Challenges in using sensors to track users health and wellbeing on a daily basis

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

Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs

Wearable sensors provide a user-friendly and non-intrusive mechanism to extract user-relateddata that paves the way to the development of personalized applications. Within those applications, humanactivity recognition (HAR) plays an important role in the characterization of the user context. Outlierdetection methods focus on finding anomalous data samples that are likely to have been generated by adifferent mechanism. This paper combines outlier detection and HAR by introducing a novel algorithmthat is able both to detect information from secondary activities inside the main activity and to extract datasegments of a particular sub-activity from a different activity. Several machine learning algorithms havebeen previously used in the area of HAR based on the analysis of the time sequences generated by wearablesensors. Deep recurrent neural networks (DRNNs) have proven to be optimally adapted to the sequentialcharacteristics of wearable sensor data in previous studies. A DRNN-based algorithm is proposed in thispaper for outlier detection in HAR. The results are validated both for intra- and inter-subject cases and bothfor outlier detection and sub-activity recognition using two different datasets. A first dataset comprising4 major activities (walking, running, climbing up, and down) from 15 users is used to train and validatethe proposal. Intra-subject outlier detection is able to detect all major outliers in the walking activity in thisdataset, while inter-subject outlier detection only fails for one participant executing the activity in a peculiarway. Sub-activity detection has been validated by finding out and extracting walking segments present inthe other three activities in this dataset. A second dataset using four different users, a different setting anddifferent sensor devices is used to assess the generalization of results.This work was supported by the ‘‘ANALYTICS USING SENSOR DATA FOR FLATCITY’’ Project (MINECO/ ERDF, EU) funded in partby the Spanish Agencia Estatal de Investigación (AEI) under Grant TIN2016-77158-C4-1-R and in part by the European RegionalDevelopment Fund (ERDF)

Editorial for the Special Issue "Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies"

Wearable and mobile personal devices, from smart phones, bands, glasses, and watches to smart clothes and implants, are becoming increasingly ubiquitous. These wearable sensing technologies can provide 24/7 physiological and movement data that enhance the knowledge base of users or groups of users. They constitute the internal fabric of an Internet of Smart Things, which provides the basis for better understanding the user—what the user does, when, how, and even why. Both physical and mental health-related information can be extracted or inferred from the diverse nature of the data. Sensor miniaturization and affordable prices are bridging the gap between theoretical health and wellbeing scenarios based on wearable technology and their feasible deployment on real settings. Personal health monitoring applications based on wearable sensors will empower the role of the user in its health self-management and will decrease the pressure of care-related resources for public health systems. Several medical conditions, from temporary illnesses to long-term chronic conditions, can benefit from the deployment of wearable sensors that monitor the user physiological parameters and physical activities on a continuous basis and provide automated feedback in real time to help each user in a personal way

Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting

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 forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios

Analysis of the latest trends in mobile commerce using the NFC technology

The aim of this research is to propose new mobile commerce proximity payment architecture, based on the analysis of existing solutions and current and future market needs. The idea is to change a Mobile Device into a reliable and secure payment tool, available to everyone and with possibility to securely and easily perform purchases and proximity paymentsThe research leading to these results has received funding by the ARTEMISA project TIN2009-14378-C02-02 within the Spanish "Plan Nacional de I+D+I", and the Madrid regional community projects S2009/TIC-1650 and CCG10-UC3M/TIC-4992

Artemisa: early design of an eco-driving assistant

Actas del XIII Jornadas de ARCA: Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental, Huelva 26 – 29 de Junio de 2011Eco-driving is becoming a very important topic in recent years since aspects such as environmental pollution, energy conservation, global warming and user safety depend on it. To save fuel, it requires a combination of vehicle design principles (including aerodynamics, engine optimization, fuel type and vehicle weight) and that the driver adopt an efficient driving style. This paper presents an eco-driving assistant that evaluates the driver's driving style from the standpoint of fuel consumption. Then, based on the assessment provides advice to adopt eco-driving habits. Eco-driving assistant will facilitate that drivers learn the techniques of efficient driving. We solution runs on mobile devices with Android OS requiring minimal HW inside the vehicle. Furthermore, analyze better driver's driving style than other solutions because it takes into account environmental variables that influence in the fuel consumption.The research leading to these results has received funding by the ARTEMISA project TIN2009-14378-C02-02 within the Spanish "Plan Nacional de I+D+I", and the Madrid regional community projects S2009/TIC-1650 and CCG10- UC3M/TIC-4992

