SMS-Builder: An adaptive software tool for building systematic mapping studies

A Systematic Mapping Study is an instrument frequently used to carry out a search process, identification, and classification of studies in different fields. Researchers in front of this type of process have a challenge while managing the data about these studies. This paper presents a software tool that has been created to help those who need to build a systematic mapping study. In addition, this work follows the evidence-based software engineering approach and extends it through a software tool by including different ways of adapting this process

Clustering Study of Vehicle Behaviors Using License Plate Recognition

Ubiquitous computing and artificial intelligence contribute to deploying intelligent environments. Sensor networks in cities generate large amounts of data that can be analyzed to provide relevant information in different fields, such as traffic control. We propose an analysis of vehicular behavior based on license plate recognition (LPR) in a rural region of three small villages. The contribution is twofold. First, we extend an existing taxonomy of the most widely used clustering algorithms in machine learning with additional classes. Second, we compare the performance of algorithms from each class of the taxonomy, extracting behavioral patterns. Partitional and hierarchical algorithms obtain the best results, while density-based algorithms have poor results. The results show four differentiated patterns in vehicular behavior, distinguishing different patterns in both residents and tourists. Our work can help policymakers develop strategies to improve services in rural villages, and developers choose the correct algorithm for a similar study.LifeWatch ERI

A psycho‑educational intervention programme for parents with SGA foetuses supported by an adaptive mHealth system: design, proof of concept and usability assessment

Background: Technology-based approaches during pregnancy can facilitate the self-reporting of emotional health issues and improve well-being. There is evidence to suggest that stress during pregnancy can affect the foetus and result in restricted growth and preterm birth. Although a number of mobile health (mHealth) approaches are designed to monitor pregnancy and provide information about a specific aspect, no proposal specifically addresses the interventions in parents at risk of having small-for-gestational-age (SGA) or premature babies. Very few studies, however, follow any design and usability guidelines which aim to ensure end-user satisfaction when using these systems. Results: We have developed an interactive, adaptable mHealth system to support a psycho-educational intervention programme for parents with SGA foetuses. The relevant results include a metamodel to support the task of modelling current or new intervention programmes, an mHealth system model with runtime adaptation to changes in the programme, the design of a usable app (called VivEmbarazo) and an architectural design and prototype implementation. The developed mHealth system has also enabled us to conduct a proof of concept based on the use of the mHealth systems and this includes data analysis and assesses usability and acceptance. Conclusions: The proof of concept confirms that parents are satisfied and that they are enthusiastic about the mHealth-supported intervention programme. It helps to technically validate the results obtained in the other stages relating to the development of the solution. The data analysis resulting from the proof of concept confirms that the stress experienced by parents who followed the mHealth-supported intervention programme was significantly lower than among those who did not follow it. This implies an improvement in the emotional health not only of the parents but also of their child. In fact, the babies of couples who followed the mHealth-supported programme weigh more than the babies of couples under traditional care. In terms of user acceptance and usability, the analysis confirms that mothers place greater value on the app design, usefulness and ease of use and are generally more satisfied than their partners. Although these results are promising in comparison with more traditional and other more recent technology- based approaches.MCIN/AEI PID2019-109644RB-I00Andalusia Ministry of Health (Junta de Andalucia-Spain) PC-0526-2016052

Spatio-Temporal Dynamics of Vehicles: Fusion of Traffic Data and Context Information

We present a dataset for vehicle tracking in a rural area. Specifically, in the Barranco de Poqueira region, which includes the municipalities of Pampaneira, Bubión, and Capileira in the Sierra Nevada National Park, Granada, Spain. Four Hikvision License Plate Recognition (LPR) cameras collect vehicle entries and exits to each village. Additional contextual data, including vacation calendars, vehicle origins, and socio-demographic information, enrich the dataset. The dataset comprises three files covering nine months from February to October 2022: one with raw data directly extracted from the cameras, another aggregated at the visit level and including context information, and a third aggregated by vehicles with context information. These datasets can be useful for mobility studies, urban planning, tourism, and socio-demographic analysis.LifeWatch ERI

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.Ministry of Economy and Competitiveness from Spain MINECO/FEDER MAT2017-85999PEuropean Union (EU) MINECO/FEDER MAT2017-85999PRegional Government of Andalusia Research Fund from Spain A-BIO-157-UGR-1

Technical note: A mobile collaborative workspace to assist forensic experts in disaster victim identification scenarios

