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Special Issue "Selected Papers from the 20th IEEE International Conference on E-health Networking, Application & Services (IEEE HealthCom 2018)"

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

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editors

Guest Editor
Prof. Norbert Noury

University Claude Bernard Lyon 1, Lyon, France
Website | E-Mail
Guest Editor
Dr. Martin Černý

VSB-Technical University Ostrava, Czech republic
Website | E-Mail

Special Issue Information

Dear Colleagues,

The 20th IEEE International Conference on E-Health Networking, Application and Services (http://healthcom2018.ieee-healthcom.org/) will be held in Ostrava, 17–20 September, 2018. Authors of selected papers from the conference are invited to submit the extended versions of their original papers and contributions regarding the following topics:

Medical, Biomedical and Health Informatics

  • Electronic medical records (EMR) and electronic prescription
  • Data preprocessing, cleansing, management and mining
  • Data quality assessment and improvement
  • Medical imaging
  • Computer-aided detection, hypothesis generation and diagnosis
  • Evidence-based medicine
  • Evolutionary and longitudinal patient and disease models
  • Clinical workflow
  • Medication adherence and health monitoring
  • Smart health and big data
  • Deep IoT analysis
  • M2M

Devices

  • High-confidence medical devices
  • Integration of medical devices with e-Health
  • Medical device interoperability
  • Wearable devices
  • In/on/around-body sensors and actuators
  • Biosensors at the micro/nano-scale
  • Smart garments/textiles
  • Wireless energy transfer
  • Energy harvesting
  • Device security

Communications and Networking

  • Communication/network infrastructures, architectures and protocols for e-Health
  • 5G
  • Soft-SIM technology
  • Narrowband technology
  • Antennas and propagation
  • Proximity-based communication, group communication and social networks
  • Power-efficient communication
  • Ultra-wideband communication
  • Delay-tolerant, fault-tolerant and reliable communication
  • Cognitive communication for medical bands
  • In-hospital networking, body area networking and cloud-integrated networking
  • Software-defined networks and network management
  • Network Function Virtualization
  • Nanoscale/molecular communications
  • Network coding and error detection/correction
  • Resilience and robustness
  • Security

Signal/Data Processing and Systems

  • Context awareness and situation awareness
  • Image/video processing and computer/robot vision
  • Internet of Things, Ambient intelligence and pervasive computing
  • Augmented reality and human-computer interaction
  • Motion detection and activity recognition
  • User modeling and personalization
  • Robotics
  • Computing/storage infrastructures for e-Health such as clouds and virtualization
  • Software, systems and performance engineering for e-Health
  • Security

Services and Applications

  • e-Health services/applications for physical and mental health; for example, in acute care, chronic care, mental health care, biomedical engineering, rehabilitation, prosthetics, elderly/nursing care, smart homes and hospitals, and rural/wilderness practice.
  • e-Health services/applications for sports and exercise; for example, in training prescription and feedback, concussion detection/monitoring, life-logging and fitness monitoring.
  • e-Health services/applications for public health; for example, disease prevention, pandemic preparedness, epidemiological interventions and smart cities.
  • e-Health services/applications for extreme environments; for example, in firefighting, disaster response, evacuation assistance, medical triage, space travel/exploration, deep diving and deep sea exploration
  • m-Health applications and software
  • Quality of experience (QoE) with e-Health services/applications.
  • Security, privacy and trust for e-Health services/applications
  • Emerging cloud-based services/applications including health clouds/grids

System Research

  • Standardization
  • Requirements Engineering
  • Social technological alignment
  • E-health and m-health governance
  • Quality of care
  • Business modeling
  • Supply chain management
  • Anti-counterfeiting
  • Smart Pharmaceuticals
  • Global e-health strategies
  • Tagging and tracking
  • Work flow
  • Patient flow

E-Health Solutions to Challenging Problems

  • Telemedicine for rural area
  • Aging problems and intelligent care
  • Bioinformatics and precision medicine
  • Smart agriculture for human health

Medical Doctors section

  • e-Health case studied—applications
  • e-Health challenges from M.D. point of view

Prof. Norbert Noury
Dr. Martin Černý
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.

Published Papers (5 papers)