Eco-driving: energy saving based on driver behavior

Ponencia presentada en: XVI Jornadas de ARCA sobre Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental (JARCA 2014), celebrado los días del 24/06/2014 a 27/06/2014, en Rota, Cádiz (España)The number of vehicles has grown in recent years. As a result, it has increased the fuel consumption and the emission of gaseous pollutants. The emission of gaseous pollutants causes more deaths than traffic accidents. On the other hand, the energy resources are limited and the increase in demand causes them even more expensive. In addition, the percentage of old vehicles is very high. Eco-driving is a good solution in order to minimize the fuel consumption because it is independent of the vehicle age. In this paper, a driving assistant is presented. This solution allows the user acquires knowledge about eco-driving. Unlike other solutions, our proposal adapts the recommendations to the user profile. It also provides information in advance such as: optimal average speed, anomalous events, deceleration pattern, and so on. These recommendations prevent that the user performs inefficient actions. In these type of systems, motivation is very important. Drivers lose the interest over time. To solve this problem, we employ gamification techniques that contribute to avoid drivers coming back to their previous driving habitsThe 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" under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036- 370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program.Publicad

Predicting upcoming values of stress while driving

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 distractions in high cognitive demanding situations and to promote stress-friendly driving behaviors. The way we drive has an impact on how stressors affect the perceived cognitive demands by drivers, and at the same time, the perceived stress has an impact on the actions taken by the driver. In this paper, we evaluate how effectively upcoming stress levels can be predicted considering current stress levels, current driving behavior, and the shape of the road. We use features, such as the positive kinetic energy and severity of curves on the road to estimate how stress levels will evolve in the next minute. Different machine learning techniques are evaluated and the results for both intra and inter-city driving and for both intra and inter driver data are presented. We have used data from four different drivers with three different car models and a motorbike and more than 220 test drives. Results show that upcoming stress levels can be accurately predicted for a single user ( correlation r = 0.99 and classification accuracy 97.5%) but prediction for different users is more limited ( correlation r = 0.92 and classification accuracy 46.9%).This work was supported in part by HERMES-SMART DRIVER Project through Spanish MINECO under Project TIN2013-46801-C4-2-R, in part by the Ministerio de Educación Cultura y Deporte under Grant PRX15/0003

La educación digital y el futuro de las formas de enseñar y aprender

This article presents some brushstrokes of a couple of technological elements that are producing great transformations in the educational environment and that, undoubtedly, will continue to do so in the near future as an approach to the subject of this digital education of the future: the MOOCs and the use of mobile devices.MOOCs or massive online and open courses (Massive Open On-line Courses in English) open the knowledge of traditional closed courses that have been used for the transmission of knowledge.For its part, mobile devices, increasingly present in all the scenarios of our lives and how could it be otherwise in education, add new components to the educational process at the hand of their availability to learn and communicate at any time and in any place.Este artículo presenta algunas pinceladas de un par de elementos tecnológicos que están produciendo grandes transformaciones en el entorno educativo y que, sin duda, lo seguirán haciendo en el futuro próximo como acercamiento al tema de esta educación digital del futuro: los MOOCs y el uso de dispositivos móviles. Los MOOCs o cursos masivos en línea y abiertos (Massive Open On-line Courses en inglés) abren el conocimiento de los tradicionales cursos cerrados que se han venido usando para la transmisión de conocimiento. Por su parte, los dispositivos móviles, cada vez más presentes en todos los escenarios de nuestras vidas y como no podía ser de otra manera también en educación, añaden componentes nuevas al proceso educativo de la mano de su disponibilidad para aprender y comunicarse en cualquier momento y en cualquier lugar

Artemisa: An eco-driving assistant for android Os

Proceedings of the 1st IEEE International Conference on Consumer Electronics - Berlin (IEEE ICCE-Berlin 2011), September 6 - 8, 2011, Berlin, GermanyThis paper proposes an eco-driving assistant that facilitates the user to learn the techniques of efficient driving. The Artemisa´s assistant evaluates the driver´s driving style taking into account some environmental as well as some vehicles´s variables such as speed, gear, R.P.M, etc. Besides, tips are inferred to teach efficient driving habits. Compared to other similar systems, the Artemisa’s assistant runs on a mobile device with Android O.S. and it does not need to install additional hardwareProyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madri