Integrated approaches to disaster victim identification (DVI) management have led to a need for technologies to improve interaction among parties involved in post-mortem (PM) and ante-mortem (AM) data collection through better communication and coordination. Mobile Forensic Workspace© (MFW) is a collaborative mobile system that not only facilitates the systematic collection of high-quality data, but also allows DVI professionals to coordinate activities and exchange data through secure real-time communication at major disaster scenarios in accordance with security, privacy and legal protocols. MFW is adaptable to any communication format (text, voice calls, photographs, etc.) and is dynamically self-reconfigurable when connectivity problems arise. It also allows data integration and backup through secure communication channels between local and remote servers. The feasibility of the system has been demonstrated through implementation of MFW on the iOS platform for iPhone, iPod Touch and iPad terminals. A further strength of MFW is that it provides out-of-the-box support for INTERPOL DVI forms. The application of information and communication technologies for DVI was shown to be useful in improving DVI management by enhancing the quality of data collection and enabling non-Internet dependent real-time data sharing and communication

A Communication Model to Integrate the Request-Response and the Publish-Subscribe Paradigms into Ubiquitous Systems

The Request-Response (RR) paradigm is widely used in ubiquitous systems to exchange information in a secure, reliable and timely manner. Nonetheless, there is also an emerging need for adopting the Publish-Subscribe (PubSub) paradigm in this kind of systems, due to the advantages that this paradigm offers in supporting mobility by means of asynchronous, non-blocking and one-to-many message distribution semantics for event notification. This paper analyzes the strengths and weaknesses of both the RR and PubSub paradigms to support communications in ubiquitous systems and proposes an abstract communication model in order to enable their seamless integration. Thus, developers will be focused on communication semantics and the required quality properties, rather than be concerned about specific communication mechanisms. The aim is to provide developers with abstractions intended to decrease the complexity of integrating different communication paradigms commonly needed in ubiquitous systems. The proposal has been applied to implement a middleware and a real home automation system to show its applicability and benefits.This research work is funded by the Project P10-TIC-6600 granted by the Andalusian Regional Government, and the Project 20F2/36 granted by CEI-BioTIC Granada. This work has also been partially supported by the “Contrato-Programa, Facultad de Educacin y Humanidades de Ceuta 2010-2012” of the University of Granada

A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals

Background Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored. Objective To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease. Methods A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions. Results The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores. Conclusion The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.R&D&i Project Ref. PID2019-109644RB-I00 funded by the Ministerio de Ciencia e Innovación / Agencia Estatal de Investigación / 10.13039/501100011033R&D&i Project Ref. B-TIC-320-UGR20 funded by Junta de Andalucía and “ERDF A way of making Europe.

Aplicación de una metodología colaborativa de aprendizaje activo basada en juegos de rol, para desarrollar el pensamiento crítico

El Espacio Europeo de Educación centra el aprendizaje en el colectivo del alumnado, y aboga a que se promueva el pensamiento crítico y el aprendizaje activo. El profesorado universitario necesita metodologías específicas que ayuden al logro de esos objetivos, especialmente considerando que el alumnado suele carecer de experiencia en el desarrollo de pensamiento crítico en el aula. En el presente proyecto nos planteamos el diseño e implantación de la metodología docente denominada “grupos analíticos” (“Analytic teams”), basada en un aprendizaje activo colaborativo y centrado en el alumnado, que pretende fomentar el análisis crítico. El desarrollo se basa en dividir la tarea compleja del pensamiento crítico en las actividades involucradas y asignarlas como roles a distintos estudiantes, que posteriormente trabajarán en grupo para completar la tarea de pensamiento crítico para tener una visión global de la misma. Esta técnica hace que los estudiantes sientan más confianza en sí mismos y en la tarea asignada y, al percibir que cada persona aporta algo valioso al grupo, facilita la equidad en la participación en clase. Esta técnica favorece no solo el pensamiento crítico y el trabajo en grupo, sino también la motivación, al tener una tarea importante en el grupo. También favorece la escucha activa, al ser necesaria una comunicación y diálogo entre todos los miembros del grupo, y por ende la adquisición de conocimientos que cada uno pueda aportar o que se descubren durante la tarea. Todos estos elementos son fundamentales tanto para un aprendizaje global como para su futura vida laboral

Reducing Response Time in Motor Imagery Using A Headband and Deep Learning

Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion-AEI) TIN2016-79484-REuropean Union (EU) TIN2016-79484-RSpanish Government PID2019-109644RB-I00/AEI/10.13039/501100011033 FPU18/0028