Mario Muñoz Organero

University Carlos III de Madrid | UC3M · Department of Telematic Engineering

In Colombia, coffee futures contracts represent essential financial agreements that allow producers and buyers to establish prices, quality, and conditions for future transactions in the coffee market. Despite the evident benefits of stability and predictability, this practice faces significant sustainability challenges that threaten its long-term...

Educational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate content indicators based on student interaction. Thi...

Origin-Destination (OD) traffic flow estimations from traffic sensor data play an important role for transportation planning and management. This paper proposes a novel method to compare OD traffic estimated matrices (using data from traffic sensors). The proposed method uses the estimated OD traffic flow values together with COVID-19 incidence dat...

In the current era of Internet of Things, typically data from multiple sources are captured through various sensors yielding Multivariate Time Series (MTS) data. Sensor MTS prediction has several real-life applications in various domains such as healthcare, manufacturing, and agriculture. In this paper, we propose a novel Recurrent Neural Network (...

People with type 1 diabetes are required to adhere to their treatment rigorously to ensure maximum benefits. Diabetes tracking tools have played an important role in this regard. Type 1 diabetes monitoring has evolved and matured with the advent of blood glucose monitor sensors, insulin pens, and insulin pump automation. However, carbohydrate monit...

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...

As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current a...

The development and progress in sensor, communication and computing technologies have led to data rich environments. In such environments, data can easily be acquired not only from the monitored entities but also from the surroundings where the entity is operating. The additional data that are available from the problem domain, which cannot be used...

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to fore...

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...

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-...

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 ou...

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emot...

Selenium WebDriver is a framework used to control web browsers automatically. It provides a cross-browser Application Programming Interface (API) for different languages (e.g., Java, Python, or JavaScript) that allows automatic navigation, user impersonation, and verification of web applications. Internally, Selenium WebDriver makes use of the nati...

Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence me...

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, mach...

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate th...

This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone’s accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (R...

Nowadays, our mobile devices have become smart computing platforms, incorporating a wide number of embedded sensors such as accelerometers, gyroscopes, barometers, GPS receivers, and magnetometers. Smartphones are valuable devices for gathering user-related data and transforming it into value-added information for the user. In this study, a novel m...

Consumer devices continue to expand their capabilities by connecting to digital services and other devices to form information-sharing ecosystems. This is complex and requires meeting connection requirements and minimal processing capabilities to ensure communication. The emergence of new services, and the evolution of current technologies, constan...

Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the base for control algorithms in glucose regulating systems such as the artificial pancreas. Numerous...

Selenium is often considered the de-facto standard framework for end-to-end web testing nowadays. It allows practitioners to drive web browsers (such as Chrome, Firefox, Edge, or Opera) in an automated fashion using different language bindings (such as Java, Python, or JavaScript, among others). The term ecosystem, referring to the open-source soft...

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of children suffering it. Inattention, hyperactivity and impulsive behaviors, major symptoms characterizing ADHD, result not only in differences in the activity levels but also in the activity patterns themse...

Wearable sensors provide a user friendly and non-intrusive mechanism to extract user related data that paves the way to the development of personalized applications. Within those applications, Human Activity Recognition (HAR) plays an important role in the characterization of the user context. Outlier detection methods focus on finding anomalous da...

Human Activity Recognition (HAR) provides the context for many user-centered personal recommender systems in areas such as healthcare, sports, lifelong learning or home automation. Based on different types of sensors (either camera based, environmental sensors or wearable and mobile sensors) user related data provides the basis to extract movement...

Recommender systems have been based on context and content, now the technological challenge of making personalized recommendations based on the user emotional state arises, through physiological signals that are obtained from devices or sensors. This study applies the deep learning approach using a Deep Convolutional Neural Network (DCNN), on a dat...

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial acceleromet...

Wearable and mobile personal devices, from smart phones, bands, glasses, and watches to smart clothes and implants, are becoming increasingly ubiquitous [...]

On-board sensors in vehicles are able to capture real-time data representations of variables conditioning the traffic flow. Extracting knowledge by combining data from different vehicles, together with machine learning algorithms, will help both to optimise transportation systems and to maximise the drivers’ and passengers’ comfort. This paper prov...

Driver stress is a growing problem in the transportation industry. It causes a deterioration of cognitive skills, resulting in poor driving and an increase in the likelihood of traffic accidents. Prediction models allow us to avoid or at least minimize the negative consequences of stress. In this article, an algorithm based on deep learning is prop...

Educational computer-based competition environments need to be designed with a set of features that enhance the learning process. Although recently some frameworks for the design of educational computer-based systems (e.g., educational games) have been proposed, they do not focus on the details of the competition. Therefore, the design of education...

In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-de...

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 est...

The number of motorcycles on the road has increased in almost all European countries according to Eurostat. Although the total number of motorcycles is lower than the number of cars, the accident rate is much higher. A large number of these accidents are due to human errors. Stress is one of the main reasons behind human errors while driving. In th...

The automatic generation of street networks is attracting the attention of research and industry communities in areas such as routable map generation. This paper presents a novel mechanism that focuses on the automatic detection of street elements such as traffic lights, street crossings and roundabouts which could be used to generate street maps a...

