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Special Issue "Wearable and Nearable Biosensors and Systems for Healthcare"

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

Deadline for manuscript submissions: 15 June 2019

Special Issue Editors

Guest Editor
Prof. Marco Di Rienzo

Department of Biomedical Technology, IRCCS Fondazione Don Carlo Gnocchi, Milano, Italy
Website | E-Mail
Interests: textile sensors; smart garments; body sensor networks; 24-h and home cardiovascular monitoring; autonomic cardiac control; baroreflex modelling; seismocardiography; cardiac mechanics; hypertension; heart failure; space physiology, telemedicine, biosignal processing
Guest Editor
Prof. Ramakrishna Mukkamala

Department of Electrical & Computer Engineering, Michigan State University, East Lansing, MI, USA
Website | E-Mail
Interests: cardiovascular system; computational physiology; medical devices; mHealth; patient monitoring; physiologic sensors; physiologic signal processing and identification; whole-animal, human, and patient studies

Special Issue Information

Dear Colleagues,

Biosensors and systems in the form of wearables and “nearables” (everyday sensorized objects with transmitting capabilities, e.g., smartphones) are rapidly evolving and used for the monitoring of health, exercise activity, and performance. Unlike conventional approaches, these devices enable convenient, continuous, and/or unobtrusive monitoring of a user’s vital signs during daily life. Examples of measurements include biopotentials, body motion, pressure, blood flow, temperature, and biochemical markers. These systems combine innovations in sensor design, electronics, data transmission, power management and signal processing. However, although much research has been carried out so far in this area, many technological aspects still remain to be optimized and represent open challenges for the scientific community.

This Special Issue invites original research papers and review articles aimed at proposing advancements in wearable and nearable biosensors and systems for healthcare. Exemplary topics of interest include biocompatible and stretchable materials, textile/flexible/tattoo/remote sensors and electronics, device miniaturization, low-power signal conditioning, multisensor wireless data transmission and synchronization, advanced algorithms for the analysis of sensor data, and extraction of biological features.

Prof. Marco Di Rienzo
Prof. Ramakrishna Mukkamala
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 papers will be 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 1800 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

  • Biosensors
  • Low power systems
  • Mobile devices
  • Physiologic monitoring
  • Sensor miniaturization
  • Sensor signal processing
  • Stretchable and flexible sensors and electronics
  • Textile sensors
  • Flexible and tattoo sensors and electronics
  • Wearables
  • Nearables
  • Wireless data transmission

Published Papers (2 papers)

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Research

Open AccessArticle
Evaluation of a Commercial Ballistocardiography Sensor for Sleep Apnea Screening and Sleep Monitoring
Sensors 2019, 19(9), 2133; https://doi.org/10.3390/s19092133
Received: 5 February 2019 / Revised: 3 May 2019 / Accepted: 4 May 2019 / Published: 8 May 2019
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Abstract
There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of [...] Read more.
There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited ( R 2 of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep. Full article
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
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Open AccessArticle
Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods
Sensors 2019, 19(7), 1736; https://doi.org/10.3390/s19071736
Received: 24 February 2019 / Revised: 29 March 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
PDF Full-text (2287 KB) | HTML Full-text | XML Full-text
Abstract
Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the [...] Read more.
Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset. Full article
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
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