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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 41301

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Dr. Gabriele S. de Blasio

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Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain
Interests: Indoor positioning and navigation systems, intelligent transportation systems, artificial vision and natural neural networks

Dr. Alexis Quesada-Arencibia

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Guest Editor

Dr. Carmelo R. García

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Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain
Interests: ubiquitous computing; smart environments; intelligent transport systems; mobile information systems
Special Issues, Collections and Topics in MDPI journals

Dr. José Bravo

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Guest Editor

Department of Information Systems and Technologies, University of Castilla–La Mancha, 13071 Ciudad Real, Spain
Interests: ubiquitous computing; ambient intelligence; AAL & m-Health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 13th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2019) will take place in Toledo (Spain), 2–5 December, 2019.

The UCAmI conference is an event that promotes the participation of specialists in different fields related to Ubiquitous Computing and Ambient Intelligence. In this Special Issue, the aim is to encourage leading, as well as new academic and industry practitioners, to engage in research relating to these fields. Authors of the selected papers from the conference are invited to submit the extended versions of their original papers and contributions regarding the following topics:

Intelligent transportation systems
Data science for transport and tourism
IoT and wearable sensors
Smart cities
Ubiquitous computing and ambient intelligence
Smart environments for health
Artificial vision systems and applications
Ad-Hoc sensor networks and security
Indoor positioning and navigation systems
Human-Computer and Human-Ambient Interaction
Internet of people

Dr. Gabriele S. de Blasio
Dr. Alexis Quesada-Arencibia
Dr. Carmelo R. García
Dr. José Bravo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

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Research

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21 pages, 2297 KiB

Open AccessArticle

Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning

by Irvin Hussein Lopez-Nava, Luis M. Valentín-Coronado, Matias Garcia-Constantino and Jesus Favela

Sensors 2020, 20(17), 4756; https://doi.org/10.3390/s20174756 - 23 Aug 2020

Cited by 14 | Viewed by 3458

Abstract

Activity recognition is one of the most active areas of research in ubiquitous computing. In particular, gait activity recognition is useful to identify various risk factors in people’s health that are directly related to their physical activity. One of the issues in activity recognition, and gait in particular, is that often datasets are unbalanced (i.e., the distribution of classes is not uniform), and due to this disparity, the models tend to categorize into the class with more instances. In the present study, two methods for classifying gait activities using accelerometer and gyroscope data from a large-scale public dataset were evaluated and compared. The gait activities in this dataset are: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The proposed methods are based on conventional (shallow) and deep learning techniques. In addition, data were evaluated from three data treatments: original unbalanced data, sampled data, and augmented data. The latter was based on the generation of synthetic data according to segmented gait data. The best results were obtained with classifiers built with augmented data, with F-measure results of 0.812 (

σ

= 0.078) for the shallow learning approach, and of 0.927 (

σ

= 0.033) for the deep learning approach. In addition, the data augmentation strategy proposed to deal with the unbalanced problem resulted in increased classification performance using both techniques. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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26 pages, 1161 KiB

Open AccessArticle

Exploring the Application of the FOX Model to Foster Pro-Environmental Behaviours in Smart Environments

by Ane Irizar-Arrieta, Diego Casado-Mansilla, Aiur Retegi, Matthias Laschke and Diego López-de-Ipiña

Sensors 2020, 20(16), 4576; https://doi.org/10.3390/s20164576 - 14 Aug 2020

Cited by 1 | Viewed by 2621

Abstract

The heterogeneity and dynamism of people make addressing user diversity and its categorisation critical factors, which should be carefully considered when developing pro-environmental strategies and interventions. Nevertheless, the complexities of individuals complicates the creation of modelling and classification systems. The aforementioned issue opens a research opportunity, which should be tackled to improve the development of human-centric systems and processes. Throughout the present piece of research, our objective is to bridge that gap by extracting knowledge and insights relating to how to address user diversity when designing technologies considering sustainable behaviour. For this, we explore the possibilities of the FOX model—an early meta-model to approach the diversity of individuals when addressing pro-environmental behaviour—to classify and understand individuals while taking their heterogeneity into account. After introducing the model, a qualitative survey of eight experts is conducted. From this study, relevant findings are analysed and exposed. Taking into account the gathered knowledge, three user profiles are developed, based on the dimensions proposed by the model. Furthermore, scenarios are created for each profile, presenting three case studies where different application modes of the model are described (personalised interventions, prediction and forecasting, and individual and collective interventions). Finally, the extracted findings are analysed, discussing the main issues related to the development of pro-environmental technologies and systems. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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26 pages, 567 KiB

Open AccessArticle

Feature Selection for Health Care Costs Prediction Using Weighted Evidential Regression

by Belisario Panay, Nelson Baloian, José A. Pino, Sergio Peñafiel, Horacio Sanson and Nicolas Bersano

