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Data Analytics for Mobile-Health
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Data Analytics for Mobile-Health

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

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 23059

Special Issue Editors

Dr. Italo Zoppis

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Lucia Manzoni

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, 20126 Milano, Italy
Interests: explainable AI; machine learning; knowledge base system
Dr. Giulia Cisotto

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, 35122 Padova PD, Italy
Interests: e-health; mobile-health; electrophysiological signals; data fusion; machine learning; explainability

Special Issue Information

Dear Colleagues,

Mobile health represents a promising horizon to promote the continuous and proactive monitoring of healthcare. The increasing development and commercial availability of wearables and low-cost portable devices is rapidly opening up new perspectives and opportunities for daily self-management of our own health and wellbeing. Mobile health and remote health are key technologies to promote public health (people can be assisted by clinical personnel remotely, without the need to regularly go to the hospital) and to relieve the costs associated to national healthcare.

At the same time, the development of mobile health solutions, including wearables and low-cost off-of-the-shelf biometric devices, have also raised previously unseen challenges: (a) the design of efficient data analytics based on limited setups with poor signal quality (compared to research-grade or clinical-purpose devices); (b) their lightweight implementation in a mobile-health framework; (c) the need for a human-understandable interpretation or explanation behind complex reasoning artificial intelligence-based data analytics; (d) the need for user-friendly and highly usable data visualization tools to increase the empowerment of the final users in managing their own health and wellbeing.

In this Special Issue, we invite original research papers and review articles aimed at promoting novel data analytics methods for mobile health solutions, methods for sensor fusion and data fusion and investigations about the explainability of available data analytics for mobile health, as well as on field experiences of mobile health applications.

Topics:

  • mobile health;
  • remote health;
  • in-home monitoring;
  • e-health;
  • wearables and body area networks;
  • smart biosensors;
  • low-cost sensors;
  • self-diagnosis;
  • data fusion;
  • sensor fusion;
  • deep learning;
  • machine learning;
  • artificial intelligence;
  • data analytics.

Dr. Italo Zoppis
Dr. Sara Lucia Manzoni
Dr. Giulia Cisotto
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.

Keywords

  • deep learning
  • machine learning
  • mobile-health
  • wearables
  • smart sensors
  • human data, self-diagnosis

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Published Papers (5 papers)

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Research

14 pages, 4132 KiB  
Article
Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics
by Nina Sviridova, Tiejun Zhao, Akimasa Nakano and Tohru Ikeguchi
Sensors 2022, 22(14), 5154; https://doi.org/10.3390/s22145154 - 9 Jul 2022
Viewed by 3598
Abstract
Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by [...] Read more.
Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of photoplethysmograms. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated with the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as determinism and entropy can be estimated with an error lower than 1% even for short photoplethysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed. Full article
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
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15 pages, 2278 KiB  
Article
Vital Signs Prediction for COVID-19 Patients in ICU
by Ahmed Youssef Ali Amer, Femke Wouters, Julie Vranken, Pauline Dreesen, Dianne de Korte-de Boer, Frank van Rosmalen, Bas C. T. van Bussel, Valérie Smit-Fun, Patrick Duflot, Julien Guiot, Iwan C. C. van der Horst, Dieter Mesotten, Pieter Vandervoort, Jean-Marie Aerts and Bart Vanrumste
Sensors 2021, 21(23), 8131; https://doi.org/10.3390/s21238131 - 5 Dec 2021
Cited by 11 | Viewed by 4222
Abstract
This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models [...] Read more.
This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time. Full article
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
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16 pages, 1730 KiB  
Article
Face with Mask Detection in Thermal Images Using Deep Neural Networks
by Natalia Głowacka and Jacek Rumiński
Sensors 2021, 21(19), 6387; https://doi.org/10.3390/s21196387 - 24 Sep 2021
Cited by 14 | Viewed by 5115
Abstract
As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for [...] Read more.
As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision—mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health. Full article
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
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16 pages, 1389 KiB  
Article
Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)
by Carlo Dindorf, Jürgen Konradi, Claudia Wolf, Bertram Taetz, Gabriele Bleser, Janine Huthwelker, Friederike Werthmann, Eva Bartaguiz, Johanna Kniepert, Philipp Drees, Ulrich Betz and Michael Fröhlich
Sensors 2021, 21(18), 6323; https://doi.org/10.3390/s21186323 - 21 Sep 2021
Cited by 38 | Viewed by 4405
Abstract
Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations [...] Read more.
Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively. Full article
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
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22 pages, 2835 KiB  
Article
A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare
by Andrei Velichko
Sensors 2021, 21(18), 6209; https://doi.org/10.3390/s21186209 - 16 Sep 2021
Cited by 20 | Viewed by 4236
Abstract
Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these [...] Read more.
Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems. Full article
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
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