Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to... more
Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to continuously monitor traffic-related human mobility, showing the impact of COVID-19 on traffic volumes and patterns. In a similar way, traffic volumes measured by smart traffic sensors provide a proxy variable to capture human mobility, which is expected to have an impact on new COVID-19 infections. Adding traffic data from smart city sensors to machine learning models designed to estimate upcoming COVID-19 incidence values should provide optimized results compared to models based on COVID-19 data alone. This paper proposes a novel model to extract spatio-temporal patterns in the spread of the COVID-19 virus for short-term predictions by organizing COVID-19 incidence and traffic data as interrelated temporal sequences of spatial images. The model is trained a...
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Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood... more
Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in range (considering the calories taken and the physical exercise carried out). In recent years, machine learning models have been developed in order to help type 1 diabetes patients with their blood glucose control. These models tend to receive the insulin units used and the carbohydrate taken as inputs and generate optimal estimations for future blood glucose levels over a prediction horizon. The body glucose kinetics is a complex user-dependent process, and learning patient-specific blood glucose patterns from insulin units and carbohydrate content is a difficult task even for deep learning-based models. This paper proposes a novel mechanism to increase the accuracy of blood glucose predictions from deep learning models based on the es...
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In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely... more
In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. This article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. The algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. Adjustments are made to the data in terms of Pearson iterations, synchronization, filtering, and normalization, and DTW, linear regression, and error analysis are used to make the corresponding comparison of the two datasets. With the...
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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 encourage users to use it is to improve... more
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 encourage 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 predict 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 predicts 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 workload and the passenger stress.
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In this paper we present a dataset componsed of domain-specific sentiment lexicons in six languages for two domains. We used existing collections of reviews from Trip Advisor, Amazon, the Stanford Network Analysis Project and the OpinRank... more
In this paper we present a dataset componsed of domain-specific sentiment lexicons in six languages for two domains. We used existing collections of reviews from Trip Advisor, Amazon, the Stanford Network Analysis Project and the OpinRank Review Dataset. We use an RDF model based on the lemon and Marl formats to represent the lexicons. We describe the methodology that we applied to generate the domain-specific lexicons and we provide access information to our datasets.
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Stress is one of the most important factors in traffic accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to estimate the optimal speed to minimize stress... more
Stress is one of the most important factors in traffic accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to estimate the optimal speed to minimize stress levels on upcoming road segments when driving. The prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section to be assessed, the road state (weather and traffic), and the previous drives made by the driver. We use this algorithm to build a speed assistant. The solution provides an optimum average speed for each road segment that minimizes the stress. A validation experiment has been conducted in a real setting using two different types of vehicles. The proposal is able to predict the stress levels given the average speed by 84.20% on average. On the other hand, the speed assistant reduces the stress levels (estimated from the driver’s heart rate signal) ...
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Audio Response Systems (ARS) are currently used as a mechanism to enhance face to face education in classrooms and the results are promising. These systems could also improve certain aspects in e-learning if designed appropriately. This... more
Audio Response Systems (ARS) are currently used as a mechanism to enhance face to face education in classrooms and the results are promising. These systems could also improve certain aspects in e-learning if designed appropriately. This paper presents a design and implementation of an ARS adapted to both face to face education and distance learning scenarios. We also show how
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Parasitological diagnosis, using staned smears, culture and pathological examination of biopsy, was studied in 146 patients infected with mucocutaneous leishmaniasis, in Bolivia and Peru. The most efficient parasite detecting technique... more
Parasitological diagnosis, using staned smears, culture and pathological examination of biopsy, was studied in 146 patients infected with mucocutaneous leishmaniasis, in Bolivia and Peru. The most efficient parasite detecting technique appeared to be the smear examination in cutaneous lesions (33 % positive) and the pathology in case of mucous lesions (28 % positive). In both, cutaneous and mucous lesions, the parasites were found most frequently in old lesions.
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CTX-M-15-producing Escherichia coli has emerged worldwide as an important pathogen associated with community-onset infections, but in South America reports are scarce. We document the presence of CTX-M-15-producing E. coli of the... more
CTX-M-15-producing Escherichia coli has emerged worldwide as an important pathogen associated with community-onset infections, but in South America reports are scarce. We document the presence of CTX-M-15-producing E. coli of the international ST131 and ST405 clones in Colombia and present the first molecular characterization of these isolates in South America.
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Research Interests: Genetics, Biology, Bolivia, Leishmania, Medicine, and 14 moreBiological Sciences, Leishmaniasis, Disease Outbreaks, Humans, Animals, Karyotyping, Skin, Ulcer, Isoenzymes, Pulsed field gel electrophoresis (PFGE), Cutaneous Leishmaniasis, Gel electrophoresis, Cricetinae, and Medical and Health Sciences
Selenium WebDriver is a library that allows controlling web browsers (e.g., Chrome, Firefox, etc.) programmatically. It provides a cross-browser programming interface in several languages used primarily to implement end-to-end tests for... more
Selenium WebDriver is a library that allows controlling web browsers (e.g., Chrome, Firefox, etc.) programmatically. It provides a cross-browser programming interface in several languages used primarily to implement end-to-end tests for web applications. JUnit is a popular unit testing framework for Java. Its latest version (i.e., JUnit 5) provides a programming and extension model called Jupiter. This paper presents Selenium-Jupiter, an open-source JUnit 5 extension for Selenium WebDriver. Selenium-Jupiter aims to ease the development of Selenium WebDriver tests thanks to an automated driver management process implemented in conjunction with the Jupiter parameter resolution mechanism. Moreover, Selenium-Jupiter provides seamless integration with Docker, allowing the use of different web browsers in Docker containers out of the box. This feature enables cross-browser testing, load testing, and troubleshooting (e.g., configurable session recordings). This paper presents an example case in which Selenium-Jupiter is used to evaluate the performance of video conferencing systems based on WebRTC. This example case shows that Selenium-Jupiter can build and maintain the required infrastructure for complex tests effortlessly.
