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Sensor-based E-Healthcare System: Greenness and Security

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

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 63931

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Special Issue Editors

Prof. Dr. Lei Shu

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Guest Editor

1. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
2. School of Engineering, College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: Internet of Things; sensor networks; green computing; cloud and fog computing; fault diagnosis; wireless sensor networks; multimedia communication; middleware; security
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Joel J. P. C. Rodrigues

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1. Post-Graduation Program on Electrical Engineering, Federal University of Piauí (UFPI), Teresina, Brazil
2. Senior Researcher, Instituto de Telecomunicações, Covilhã, Portugal
Interests: vehicular communications; Internet of Things; eHealth technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, sensor-based E-Healthcare systems are attracting increasing attention from both academic and industrial communities, with a number of benefits (e.g., easy access to diagnostic information, reduction of duplicated calls to doctors, fewer delays in treatment) in terms of traditional healthcare systems. As various information and communication technologies are incorporated in sensor-based E-Healthcare systems, the greenness and security, along with the utilization of these technologies, should be considered. For example, data sensing and transmission technologies should enable energy-efficient collection and delivery of patient information, while data storage and access technologies should enable secure storage and access of patient information.

Thus, for operating sensor-based E-Healthcare systems energy-efficiently and securely, this Special Issue calls for original technical papers, which focus on the greenness and security of sensor-based E-Healthcare systems. Tutorials or survey papers will also be considered. In addition, selected high quality papers from HealthCom 2017 (http://healthcom2017.ieee-healthcom.org/) will be invited for further consideration in this Special Issue for publication. Potential topics include, but are not limited to:

Green architecture for sensor-based E-Healthcare system
Secure architecture for sensor-based E-Healthcare system
Green sensing for sensor-based E-Healthcare system
Secure sensing for sensor-based E-Healthcare system
Green communication for sensor-based E-Healthcare system
Secure communication for sensor-based E-Healthcare system
Green computing for sensor-based E-Healthcare system
Secure computing for sensor-based E-Healthcare system
Green data for sensor-based E-Healthcare system
Secure data for sensor-based E-Healthcare system
Green network for sensor-based E-Healthcare system
Secure network for sensor-based E-Healthcare system
Green middleware for sensor-based E-Healthcare system
Secure middleware for sensor-based E-Healthcare system

Prof. Dr. Lei Shu
Prof. Dr. Joel J.P.C. Rodrigues
Guest Editors

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Keywords

Sensor
E-Healthcare
Greenness
Security
Architecture
Sensing
Communication
Computing
Data
Network
Middleware

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Published Papers (11 papers)

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Research

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19 pages, 3852 KiB

Open AccessArticle

Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information

by Jialin Liu, Lei Wang, Jian Fang, Linlin Guo, Bingxian Lu and Lei Shu

Sensors 2018, 18(10), 3379; https://doi.org/10.3390/s18103379 - 10 Oct 2018

Cited by 23 | Viewed by 6022

Abstract

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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17 pages, 1733 KiB

Open AccessArticle

An Artificial Bee Colony-Based Green Routing Mechanism in WBANs for Sensor-Based E-Healthcare Systems

by Jian Yan, Yuhuai Peng, Dawei Shen, Xinxin Yan and Qingxu Deng

Sensors 2018, 18(10), 3268; https://doi.org/10.3390/s18103268 - 28 Sep 2018

Cited by 22 | Viewed by 2944

Abstract

At present, sensor-based E-Healthcare systems are attracting more and more attention from academia and industry. E-Healthcare systems are usually a Wireless Body Area Network (WBANs), which can monitor or diagnose human health by placing miniaturized, low-power sensor nodes in or on patient’s bodies to measure various physiological parameters. However, in this process, WBAN nodes usually use batteries, and especially for implantable flexible nodes, it is difficult to accomplish the battery replacement, so the energy that the node can carry is very limited, making the efficient use of energy the most important problem to consider when designing WBAN routing algorithms. By considering factors such as residual energy of node, the importance level of nodes, path cost and path energy difference ratios, this paper gives a definition of Optimal Path of Energy Consumption (OPEC) in WBANs, and designs the Optimal Energy Consumption routing based on Artificial Bee Colony (ABC) for WBANs (OEABC). A performance simulation is carried out to verify the effectiveness of the OEABC. Simulation results demonstrate that compared with the genetic algorithm and ant colony algorithm, the proposed OEABC has a better energy efficiency and faster convergence rate. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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16 pages, 4397 KiB

Open AccessArticle

Automatic Emotion Perception Using Eye Movement Information for E-Healthcare Systems

by Yang Wang, Zhao Lv and Yongjun Zheng

Sensors 2018, 18(9), 2826; https://doi.org/10.3390/s18092826 - 27 Aug 2018

Cited by 23 | Viewed by 4494

Abstract

Facing the adolescents and detecting their emotional state is vital for promoting rehabilitation therapy within an E-Healthcare system. Focusing on a novel approach for a sensor-based E-Healthcare system, we propose an eye movement information-based emotion perception algorithm by collecting and analyzing electrooculography (EOG) signals and eye movement video synchronously. Specifically, we extract the time-frequency eye movement features by firstly applying the short-time Fourier transform (STFT) to raw multi-channel EOG signals. Subsequently, in order to integrate time domain eye movement features (i.e., saccade duration, fixation duration, and pupil diameter), we investigate two feature fusion strategies: feature level fusion (FLF) and decision level fusion (DLF). Recognition experiments have been also performed according to three emotional states: positive, neutral, and negative. The average accuracies are 88.64% (the FLF method) and 88.35% (the DLF with maximal rule method), respectively. Experimental results reveal that eye movement information can effectively reflect the emotional state of the adolescences, which provides a promising tool to improve the performance of the E-Healthcare system. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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21 pages, 708 KiB

