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Special Issue "Applications and Innovations on Sensor-Enabled Wearable Devices"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Elena Simona Lohan
Website
Guest Editor
Electrical Engineering unit, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland
Interests: wireless positioning and navigation; 5G; wearable computing; statistical signal processing; IoT
Special Issues and Collections in MDPI journals
Prof. Dr. Antonella Molinaro
Website
Guest Editor
DIIES Department, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
Interests: wireless and mobile networks; vehicular networks; 5G systems; future internet
Special Issues and Collections in MDPI journals
Prof. Dr. Adriano Moreira
Website
Guest Editor
Algoritmi Research Centre, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Interests: indoor positioning; mobile and context-aware computing; urban computing; human mobility analysis and simulation of wireless and mobile networks in urban contexts
Dr. Alexandru Rusu

Guest Editor
Communications and Signal Processing Research Center, Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, Splaiul Independentei nr. 313, Sector 6, Bucuresti 060042, Romania
Interests: radio navigation; wireless communications; satellite communication systems
Prof. Dr. Zdenek Smekal
Website
Guest Editor
Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic
Interests: digital signal processing; neural networks; pattern recognition; machine learning; speech and image processing; artificial intelligence
Dr. Joaquín Torres-Sospedra
Website
Guest Editor
Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón de la Plana, Spain
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localisation and positioning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wearables devices are expected to increasingly permeate our world, applied as they are to a very large variety of applications, including leisure, health, communication, security or work. In the next few years, wearables are expected to disrupt most business sectors, and it is expected that the number of wearable shipments will exceed 200 million by 2021.

Multiple sensors are embedded in wearable devices. Sensors are mainly included for tracking information on the user’s physical activity and physiological parameters, but additional sensors are also included for radio-communications and other purposes. This advanced complex sensory system enables wearables to be a source of invaluable crowdsourced data, where sensor fusion may bring innovative applications in many fields (engineering, telecommunications, computer science, eHealth, Internet of Things, Sensor Networks, etc.).

This Special Issue encourages authors from academia and industry to submit new research results about technological innovations and novel applications for wearable devices, with special interest in positioning. The Special Issue topics include but are not limited to:

  • Ambient assisted living;
  • Artificial Intelligence;
  • CloudEdgeFog computing;
  • Computer vision;
  • Collaborative positioning;
  • Crowdsensing;
  • Cryptography;
  • Cybersecurity;
  • D2D communications;
  • Data fusion;
  • eHealth solutions;
  • Internet of Things (IoT);
  • Low-latency and industrial applications;
  • Machine learning, deep learning;
  • Mesh sensor networks;
  • Mobile computing;
  • Software design;
  • Supporting architectures and platforms;
  • Systems on Chip (SoC);
  • Testing and evaluation protocols, strategies, and standards;
  • Wireless indoor positioning, localization, and navigation.

Dr. Elena Simona Lohan
Prof. Dr. Antonella Molinaro
Prof. Dr. Adriano Moreira
Dr. Alexandru Rusu
Prof. Dr. Zdenek Smekal
Dr. Joaquín Torres-Sospedra
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 2000 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 (5 papers)

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Research

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Open AccessArticle
Automatic Museum Audio Guide
Sensors 2020, 20(3), 779; https://doi.org/10.3390/s20030779 - 31 Jan 2020
Abstract
An automatic “museum audio guide” is presented as a new type of audio guide for museums. The device consists of a headset equipped with a camera that captures exhibit pictures and the eyes of things computer vision device (EoT). The EoT board is [...] Read more.
An automatic “museum audio guide” is presented as a new type of audio guide for museums. The device consists of a headset equipped with a camera that captures exhibit pictures and the eyes of things computer vision device (EoT). The EoT board is capable of recognizing artworks using features from accelerated segment test (FAST) keypoints and a random forest classifier, and is able to be used for an entire day without the need to recharge the batteries. In addition, an application logic has been implemented, which allows for a special highly-efficient behavior upon recognition of the painting. Two different use case scenarios have been implemented. The main testing was performed with a piloting phase in a real world museum. Results show that the system keeps its promises regarding its main benefit, which is simplicity of use and the user’s preference of the proposed system over traditional audioguides. Full article
(This article belongs to the Special Issue Applications and Innovations on Sensor-Enabled Wearable Devices)
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Review