Sensors 2020, 20(16), 4392; https://doi.org/10.3390/s20164392 - 06 Aug 2020

Cited by 7 | Viewed by 2685

Abstract

Although many authors have highlighted the importance of predicting people’s health costs to improve healthcare budget management, most of them do not address the frequent need to know the reasons behind this prediction, i.e., knowing the factors that influence this prediction. This knowledge allows avoiding arbitrariness or people’s discrimination. However, many times the black box methods (that is, those that do not allow this analysis, e.g., methods based on deep learning techniques) are more accurate than those that allow an interpretation of the results. For this reason, in this work, we intend to develop a method that can achieve similar returns as those obtained with black box methods for the problem of predicting health costs, but at the same time it allows the interpretation of the results. This interpretable regression method is based on the Dempster-Shafer theory using Evidential Regression (EVREG) and a discount function based on the contribution of each dimension. The method “learns” the optimal weights for each feature using a gradient descent technique. The method also uses the nearest k-neighbor algorithm to accelerate calculations. It is possible to select the most relevant features for predicting a patient’s health care costs using this approach and the transparency of the Evidential Regression model. We can obtain a reason for a prediction with a k-NN approach. We used the Japanese health records at Tsuyama Chuo Hospital to test our method, which included medical examinations, test results, and billing information from 2013 to 2018. We compared our model to methods based on an Artificial Neural Network, Gradient Boosting, Regression Tree and Weighted k-Nearest Neighbors. Our results showed that our transparent model performed like the Artificial Neural Network and Gradient Boosting with an

R^{2}

0.44

. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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16 pages, 17242 KiB

Open AccessArticle

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

by Guillermo Almonacid-Olleros, Gabino Almonacid, Juan Ignacio Fernandez-Carrasco, Macarena Espinilla-Estevez and Javier Medina-Quero

Sensors 2020, 20(15), 4224; https://doi.org/10.3390/s20154224 - 29 Jul 2020

Cited by 17 | Viewed by 3968

Abstract

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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20 pages, 3268 KiB

Open AccessArticle

Towards Outlier Sensor Detection in Ambient Intelligent Platforms—A Low-Complexity Statistical Approach

by Diego Martín, Damaris Fuentes-Lorenzo, Borja Bordel and Ramón Alcarria

Sensors 2020, 20(15), 4217; https://doi.org/10.3390/s20154217 - 29 Jul 2020

Cited by 7 | Viewed by 2225

Abstract

Sensor networks in real-world environments, such as smart cities or ambient intelligent platforms, provide applications with large and heterogeneous sets of data streams. Outliers—observations that do not conform to an expected behavior—has then turned into a crucial task to establish and maintain secure and reliable databases in this kind of platforms. However, the procedures to obtain accurate models for erratic observations have to operate with low complexity in terms of storage and computational time, in order to attend the limited processing and storage capabilities of the sensor nodes in these environments. In this work, we analyze three binary classifiers based on three statistical prediction models—ARIMA (Auto-Regressive Integrated Moving Average), GAM (Generalized Additive Model), and LOESS (LOcal RegrESSion)—for outlier detection with low memory consumption and computational time rates. As a result, we provide (1) the best classifier and settings to detect outliers, based on the ARIMA model, and (2) two real-world classified datasets as ground truths for future research. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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19 pages, 3779 KiB

Open AccessArticle

A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare

by Andrea Vázquez-Ingelmo, Alicia García-Holgado, Francisco José García-Peñalvo and Roberto Therón

Sensors 2020, 20(15), 4072; https://doi.org/10.3390/s20154072 - 22 Jul 2020

Cited by 10 | Viewed by 2962

Abstract

Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and know-how—into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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20 pages, 8173 KiB

Open AccessArticle

RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments

by Andrés Fuster-Guilló, Jorge Azorín-López, Marcelo Saval-Calvo, Juan Miguel Castillo-Zaragoza, Nahuel Garcia-D'Urso and Robert B. Fisher

Sensors 2020, 20(13), 3690; https://doi.org/10.3390/s20133690 - 01 Jul 2020

Cited by 8 | Viewed by 3660

Abstract

This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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23 pages, 2656 KiB

Open AccessArticle

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

by Francisco M. Garcia-Moreno, Maria Bermudez-Edo, José Luis Garrido, Estefanía Rodríguez-García, José Manuel Pérez-Mármol and María José Rodríguez-Fórtiz

Sensors 2020, 20(12), 3427; https://doi.org/10.3390/s20123427 - 17 Jun 2020

Cited by 26 | Viewed by 4503

Abstract

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. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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30 pages, 16529 KiB

Open AccessArticle

A Very High-Speed Validation Scheme Based on Template Matching for Segmented Character Expiration Codes on Beverage Cans

by José C. Rodríguez-Rodríguez, Gabriele S. de Blasio, Carmelo R. García and Alexis Quesada-Arencibia

Sensors 2020, 20(11), 3157; https://doi.org/10.3390/s20113157 - 02 Jun 2020

Viewed by 2086

Abstract

This paper expands upon a previous publication and is the natural continuation of an earlier study which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. MONICOD is distinguished by its high operating speed, running at 200 frames per second and validating up to 35 cans per second. This paper adds further detail to this description by describing the final stage of the MONICOD industrial validator: the process of effectively validating the characters. In this process we compare the acquired shapes, segmented during the prior stages, with expected character shapes. To do this, we use a template matching scheme (here called “morphologies”) based on bitwise operations. Two learning algorithms for building the valid morphology databases are also presented. The results of the study presented here show that in the acquisition of 9885 frames containing 465 cans to be validated, there was only one false positive (0.21% of the total). Another notable feature is that it is at least 20% faster in validation time with error rates similar to those of classifiers such as support vector machines (SVM), radial base functions (RBF), multi-layer perceptron with backpropagation (MLP) and k-nearest neighbours (KNN). Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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22 pages, 1037 KiB

