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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 18464

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Special Issue Editor

Dr. Natividad Duro Carralero

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Guest Editor

Department of Computer Sciences and Automatic Control, UNED, C/Juan del Rosal, 16, 28040 Madrid, Spain
Interests: sensor data fusion; industry applications; machine learning; data analysis algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, medical and industrial applications use many different sensors, because it is increasingly common to collect as much information as possible from our systems. New technologies allow us to analyze these data and obtain relevant information from them. From this analysis, it is possible to support important decision making that leads to higher productivity and more efficient operations.

In all these applications, we can collect the information from physical sensors, but it is also very common to connect some sensors of our systems to the Internet. The Internet of Things (IoT) represents a revolution but also a real trend in the software industry. The use of the IoT implies a fusion between the digital and the physical worlds, involving sensors that are embedded in all kinds of devices interconnected between them and increasing the amount of data to analyze. To analyze these large volumes of data from multiple sources, we need to use special mathematical methods, algorithms, and techniques which help us to understand these data.

This Special Issue encourages authors from academia, medicine, and industry to submit new research results from the use of multiple sensors in applications of these areas. The sensors can be directly connected to the system, but they can also be connected to the Internet. The topics of this Special Issue will include, but are not limited to:

Sensors for medical applications;
IoT environments for medical applications;
Sensors for industrial applications;
IoT environments for industrial applications;
Mathematical algorithms and techniques to analyze medical sensor data;
Mathematical algorithms and techniques to analyze industrial sensor data.

Dr. Natividad Duro Carralero
Guest Editor

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.

Keywords

sensors;
medical applications;
industrial applications;
IoT environment;
mathematical algorithms for data fusion

Published Papers (5 papers)

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Research

26 pages, 18140 KiB

Open AccessArticle

Automated Non-Contact Respiratory Rate Monitoring of Neonates Based on Synchronous Evaluation of a 3D Time-of-Flight Camera and a Microwave Interferometric Radar Sensor

by Johanna Gleichauf, Sven Herrmann, Lukas Hennemann, Hannes Krauss, Janina Nitschke, Philipp Renner, Christine Niebler and Alexander Koelpin

Sensors 2021, 21(9), 2959; https://doi.org/10.3390/s21092959 - 23 Apr 2021

Cited by 11 | Viewed by 4120

Abstract

This paper introduces an automatic non-contact monitoring method based on the synchronous evaluation of a 3D time-of-flight (ToF) camera and a microwave interferometric radar sensor for measuring the respiratory rate of neonates. The current monitoring on the Neonatal Intensive Care Unit (NICU) has several issues which can cause pressure marks, skin irritations and eczema. To minimize these risks, a non-contact system made up of a 3D time-of-flight camera and a microwave interferometric radar sensor is presented. The 3D time-of-flight camera delivers 3D point clouds which can be used to calculate the change in distance of the moving chest and from it the respiratory rate. The disadvantage of the ToF camera is that the heartbeat cannot be determined. The microwave interferometric radar sensor determines the change in displacement caused by the respiration and is even capable of measuring the small superimposed movements due to the heartbeat. The radar sensor is very sensitive towards movement artifacts due to, e.g., the baby moving its arms. To allow a robust vital parameter detection the data of both sensors was evaluated synchronously. In this publication, we focus on the first step: determining the respiratory rate. After all processing steps, the respiratory rate determined by the radar sensor was compared to the value received from the 3D time-of-flight camera. The method was validated against our gold standard: a self-developed neonatal simulation system which can simulate different breathing patterns. In this paper, we show that we are the first to determine the respiratory rate by evaluating the data of an interferometric microwave radar sensor and a ToF camera synchronously. Our system delivers very precise breaths per minute (BPM) values within the norm range of 20–60 BPM with a maximum difference of 3 BPM (for the ToF camera itself at 30 BPM in normal mode). Especially in lower respiratory rate regions, i.e., 5 and 10 BPM, the synchronous evaluation is required to compensate the drawbacks of the ToF camera. In the norm range, the ToF camera performs slightly better than the radar sensor. Full article