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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... more
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 2015The stress, safety and fuel consumption are variables that are strongly related. If the stress is high, the driver is more likely to make mistakes and have ac- cidents. In addition, he or she will make decisions at short notice. The acceleration and deceleration increases, minimizing the use of energy generated by the engine. However, the stress can be reduced if we provide information about the environment in ad- vance. In this paper, we propose a driving assistant which issues tips to the driver in order to improve the stress level. These tips are based on speed. The solution estimates the optimal average speed for each road section. In additi...
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COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that... more
COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components. This manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The res...
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Los dispositivos móviles han adquirido en los últimos años una gran importancia en la sociedad de la comunicación y la información actual. Esta tendencia, se ha disparado con la aparición de los dispositivos smartphones. La introducción a... more
Los dispositivos móviles han adquirido en los últimos años una gran importancia en la sociedad de la comunicación y la información actual. Esta tendencia, se ha disparado con la aparición de los dispositivos smartphones. La introducción a estos dispositivos de capacidades NFC, dota de un gran potencial a esta tecnología. NFC una tecnología inalámbrica de corto alcance que permite comunicarse y obtener información entre dos dispositivos de forma muy fácil, con el simple gesto de acercar el uno con el otro. A partir de esta característica, ya se le han encontrado multitud de usos para la sociedad, como, por ejemplo, recoger información de posters, tarjetas de fidelización de restaurantes, o realizar pagos. En este proyecto se ha diseñado y desarrollado en Java, una simulación de un proceso de pago a través de la tecnología NFC integrada en un teléfono móvil. Al tratarse de una simulación de todo el proceso, se han implementado las distintas partes que intervienen en la compra y pago d...
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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... more
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 th...
Research Interests: Computer Science, Stroke rehabilitation, Modelling, Medicine, Physical Medicine and Rehabilitation, and 12 moreLibrary and Information Studies, Gait, Stroke, Telecomunicaciones, Humans, Walking, Medicina, Public health systems and services research, Knee Joint, Biomechanical Phenomena, Pattern analysis, and Mahalanobis distance
El crecimiento del numero de vehiculos en circulacion ha experimentado un fuerte aumento en los ultimos 20 anos. La generalizacion del uso del automovil ha tenido efectos muy positivos en la economia de los paises. Sin embargo, tambien ha... more
El crecimiento del numero de vehiculos en circulacion ha experimentado un fuerte aumento en los ultimos 20 anos. La generalizacion del uso del automovil ha tenido efectos muy positivos en la economia de los paises. Sin embargo, tambien ha provocado grandes problemas debido la contaminacion y a la cantidad de energia que consumen. Por otra parte, la mayoria de los vehiculos emplean hidrocarburos, que no se encuentran disponibles en todas las regiones, provocando dependencias energeticas entre paises. Ademas, su extraccion tiene un impacto muy grande en el medioambiente. Los vehiculos se han convertido en un problema importante para los gobiernos y los habitantes, que sufren enfermedades respiratorias provocadas por los gases que emiten. Ante estos inconvenientes, los gobiernos han desarrollado normativas para regular las emisiones de los vehiculos. Las conductores tambien han empezado a exigir vehiculos que consuman menos debido al aumento del precio del combustible, convirtiendose e...
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espanolEn este articulo se pasa revista a las lineas de investigacion en torno a la tecnologia educativa que se realiza en el grupo de investigacion GAST-UC3M del Departamento de Ingenieria Telematica de la Universidad Carlos III de... more
espanolEn este articulo se pasa revista a las lineas de investigacion en torno a la tecnologia educativa que se realiza en el grupo de investigacion GAST-UC3M del Departamento de Ingenieria Telematica de la Universidad Carlos III de Madrid, en concreto en el laboratorio GRADIENT, que es uno de los tres laboratorios de este grupo de investigacion. La descripcion se organiza en tres temas: (1) MOOCs, SPOCs y blended learning, (2) analitica del aprendizaje y (3) realidades mixtas (realidad aumentada y realidad virtual). EnglishIn this paper, we review the research lines on educational technology performed by the GASTUC3M research group of the Telematic Engineering Dept. of Universidad Carlos III de Madrid, and in particular, of the GRADIENT lab, which is one of the three labs in this research group. The description is organized in three topics: (1) MOOCs, SPOCs, and blended learning, (2) learning analytics, and (3) mixed realities (augmented reality and virtual reality).
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ABSTRACT
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ABSTRACT