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Open AccessReview
Arrowhead Technology for Digitalization and Automation Solution: Smart Cities and Smart Agriculture
Sensors 2020, 20(5), 1464; https://doi.org/10.3390/s20051464 - 06 Mar 2020
Abstract
The Internet of Things (IoT) concept has met requirements for security and reliability in domains like automotive industry, food industry, as well as precision agriculture. Furthermore, System of Systems (SoS) expands the use of local clouds for the evolution of integration and communication [...] Read more.
The Internet of Things (IoT) concept has met requirements for security and reliability in domains like automotive industry, food industry, as well as precision agriculture. Furthermore, System of Systems (SoS) expands the use of local clouds for the evolution of integration and communication technologies. SoS devices need to ensure Quality of Service (QoS) capabilities including service-oriented management and different QoS characteristics monitoring. Smart applications depend on information quality since they are driven by processes which require communication robustness and enough bandwidth. Interconnectivity and interoperability facilities among different smart devices can be achieved using Arrowhead Framework technology via its core systems and services. Arrowhead Framework is targeting smart IoT devices with wide applicability areas including smart building, smart energy, smart cities, smart agriculture, etc. The advantages of Arrowhead Framework can be underlined by parameters such as transmission speed, latency, security, etc. This paper presents a survey of Arrowhead Framework in IoT/SoS dedicated architectures for smart cities and smart agriculture developed around smart cities, aiming to outline its significant impact on the global performances. The advantages of Arrowhead Framework technology are emphasized by analysis of several smart cities use-cases and a novel architecture for a telemetry system that will enable the use of Arrowhead technology in smart agriculture area is introduced and detailed by authors. Full article
(This article belongs to the Special Issue Applications and Innovations on Sensor-Enabled Wearable Devices)
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Open AccessReview
Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review
Sensors 2019, 19(21), 4678; https://doi.org/10.3390/s19214678 - 28 Oct 2019
Cited by 2
Abstract
Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette [...] Read more.
Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers’ behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions. Full article
(This article belongs to the Special Issue Applications and Innovations on Sensor-Enabled Wearable Devices)
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Open AccessReview
Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions
Sensors 2019, 19(12), 2651; https://doi.org/10.3390/s19122651 - 12 Jun 2019
Cited by 3
Abstract
With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed [...] Read more.
With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military. Full article
(This article belongs to the Special Issue Applications and Innovations on Sensor-Enabled Wearable Devices)
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Open AccessReview
Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review
Sensors 2019, 19(11), 2438; https://doi.org/10.3390/s19112438 - 28 May 2019
Cited by 2
Abstract
With the growing interest in daily activity monitoring, several insole designs have been developed to identify postures, detect activities, and count steps. However, the validity of these devices is not clearly established. The aim of this systematic review was to synthesize the available [...] Read more.
With the growing interest in daily activity monitoring, several insole designs have been developed to identify postures, detect activities, and count steps. However, the validity of these devices is not clearly established. The aim of this systematic review was to synthesize the available information on the criterion validity of instrumented insoles in detecting postures activities and steps. The literature search through six databases led to 33 articles that met inclusion criteria. These studies evaluated 17 different insole models and involved 290 participants from 16 to 75 years old. Criterion validity was assessed using six statistical indicators. For posture and activity recognition, accuracy varied from 75.0% to 100%, precision from 65.8% to 100%, specificity from 98.1% to 100%, sensitivity from 73.0% to 100%, and identification rate from 66.2% to 100%. For step counting, accuracies were very high (94.8% to 100%). Across studies, different postures and activities were assessed using different criterion validity indicators, leading to heterogeneous results. Instrumented insoles appeared to be highly accurate for steps counting. However, measurement properties were variable for posture and activity recognition. These findings call for a standardized methodology to investigate the measurement properties of such devices. Full article
(This article belongs to the Special Issue Applications and Innovations on Sensor-Enabled Wearable Devices)
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