Open AccessArticle

Emulating and Evaluating Virtual Remote Laboratories for Cybersecurity

by Antonio Robles-Gómez, Llanos Tobarra, Rafael Pastor-Vargas, Roberto Hernández and Jesús Cano

Sensors 2020, 20(11), 3011; https://doi.org/10.3390/s20113011 - 26 May 2020

Cited by 10 | Viewed by 3225

Abstract

Our society is nowadays evolving towards a digital era, due to the extensive use of computer technologies and their interconnection mechanisms, i.e., social networks, Internet resources, IoT services, etc. This way, new threats and vulnerabilities appear. Therefore, there is an urgent necessity of training students in the topic of cybersecurity, in which practical skills have to be acquired. In distance education, the inclusion of on-line resources for hands-on activities in its curricula is a key step in meeting that need. This work presents several contributions. First, the fundamentals of a virtual remote laboratory hosted in the cloud are detailed. This laboratory is a step forward since the laboratory combines both virtualization and cloud paradigms to dynamically create emulated environments. Second, this laboratory has also been integrated into the practical curricula of a cybersecurity subject, as an additional on-line resource. Third, the students’ traceability, in terms of their interactions with the laboratory, is also analyzed. Psychological TAM/UTAUT factors (perceived usefulness, estimated effort, social influence, attitude, ease of access) that may affect the intention of using the laboratory are analyzed. Fourth, the degree of satisfaction is analyzed with a great impact, since the mean values of these factors are most of them higher than 4 points out of 5. In addition to this, the students’ acceptance of the presented technology is exhaustively studied. Two structural equation models have been hypothesized and validated. Finally, the acceptance of the technology can be concluded as very good in order to be used in other Engineering contexts. In this sense, the calculated statistical values for the improved proposed model are within the expected ranges of reliability (X2 = 0.6, X2/DF = 0.3, GFI = 0.985, CIF = 0.985, RMSEA = 0) by considering the literature. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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23 pages, 7416 KiB

Open AccessArticle

Developing an Interactive Environment through the Teaching of Mathematics with Small Robots

by Lilia Muñoz, Vladimir Villarreal, Itza Morales, Joseph Gonzalez and Mel Nielsen

Sensors 2020, 20(7), 1935; https://doi.org/10.3390/s20071935 - 30 Mar 2020

Cited by 18 | Viewed by 4208

Abstract

The article is the product of the study “Development of innovative resources to improve logical-mathematical skills in primary school, through educational robotics”, developed during the 2019 school year in three public schools in the province of Chiriquí, Republic of Panama. The teaching-learning process in students is influenced by aspects inside and outside the classroom, since not all schools have the necessary resources to deliver content or teaching material. The general objective of the project is to design, develop and implement educational robotics to improve logical-mathematical skills aimed at preschool and first grade students in public schools, using programmable educational robots. For this, a set of resources and activities were developed to improve the logical-mathematical skills of the initial stages, in public schools, obtaining significant results. Playful activities favor the teaching-learning process. Considering the analysis of the results made on the data obtained through the applied collection instruments, it can be argued that in general terms the values indicate that the students obtained a favorable level of performance in the different challenges proposed. The project has allowed the academic community to have an application of great value that allows teaching about the conservation of natural sites. The project only covers the area of mathematics in preschool and first grade. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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Review

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16 pages, 989 KiB

Open AccessReview

Enabling Older Adults’ Health Self-Management through Self-Report and Visualization—A Systematic Literature Review

by Gabriela Cajamarca, Valeria Herskovic and Pedro O. Rossel

Sensors 2020, 20(15), 4348; https://doi.org/10.3390/s20154348 - 04 Aug 2020

Cited by 17 | Viewed by 4921

Abstract

Aging is associated with a progressive decline in health, resulting in increased medical care and costs. Mobile technology may facilitate health self-management, thus increasing the quality of care and reducing costs. Although the development of technology offers opportunities in monitoring the health of older adults, it is not clear whether these technologies allow older adults to manage their health data themselves. This paper presents a review of the literature on mobile health technologies for older adults, focusing on whether these technologies enable the visualization of monitored data and the self-reporting of additional information by the older adults. The systematic search considered studies published between 2009 and 2019 in five online databases. We screened 609 articles and identified 95 that met our inclusion and exclusion criteria. Smartphones and tablets are the most frequently reported technology for older adults to enter additional data to the one that is monitored automatically. The recorded information is displayed on the monitoring device and screens of external devices such as computers. Future designs of mobile health technology should allow older users to enter additional information and visualize data; this could enable them to understand their own data as well as improve their experience with technology. Full article

(This article belongs to the Special Issue Selected Papers from UCAmI 2019)

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