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

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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) | Viewed by 35679

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Special Issue Editors

Prof. Norbert Noury

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Guest Editor

University Claude Bernard Lyon 1, Lyon, France

Dr. Martin Černý

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Guest Editor

Biomedical Engineering Group, Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB –Technical University of Ostrava,17. listopadu 15, 708 00 Ostrava, Czech Republic
Interests: contactless vital signs monitoring; assistive technologies; telemedicine; fuzzy logic
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Special Issue Information

Dear Colleagues,

The 20^th 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

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Published Papers (5 papers)

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Research

28 pages, 12035 KiB

Open AccessArticle

Using the IBM SPSS SW Tool with Wavelet Transformation for CO₂ Prediction within IoT in Smart Home Care

by Jan Vanus, Jan Kubicek, Ojan M. Gorjani and Jiri Koziorek

Sensors 2019, 19(6), 1407; https://doi.org/10.3390/s19061407 - 21 Mar 2019

Cited by 22 | Viewed by 6926

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 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

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

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31 pages, 412 KiB

Open AccessArticle

Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments

by Lorenzo Fernández Maimó, Alberto Huertas Celdrán, Ángel L. Perales Gómez, Félix J. García Clemente, James Weimer and Insup Lee

Sensors 2019, 19(5), 1114; https://doi.org/10.3390/s19051114 - 5 Mar 2019

Cited by 53 | Viewed by 9073

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 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

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

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19 pages, 1982 KiB

Open AccessArticle

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

by Aimilia Papagiannaki, Evangelia I. Zacharaki, Gerasimos Kalouris, Spyridon Kalogiannis, Konstantinos Deltouzos, John Ellul and Vasileios Megalooikonomou

Sensors 2019, 19(4), 880; https://doi.org/10.3390/s19040880 - 20 Feb 2019

Cited by 31 | Viewed by 5143

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, 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

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

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21 pages, 608 KiB

Open AccessArticle

Outage Performance Analysis and SWIPT Optimization in Energy-Harvesting Wireless Sensor Network Deploying NOMA

by Hoang-Sy Nguyen, Tran Thai Hoc Ly, Thanh-Sang Nguyen, Van Van Huynh, Thanh-Long Nguyen and Miroslav Voznak

Sensors 2019, 19(3), 613; https://doi.org/10.3390/s19030613 - 1 Feb 2019

Cited by 16 | Viewed by 4571

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 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

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

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15 pages, 2747 KiB

Open AccessArticle

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement

by Daniele Bibbo, Marco Carli, Silvia Conforto and Federica Battisti

Sensors 2019, 19(3), 455; https://doi.org/10.3390/s19030455 - 22 Jan 2019

Cited by 30 | Viewed by 9420

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 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

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

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