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Research

Open AccessArticle
Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care
Sensors 2019, 19(6), 1407; https://doi.org/10.3390/s19061407
Received: 31 January 2019 / Revised: 7 March 2019 / Accepted: 13 March 2019 / Published: 21 March 2019
PDF Full-text (12035 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real [...] Read more.
Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO2 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO2 levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%. Full article
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Open AccessArticle
Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments
Sensors 2019, 19(5), 1114; https://doi.org/10.3390/s19051114
Received: 30 November 2018 / Revised: 16 February 2019 / Accepted: 27 February 2019 / Published: 5 March 2019
Cited by 1 | PDF Full-text (412 KB) | HTML Full-text | XML Full-text
Abstract
Medical Cyber-Physical Systems (MCPS) hold the promise of reducing human errors and optimizing healthcare by delivering new ways to monitor, diagnose and treat patients through integrated clinical environments (ICE). Despite the benefits provided by MCPS, many of the ICE medical devices have not [...] Read more.
Medical Cyber-Physical Systems (MCPS) hold the promise of reducing human errors and optimizing healthcare by delivering new ways to monitor, diagnose and treat patients through integrated clinical environments (ICE). Despite the benefits provided by MCPS, many of the ICE medical devices have not been designed to satisfy cybersecurity requirements and, consequently, are vulnerable to recent attacks. Nowadays, ransomware attacks account for 85% of all malware in healthcare, and more than 70% of attacks confirmed data disclosure. With the goal of improving this situation, the main contribution of this paper is an automatic, intelligent and real-time system to detect, classify, and mitigate ransomware in ICE. The proposed solution is fully integrated with the ICE++ architecture, our previous work, and makes use of Machine Learning (ML) techniques to detect and classify the spreading phase of ransomware attacks affecting ICE. Additionally, Network Function Virtualization (NFV) and Software Defined Networking (SDN)paradigms are considered to mitigate the ransomware spreading by isolating and replacing infected devices. Different experiments returned a precision/recall of 92.32%/99.97% in anomaly detection, an accuracy of 99.99% in ransomware classification, and promising detection and mitigation times. Finally, different labelled ransomware datasets in ICE have been created and made publicly available. Full article
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Open AccessArticle
Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data
Sensors 2019, 19(4), 880; https://doi.org/10.3390/s19040880
Received: 29 November 2018 / Revised: 15 January 2019 / Accepted: 4 February 2019 / Published: 20 February 2019
Cited by 1 | PDF Full-text (1982 KB) | HTML Full-text | XML Full-text
Abstract
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, [...] Read more.
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative. Full article
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Open AccessArticle
Outage Performance Analysis and SWIPT Optimization in Energy-Harvesting Wireless Sensor Network Deploying NOMA
Sensors 2019, 19(3), 613; https://doi.org/10.3390/s19030613
Received: 17 December 2018 / Revised: 16 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
PDF Full-text (608 KB) | HTML Full-text | XML Full-text
Abstract
Thanks to the benefits of non-orthogonal multiple access (NOMA) in wireless communications, we evaluate a wireless sensor network deploying NOMA (WSN-NOMA), where the destination can receive two data symbols in a whole transmission process with two time slots. In this work, two relaying [...] Read more.
Thanks to the benefits of non-orthogonal multiple access (NOMA) in wireless communications, we evaluate a wireless sensor network deploying NOMA (WSN-NOMA), where the destination can receive two data symbols in a whole transmission process with two time slots. In this work, two relaying protocols, so-called time-switching-based relaying WSN-NOMA (TSR WSN-NOMA) and power-splitting-based relaying WSN-NOMA (PSR WSN-NOMA) are deployed to study energy-harvesting (EH). Regarding the system performance analysis, we obtain the closed-form expressions for the exact and approximate outage probability (OP) in both protocols, and the delay-limited throughput is also evaluated. We then compare the two protocols theoretically, and two optimization problems are formulated to reduce the impact of OP and optimize the data rate. Our numerical and simulation results are provided to prove the theoretical and analytical analysis. Thanks to these results, a great performance gain can be achieved for both TSR WSN-NOMA and PSR WSN-NOMA if optimal values of TS and PS ratios are found. In addition, the optimized TSR WSN-NOMA outperforms that of PSR WSN-NOMA in terms of OP. Full article
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Open AccessArticle
A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement
Sensors 2019, 19(3), 455; https://doi.org/10.3390/s19030455
Received: 30 November 2018 / Revised: 17 January 2019 / Accepted: 17 January 2019 / Published: 22 January 2019
Cited by 1 | PDF Full-text (2747 KB) | HTML Full-text | XML Full-text
Abstract
An office chair for analyzing the seated posture variation during the performance of a stress-level test is presented in this work. To meet this aim, we placed a set of textile pressure sensors both on the backrest and on the seat of the [...] Read more.
An office chair for analyzing the seated posture variation during the performance of a stress-level test is presented in this work. To meet this aim, we placed a set of textile pressure sensors both on the backrest and on the seat of the chair. The position of the sensors was selected for maximizing the detection of variations of user’s posture. The effectiveness of the designed system was evaluated through an experiment where increasing stress levels were obtained by administering a Stroop test. The collected results had been analyzed by considering three different time intervals based on the difficulty level of the test (low, medium, and high). A transition analysis conducted on postures assumed during the test showed that participants reached a different posture at the end of the test, when the cognitive engagement increased, with respect to the beginning. This evidence highlighted the presence of movement presumably due to the increased cognitive engagement. Overall, the performed analysis showed the proposed monitoring system could be used to identify body posture variations related to different levels of engagement of a seated user while performing cognitive tasks. Full article
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