (2) analítica del aprendizaje y (3) realidades mixtas (realidad aumentada y realidad virtual).

Proceedings of: 14th conference of the Association for the Advancement of Assistive Technology in Europe (AAATE 2017), Sheffield (UK), 12-15th September 2017. Insole pressure sensors capture the force distribution patterns during the stance phase while walking. By comparing patterns obtained from healthy individuals to patients suffering different medical conditions based on a given similarity measure, automatic impairment indexes can be computed in order to help in applications such as rehabilitation. This paper uses the data sensed from insole pressure sensors for a group of healthy controls to train an auto-encoder using patterns of stochastic distances in series of consecutive steps while walking at normal speeds. Two experiment groups are compared to the healthy control group: a group of patients suffering knee pain and a group of post-stroke survivors. The Mahalanobis distance is computed for every single step by each participant compared to the entire dataset sensed from healthy controls. The computed distances for consecutive steps are fed into the previously trained autoencoder and the average error is used to assess how close the walking segment is to the autogenerated model from healthy controls. The results show that automatic distortion indexes can be used to assess each participant as compared to normal patterns computed from healthy controls. The stochastic distances observed for the group of stroke survivors are bigger than those for the people with knee pain. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).

Proceedings of JARCA 2016: XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence: El Toyo, Almería (Spain), 23-29 June, 2016 Many of the services a smart city can provide to its citizens rely on the ability of its infrastructure to collect and process in real time vast amounts of continuous data that sensors deployed through the city produce. In this paper we present the server in- frastructure we have designed in the context of the HERMES project to collect the data from sensors and aggregate it in streams for their use in services of the smart city. Research reported in this paper was supported by the Spanish Economy Ministry through the “HERMES–Smart Driver” project (TIN2013-46801-C4-2-R) and the “HERMES–Smart Citizen” project (TIN2013-46801-C4-1-R)

Proceedings of Med-e-Tel 2016: The International eHealth, Telemedicine and Health ICT Forum for Educational, Networking and Business: April 6-8, 2016, Luxembourg Sudden or gradual loss of ability to drive a car - such as with sight loss and/or cognitive decline - can have significant impact on people's wellbeing and mental health. In the INLIFE project there is a desire to provide an integrated suite of assistive technologies to particularly support people with dementia or mild cognitive impairment. This paper presents a possible wearable technology to facilitate driving and monitor decline in driving capacity. The way we perform common daily activities is influenced by our cognitive abilities. Driving is one example of significant importance since the way we drive may have a potential impact in our safety. This paper proposes a framework to monitor and track the driving efficiency of a particular user in terms of energy consumption and perceived stress as a way to assess the user's cognitive abilities. A smartphone based personal recommender system is proposed to help the driver to drive better in high cognitive demanding situations as measured by the perceived stress by providing in advance information to help the driver better manage those situations. The perceived stress is estimated from the heart rate variability (HRV) signal from a wearable device. In the long term, the evolution in detected patterns in personal time series of perceived stress when driving in similar conditions could be used as an indicator of the severity and progress in mild cognitive impairment or early stages of dementia for particular patients. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" funded by the Spanish Ministerio de Economía y Competitividad and from the grant PRX15/00036 for the “Estancias de movilidad de profesores e investigadores seniores en centros extranjeros de enseñanza superior e investigación” from the Ministerio de Educación Cultura y Deporte.

Proceedings of JARCA 2016: XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence: El Toyo, Almería (Spain), 23-29 June, 2016 The number of vehicles in circulation has become a problem both for safety and for the citizens health. Public transport is a solution to reduce its impact on the environment. One of the keys to encourag e users to use it is to improve comfort. On the other hand, numerous studies highlight that drivers are more likely to suffer physical and psychological illnesses due to the sedentary nature of this work and workload . In this paper, we propose a model to p redict the stress level on drivers and passengers. The solution is based on deep learning algorithms. The proposal employs the Heart Rate Variability (HRV) and telemetry from the vehicle in order to anticipate the incoming stress . It has been validated in a real environment on distinct routes. The results show that it predict s the stress by 86 % on drivers and 92% on passengers. This algorithm could be used to develop driving assistants that recommend actions to smooth driving, reducing the work load and the passenger stress. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte.

JARCA 2015: Actas de las XVII Jornadas de ARCA: Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica, Inteligencia Ambiental y Ciudades Inteligentes = Proceedings of the XVII ARCA Days: Qualitative Systems and its Applications in Diagnose Robotics, Ambient Intelligence and Smart Cities, Vinaros (Valencia), 23 al 27 de Junio de 2015. En este trabajo se describe la infraestructura propuesta en el proyecto Nacional Hermes en el que se plantea un modelo de transición para llegar a crear ciudades inteligentes sin necesidad de grandes inversiones en infraestructuras. Este trabajo ha sido parcialmente financiado por el proyecto del Ministerio de Economía y Competitividad HERMES (TIN2013-46801-C4-1-r) y los proyectos de excelencia de la Junta de Andalucía Simon (P11-TIC-8052) y Context-Learning (P11-TIC-7124)

JARCA 2015: Actas de las XVII Jornadas de ARCA: Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica, Inteligencia Ambiental y Ciudades Inteligentes = Proceedings of the XVII ARCA Days: Qualitative Systems and its Applications in Diagnose Robotics, Ambient Intelligence and Smart Cities, Vinaros (Valencia), 23 al 27 de Junio de 2015 El aumento de la población en áreas urbanas y el ritmo de vida cada vez más sedentario es una preocupación creciente. Por otra parte, los avances tecnológicos en sensores y redes de comunicaciones permiten obtener mucha información, que antes no era posible conocer, prácticamente en tiempo real. Este trabajo en progreso utiliza estos avances, para recopilar datos de los habitantes de una zona urbana en una plataforma web en que en un futuro, los profesionales puedan obtener datos anónimos, analizarlos y suministrar patrones de salud en base a los mismos, dotando al sistema de la capacidad de crear planes de acción comunes y personalizados a los perfiles de los ciudadanos, con el fin de mejorar su calidad de vida. Este trabajo ha sido parcialmente financiado por el proyecto del Ministerio de Economía y Competitividad HERMES (TIN2013-46801-C4-1-r) y los proyectos de excelencia de la Junta de Andalucía Simon (P11-TIC-8052) y Context-Learning (P11-TIC-7124).

El aumento de la población en áreas urbanas y el ritmo de vida cada vez más sedentario es una preocupación creciente. Por otra parte, los avances tecnológicos en sensores y redes de comunicaciones permiten obtener mucha información, que antes no era posible conocer, prácticamente en tiempo real. Este trabajo en progreso utiliza estos avances, para recopilar datos de los habitantes de una zona urbana en una plataforma web en que en un futuro, los profesionales puedan obtener datos anónimos, analizarlos y suministrar patrones de salud en base a los mismos, dotando al sistema de la capacidad de crear planes de acción comunes y personalizados a los perfiles de los ciudadanos, con el fin de mejorar su calidad de vida. MInisterio de Economía y Competitividad TIN2013-46801-C4-1-r Junta de Andalucía P11-TIC-8052