Special Issue "Smart Healthcare"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computer Science and Electrical Engineering".

Deadline for manuscript submissions: 15 July 2017

Special Issue Editors

Guest Editor
Prof. Dr. Wenbing Zhao

Department of Electrical Engineering and Computer Science, College of Engineering, Cleveland State University, Cleveland, OH 44011, USA
Website | E-Mail
Interests: human computer interaction; rehabilitation; computer vision; distributed systems
Guest Editor
Dr. Xiong Luo

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Website | E-Mail
Interests: machine learning, artificial neural networks, fuzzy logic
Guest Editor
Dr. Tie Qiu

School of Software, Dalian University of Technology, Dalian 116620, China
Website | E-Mail
Interests: internet of things; embedded systems; wireless sensor networks

Special Issue Information

Dear Colleagues,

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professional through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this Special Issue, we welcome original research, as well as review articles in all areas of smart healthcare.

Dr. Wenbing Zhao
Dr. Xiong Luo
Dr. Tie Qiu
Guest Editors

Manuscript Submission Information

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Published Papers (8 papers)

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Research

Open AccessArticle IoT-Based Information System for Healthcare Application: Design Methodology Approach
Appl. Sci. 2017, 7(6), 596; doi:10.3390/app7060596
Received: 28 April 2017 / Revised: 30 May 2017 / Accepted: 3 June 2017 / Published: 8 June 2017
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Abstract
Over the last few decades, life expectancy has increased significantly. However, elderly people who live on their own often need assistance due to mobility difficulties, symptoms of dementia or other health problems. In such cases, an autonomous supporting system may be helpful. This
[...] Read more.
Over the last few decades, life expectancy has increased significantly. However, elderly people who live on their own often need assistance due to mobility difficulties, symptoms of dementia or other health problems. In such cases, an autonomous supporting system may be helpful. This paper proposes the Internet of Things (IoT)-based information system for indoor and outdoor use. Since the conducted survey of related works indicated a lack of methodological approaches to the design process, therefore a Design Methodology (DM), which approaches the design target from the perspective of the stakeholders, contracting authorities and potential users, is introduced. The implemented solution applies the three-axial accelerometer and magnetometer, Pedestrian Dead Reckoning (PDR), thresholding and the decision trees algorithm. Such an architecture enables the localization of a monitored person within four room-zones with accuracy; furthermore, it identifies falls and the activities of lying, standing, sitting and walking. Based on the identified activities, the system classifies current activities as normal, suspicious or dangerous, which is used to notify the healthcare staff about possible problems. The real-life scenarios validated the high robustness of the proposed solution. Moreover, the test results satisfied both stakeholders and future users and ensured further cooperation with the project. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks
Appl. Sci. 2017, 7(4), 412; doi:10.3390/app7040412
Received: 12 February 2017 / Revised: 13 April 2017 / Accepted: 14 April 2017 / Published: 19 April 2017
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Abstract
Proteomics research has become one of the most important topics in the field of life science and natural science. At present, research on protein–protein interaction networks (PPIN) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or
[...] Read more.
Proteomics research has become one of the most important topics in the field of life science and natural science. At present, research on protein–protein interaction networks (PPIN) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate detection mechanisms of functional modules in PPIN, including open database, existing detection algorithms, and recent solutions. After that, we describe the proposed approach based on the simplified swarm optimization (SSO) algorithm and the knowledge of Gene Ontology (GO). The proposed solution implements the SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species datasets for experiment: fruitfly, mouse, scere, and human. The testing and analysis result show that the proposed solution is feasible, efficient, and could achieve a higher accuracy of prediction than existing approaches. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System
Appl. Sci. 2017, 7(4), 366; doi:10.3390/app7040366
Received: 17 December 2016 / Revised: 29 March 2017 / Accepted: 4 April 2017 / Published: 6 April 2017
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Abstract
An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive
[...] Read more.
An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive size of the electromyography (EMG) signal impede the practical application of such medical information systems. Therefore, this research proposes an EMG uterine monitoring transmission solution (EUMTS), a lossless efficient real-time EMG transmission solution that solves such problems through efficient EMG data lossless compression. EMG data samples obtained from the Physionet PhysioBank database were used. Solution performance comparisons were conducted using Lempel-Ziv Welch (LZW) and Huffman methods, in addition to related researches. The LZW and Huffman methods showed CRs of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm. This was relatively high compared to related researches, even when considering that those researches were lossy whereas the proposed research was lossless. The results also showed that the proposed algorithm contributes to a reduction in battery consumption by reducing the wake-up time by 1470.6 ms. Therefore, EUMTS will contribute to providing an efficient wireless transmission environment for the prediction of pre-term delivery, enabling immediate interventions by medical professionals. Another novel point of EUMTS is that it is a lossless algorithm, which will prevent any misjudgement by clinicians because the data will not be distorted. Pre-term babies may receive point-of-care immediately after birth, preventing exposure to the development of disabilities. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
Appl. Sci. 2017, 7(3), 302; doi:10.3390/app7030302
Received: 30 January 2017 / Revised: 7 March 2017 / Accepted: 15 March 2017 / Published: 21 March 2017
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Abstract
In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes
[...] Read more.
In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle An IoT System for Remote Monitoring of Patients at Home
Appl. Sci. 2017, 7(3), 260; doi:10.3390/app7030260
Received: 18 December 2016 / Revised: 14 February 2017 / Accepted: 1 March 2017 / Published: 8 March 2017
Cited by 1 | PDF Full-text (5645 KB) | HTML Full-text | XML Full-text
Abstract
Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To make it operable