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the tota...

Objectives: Recognising human activity is very useful for an investigator about a patient’s behaviour and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity, however research looking at the use of multiple body worn accelerometers in a free...

Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up. Using data analysis and machine learning techniques, common patterns and strategies from different users to execute different tasks can be extracted. In this paper, we present the evaluation results of the impact th...

By deploying pressure sensors on insoles, the forces exerted by the different parts of the foot when performing tasks standing up can be captured. The number and location of sensors to use is an important factor in order to enhance the accuracy of parameters used in assessment while minimizing the cost of the device by reducing the number of deploy...

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 infrastructure we have designed in the context of the HERMES project to collect the data from...

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorith...

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. T...

The levels of stress while driving affect the way we drive and have an impact on the likelihood of having an accident. Different types of sensors, such as heart rate or skin conductivity sensors, have been previously used to measure stress related features. Estimated stress levels could be used to adapt the driver's environment to minimize distract...

Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up such as walking. Using data analysis and machine learning techniques, common patterns and strategies from different users to achieve different tasks can be automatically extracted. In this paper, we present the resul...

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data....

In this paper we present the studies and research conducted within the eMadrid project, funded by the Regional Government of Comunidad Autonoma de Madrid. In particular, we focus on those works dealing with two research lines: ubiquitous and mobile learning; adaptation, adaptability and accessibility.

The use of pervasive computing technologies for advertising purposes is an interesting emergent field for large, medium, and small companies. Although recommender systems have been a traditional solution to decrease users’ cognitive effort to find good and personalized items, the classic collaborative filtering needs to include contextual informati...

Face-to-face teaching in higher education needs some evolution in order to accommodate the new students generation. The old-fashioned master class way of instruction has led to worrying levels of demotivation not only in learners but also in teachers. The use of active learning techniques allow students to participate in their active cognitive proc...

Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detect...

Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator) from different radio wave transmitters, such as Wi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms), fingerprinting-based syst...

Stress is one of the most important factors in car accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to predict stress level on a road. Prediction model is based on deep learning. The stress level estimation considers the previous driver’s driving behavior before reach...

Student response systems (SRSs) are becoming popular among instructors in nearly all levels of learning. The benefits of using SRSs in terms of attendance, attention, participation, or motivation have been shown in many studies. Moreover, several studies demonstrate that the use of some kind of gaming techniques in education are useful to stimulate...

In this paper, we propose, implement and user-validate a training tool for saving fuel that uses some elements from games in order to promote efficient driving and provide feedback to the user. The proposed system uses a fuzzy logic system in order to assess the driving style from the point of view of the fuel consumption. The output is a score bet...

In this paper, we propose a solution to reduce the stress level of the driver, minimize fuel consumption and improve safety. The system analyzes the driving and driver workload during the trip. If it discovers an area where the stress increases and the driving style is worse from the point of view of energy efficiency, a photo is taken and is saved...

The contribution of this paper is twofold. On the one hand, we present an experience of a Masters’ level course based on real-world projects with interaction with real developers from industry. This reality check created a special motivation for students as well as provided a useful experience for their future work. On the other hand, we describe t...

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic sig...

En este trabajo se propone un algoritmo para obtener la velocidad media óptima para ahorrar combustible y mejorar la seguridad. El algoritmo propuesto se basa en los algoritmos genéticos. El algoritmo emplea información sobre el entorno, la carretera y el vehículo para obtener la velocidad media que minimice el consumo de combustible sin incrementa...

In this paper, we propose a solution to reduce the stress level of the driver, minimize fuel consumption and improve safety. The system analyzes the driving style and the driver’s workload during the trip while driving. If it discovers an area where the stress increases and the driving style is not appropriate from the point of view of energy effic...

In this paper, we propose a learning method for eco-driving based on imitation. The system uses Data Envelopment Analysis (DEA) in order to calculate the driving efficiency from the point of view of the fuel consumption. The input and output parameters have been selected taking into account the Longitudinal Vehicle Dynamics Model. This technique al...

A lack of student motivation is a problem in many courses in electrical engineering. Introducing competition between students can enhance their motivation, but it can also generate negative emotions. This paper presents an empirical study of students in a telecommunications engineering degree; it measured their level of motivation, and their emotio...

This paper presents an in-depth empirical analysis of a nine-week MOOC. This analysis provides novel results regarding participants’ profiles and use of built-in and external social tools. The results served to detect seven participants’ patterns and conclude that the forum was the social tool preferred to contribute to the MOOC.

In this paper, we propose a mechanism to optimize fuel consumption on regular routes. The idea is to find out in which areas a driver usually realizes inefficient actions from the point of view of energy consumption. The aim is to alert the user in advance in order to adjust the vehicle speed or change gear, avoiding inefficient driving. Unlike oth...

Ads recommendations delivery on digital signage environments must consider group and individual profiles. Although the ads recommendation precision is relevant and frequently explored, a good serendipity/precision balance must be a concern on Digital Signage context. This paper introduces a public display recommender system approach based on an ind...