Open AccessFeature PaperArticle

Green Communication for Tracking Heart Rate with Smartbands

by Franks González-Landero, Iván García-Magariño, Raquel Lacuesta and Jaime Lloret

Sensors 2018, 18(8), 2652; https://doi.org/10.3390/s18082652 - 13 Aug 2018

Cited by 12 | Viewed by 4238

Abstract

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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28 pages, 12790 KiB

Open AccessArticle

Systemic Design Approach to a Real-Time Healthcare Monitoring System: Reducing Unplanned Hospital Readmissions ^†

by Faisal Alkhaldi and Ali Alouani

Sensors 2018, 18(8), 2531; https://doi.org/10.3390/s18082531 - 2 Aug 2018

Cited by 9 | Viewed by 3831

Abstract

Following hospital discharge, millions of patients continue to recover outside formal healthcare organizations (HCOs) in designated transitional care periods (TCPs). Unplanned hospital readmissions of patients during TCPs adversely affects the quality and cost of care. In order to reduce the rates of unplanned hospital readmissions, we propose a real-time patient-centric system, built around applications, to assist discharged patients in remaining at home or in the workplace while being supported by care providers. Discrete-event system modeling techniques and supervisory control theory play fundamental roles in the system’s design. Simulation results and analysis show that the proposed system can be effective in documenting a patient’s condition and health-related behaviors. Most importantly, the system tackles the problem of unplanned hospital readmissions by supporting discharged patients at a lower cost via home/workplace monitoring without sacrificing the quality of care. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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16 pages, 921 KiB

Open AccessArticle

Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

by Yancheng Shi, Zhenjiang Zhang, Han-Chieh Chao and Bo Shen

Sensors 2018, 18(7), 2307; https://doi.org/10.3390/s18072307 - 16 Jul 2018

Cited by 15 | Viewed by 3670

Abstract

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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2391 KiB

Open AccessArticle

Mixed H₂/H_∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

by Chao Li, Zhenjiang Zhang and Han-Chieh Chao

Sensors 2018, 18(1), 56; https://doi.org/10.3390/s18010056 - 27 Dec 2017

Cited by 1 | Viewed by 3273

Abstract

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H₂/H_∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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7855 KiB

Open AccessArticle

A Component-Based Approach for Securing Indoor Home Care Applications

by Aitor Agirre, Aintzane Armentia, Elisabet Estévez and Marga Marcos

Sensors 2018, 18(1), 46; https://doi.org/10.3390/s18010046 - 26 Dec 2017

Cited by 7 | Viewed by 4865

Abstract

eHealth systems have adopted recent advances on sensing technologies together with advances in information and communication technologies (ICT) in order to provide people-centered services that improve the quality of life of an increasingly elderly population. As these eHealth services are founded on the acquisition and processing of sensitive data (e.g., personal details, diagnosis, treatments and medical history), any security threat would damage the public’s confidence in them. This paper proposes a solution for the design and runtime management of indoor eHealth applications with security requirements. The proposal allows applications definition customized to patient particularities, including the early detection of health deterioration and suitable reaction (events) as well as security needs. At runtime, security support is twofold. A secured component-based platform supervises applications execution and provides events management, whilst the security of the communications among application components is also guaranteed. Additionally, the proposed event management scheme adopts the fog computing paradigm to enable local event related data storage and processing, thus saving communication bandwidth when communicating with the cloud. As a proof of concept, this proposal has been validated through the monitoring of the health status in diabetic patients at a nursing home. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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1627 KiB

Open AccessArticle

A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres

by Yungang Zhu, Dayou Liu, Radu Grosu, Xinhua Wang, Hongying Duan and Guodong Wang

Sensors 2017, 17(9), 2049; https://doi.org/10.3390/s17092049 - 7 Sep 2017

Cited by 9 | Viewed by 4298

Abstract

Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates “hyperspheres” to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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Review

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26 pages, 897 KiB

Open AccessReview

Advances in Photopletysmography Signal Analysis for Biomedical Applications

by Jermana L. Moraes, Matheus X. Rocha, Glauber G. Vasconcelos, José E. Vasconcelos Filho, Victor Hugo C. De Albuquerque and Auzuir R. Alexandria

Sensors 2018, 18(6), 1894; https://doi.org/10.3390/s18061894 - 9 Jun 2018

Cited by 213 | Viewed by 14384

Abstract

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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1425 KiB

Open AccessReview

Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare

by Karen Avila, Paul Sanmartin, Daladier Jabba and Miguel Jimeno

Sensors 2017, 17(8), 1703; https://doi.org/10.3390/s17081703 - 25 Jul 2017

Cited by 50 | Viewed by 9942

Abstract

This article makes a literature review of applications developed in the health industry which are focused on patient care from home and implement a service-oriented (SOA) design in architecture. Throughout this work, the applicability of the concept of Internet of Things (IoT) in the field of telemedicine and health care in general is evaluated. It also performs an introduction to the concept of SOA and its main features, making a small emphasis on safety aspects. As a central theme, the description of different solutions that can be found in the health industry is developed, especially those whose goal is health care at home; the main component of these solutions are body sensor networks. Finally, an analysis of the literature from the perspectives of functionalities, security implementation and semantic interoperability is made to have a better understanding of what has been done and which are probable research paths to be studied in the future. Full article

(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)

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