(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)

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20 pages, 5514 KiB

Open AccessArticle

ResUHUrge: A Low Cost and Fully Functional Ventilator Indicated for Application in COVID-19 Patients

by Francisco José Vivas Fernández, José Sánchez Segovia, Ismael Martel Bravo, Carlos García Ramos, Daniel Ruiz Castilla, José Gamero López and José Manuel Andújar Márquez

Sensors 2020, 20(23), 6774; https://doi.org/10.3390/s20236774 - 27 Nov 2020

Cited by 6 | Viewed by 3129

Abstract

Although the cure for the SARS-CoV-2 virus (COVID-19) will come in the form of pharmaceutical solutions and/or a vaccine, one of the only ways to face it at present is to guarantee the best quality of health for patients, so that they can overcome the disease on their own. Therefore, and considering that COVID-19 generally causes damage to the respiratory system (in the form of lung infection), it is essential to ensure the best pulmonary ventilation for the patient. However, depending on the severity of the disease and the health condition of the patient, the situation can become critical when the patient has respiratory distress or becomes unable to breathe on his/her own. In that case, the ventilator becomes the lifeline of the patient. This device must keep patients stable until, on their own or with the help of medications, they manage to overcome the lung infection. However, with thousands or hundreds of thousands of infected patients, no country has enough ventilators. If this situation has become critical in the Global North, it has turned disastrous in developing countries, where ventilators are even more scarce. This article shows the race against time of a multidisciplinary research team at the University of Huelva, UHU, southwest of Spain, to develop an inexpensive, multifunctional, and easy-to-manufacture ventilator, which has been named ResUHUrge. The device meets all medical requirements and is developed with open-source hardware and software. Full article

(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)

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15 pages, 33710 KiB

Open AccessArticle

Spectral–Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification

by Hiren K Mewada, Amit V Patel, Mahmoud Hassaballah, Monagi H. Alkinani and Keyur Mahant

Sensors 2020, 20(17), 4747; https://doi.org/10.3390/s20174747 - 22 Aug 2020

Cited by 30 | Viewed by 3473

Abstract

Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral–spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral–spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively. Full article

(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)

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21 pages, 2697 KiB

Open AccessArticle

Feature Extraction from Building Submetering Networks Using Deep Learning

by Antonio Morán, Serafín Alonso, Daniel Pérez, Miguel A. Prada, Juan José Fuertes and Manuel Domínguez

Sensors 2020, 20(13), 3665; https://doi.org/10.3390/s20133665 - 30 Jun 2020

Cited by 6 | Viewed by 2956

Abstract

The understanding of the nature and structure of energy use in large buildings is vital for defining novel energy and climate change strategies. The advances on metering technology and low-cost devices make it possible to form a submetering network, which measures the main supply and other intermediate points providing information of the behavior of different areas. However, an analysis by means of classical techniques can lead to wrong conclusions if the load is not balanced. This paper proposes the use of a deep convolutional autoencoder to reconstruct the whole consumption measured by the submeters using the learnt features in order to analyze the behavior of different building areas. The display of weights and information of the latent space provided by the autoencoder allows us to obtain precise details of the influence of each area in the whole building consumption and its dependence on external factors such as temperature. A submetering network is deployed in the León University Hospital building in order to test the proposed methodology. The results show different correlations between environmental variables and building areas and indicate that areas can be grouped depending on their function in the building performance. Furthermore, this approach is able to provide discernible results in the presence of large differences with respect to the consumption ranges of the different areas, unlike conventional approaches where the influence of smaller areas is usually hidden. Full article

(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)

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14 pages, 4046 KiB

Open AccessArticle

An Oximetry Based Wireless Device for Sleep Apnea Detection

by Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias and Antonio G. Ravelo-García

Sensors 2020, 20(3), 888; https://doi.org/10.3390/s20030888 - 07 Feb 2020

Cited by 24 | Viewed by 3799

Abstract

Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis. Full article

(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)

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