[...] Read more.
Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To make it operable in IoT environments, a protocol conversion scheme between ISO/IEEE 11073 protocol and oneM2M protocol, and a Multiclass Q-learning scheduling algorithm based on the urgency of biomedical data delivery to medical staff are proposed. In addition, for the sake of patients’ privacy, two security schemes are proposed—the separate storage scheme of data in parts and the Buddy-ACK authorization scheme. The experiment on the constructed system showed that the system worked well and the Multiclass Q-learning scheduling algorithm performs better than the Multiclass Based Dynamic Priority scheduling algorithm. We also found that the throughputs of the Multiclass Q-learning scheduling algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the Multiclass Based Dynamic Priority algorithm increase with decreases in the increasing ratio. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare
Appl. Sci. 2017, 7(3), 254; doi:10.3390/app7030254
Received: 31 December 2016 / Revised: 25 February 2017 / Accepted: 27 February 2017 / Published: 7 March 2017
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Abstract
With the development of science and technology, it is possible to analyze residents’ daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents’ anomalous behaviors and assess their physical condition, but these approaches
[...] Read more.
With the development of science and technology, it is possible to analyze residents’ daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents’ anomalous behaviors and assess their physical condition, but these approaches used by the researchers are often caught in plight caused by a lack of ground truth, one-sided analysis of behavior, and difficulty of understanding behaviors. In this paper, we put forward a smart home visual analysis system (SHVis) to help analysts detect and comprehend unusual behaviors of residents, and predict the health information intelligently. Firstly, the system classifies daily activities recorded by sensor devices in smart home environment into different categories, and discovers unusual behavior patterns of residents living in this environment by using various characteristics extracted from those activities and appropriate unsupervised anomaly detection algorithm. Secondly, on the basis of figuring out the residents’ anomaly degree of every date, we explore the daily behavior patterns and details with the help of several visualization views, and compare and analyze residents’ activities of various dates to find the reasons why residents act unusually. In the case study of this paper, we analyze residents’ behaviors that happened over two months and find unusual indoor behaviors and give health advice to the residents. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
Appl. Sci. 2017, 7(2), 192; doi:10.3390/app7020192
Received: 20 December 2016 / Accepted: 13 February 2017 / Published: 16 February 2017
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Abstract
To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classification accuracy, and greatly reduced the amount
[...] Read more.
To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classification accuracy, and greatly reduced the amount of computation to achieve information dimension compression and knowledge system simplification. However, before this reduction, data must be discretized, and this process causes some degree of information loss. Therefore, to maintain the integrity of the information, we used the improved FCM to make attributes fuzzy instead of discrete before continuing with attribute reduction, and thus, the implicit knowledge and decision rules were more accurate. Our algorithm overcame the defects of the traditional FCM algorithm, which is sensitive to outliers and easily falls into local optima. Our experimental results show that the proposed method improved recognition efficiency without degrading recognition accuracy, which was as high as 97.5%. Furthermore, the meibomian gland morphology was diagnosed efficiently, and thus this method can provide practical application values for the recognition of meibomian gland morphology. Full article
(This article belongs to the Special Issue Smart Healthcare)
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Open AccessArticle Design, Development and Implementation of a Smartphone Overdependence Management System for the Self-Control of Smart Devices
Appl. Sci. 2016, 6(12), 440; doi:10.3390/app6120440
Received: 27 November 2016 / Revised: 9 December 2016 / Accepted: 10 December 2016 / Published: 16 December 2016
Cited by 1 | PDF Full-text (2439 KB) | HTML Full-text | XML Full-text
Abstract
Background: Smartphone overdependence is a type of mental disorder that requires continuous treatment for cure and prevention. A smartphone overdependence management system that is based on scientific evidence is required. This study proposes the design, development and implementation of a smartphone overdependence management
[...] Read more.
Background: Smartphone overdependence is a type of mental disorder that requires continuous treatment for cure and prevention. A smartphone overdependence management system that is based on scientific evidence is required. This study proposes the design, development and implementation of a smartphone overdependence management system for self-control of smart devices. Methods: The system architecture of the Smartphone Overdependence Management System (SOMS) primarily consists of four sessions of mental monitoring: (1) Baseline settlement session; (2) Assessment session; (3) Sensing & monitoring session; and (4) Analysis and feedback session. We developed the smartphone-usage-monitoring application (app) and MindsCare personal computer (PC) app to receive and integrate usage data from smartphone users. We analyzed smartphone usage data using the Chi-square Automatic Interaction Detector (CHAID). Based on the baseline settlement results, we designed a feedback service to intervene. We implemented the system using 96 participants for testing and validation. The participants were classified into two groups: the smartphone usage control group (SUC) and the smartphone usage disorder addiction group (SUD). Results: The background smartphone monitoring app of the proposed system successfully monitored the smartphone usage based on the developed algorithm. The usage minutes of the SUD were higher than the usage minutes of the SUC in 11 of the 16 categories developed in our study. Via the MindsCare PC app, the data were successfully integrated and stored, and managers can successfully analyze and diagnose based on the monitored data. Conclusion: The SOMS is a new system that is based on integrated personalized data for evidence-based smartphone overdependence intervention. The SOMS is useful for managing usage data, diagnosing smartphone overdependence, classifying usage patterns and predicting smartphone overdependence. This system contributes to the diagnosis of an abstract mental status, such as smartphone overdependence, based on specific scientific indicators without reliance on consultation. Full article
(This article belongs to the Special Issue Smart Healthcare)
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