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Open AccessEditorial

Smart Healthcare

by Wenbing Zhao, Xiong Luo and Tie Qiu

Appl. Sci. 2017, 7(11), 1176; https://doi.org/10.3390/app7111176 - 15 Nov 2017

Cited by 26 | Viewed by 5729

Abstract

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies [...]
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(This article belongs to the Special Issue Smart Healthcare)

Research

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Open AccessArticle

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

by Shipra Suman, Fawnizu Azmadi Hussin, Aamir Saeed Malik, Shiaw Hooi Ho, Ida Hilmi, Alex Hwong-Ruey Leow and Khean-Lee Goh

Appl. Sci. 2017, 7(10), 1097; https://doi.org/10.3390/app7101097 - 24 Oct 2017

Cited by 30 | Viewed by 5625

Abstract

Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is [...] Read more.

Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Open AccessArticle

Secure Authentication and Prescription Safety Protocol for Telecare Health Services Using Ubiquitous IoT

by Zahid Mahmood, Huansheng Ning, Ata Ullah and Xuanxia Yao

Appl. Sci. 2017, 7(10), 1069; https://doi.org/10.3390/app7101069 - 16 Oct 2017

Cited by 33 | Viewed by 5726

Abstract

Internet-of-Things (IoT) include a large number of devices that can communicate across different networks. Cyber-Physical Systems (CPS) also includes a number of devices connected to the internet where wearable devices are also included. Both systems enable researchers to develop healthcare systems with additional [...] Read more.

Internet-of-Things (IoT) include a large number of devices that can communicate across different networks. Cyber-Physical Systems (CPS) also includes a number of devices connected to the internet where wearable devices are also included. Both systems enable researchers to develop healthcare systems with additional intelligence as well as prediction capabilities both for lifestyle and in hospitals. It offers as much persistence as a platform to ubiquitous healthcare by using wearable sensors to transfer the information over servers, smartphones, and other smart devices in the Telecare Medical Information System (TMIS). Security is a challenging issue in TMIS, and resourceful access to health care services requires user verification and confidentiality. Existing schemes lack in ensuring reliable prescription safety along with authentication. This research presents a Secure Authentication and Prescription Safety (SAPS) protocol to ensure secure communication between the patient, doctor/nurse, and the trusted server. The proposed procedure relies upon the efficient elliptic curve cryptosystem which can generate a symmetric secure key to ensure secure data exchange between patients and physicians after successful authentication of participants individually. A trusted server is involved for mutual authentication between parties and then generates a common key after completing the validation process. Moreover, the scheme is verified by doing formal modeling using Rubin Logic and validated using simulations in NS-2.35. We have analyzed the SAPS against security attacks, and then performance analysis is elucidated. Results prove the dominance of SAPS over preliminaries regarding mutual authentication, message integrity, freshness, and session key management and attack prevention. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Open AccessArticle

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

by Youjun Li, Jiajin Huang, Haiyan Zhou and Ning Zhong

Appl. Sci. 2017, 7(10), 1060; https://doi.org/10.3390/app7101060 - 13 Oct 2017

Cited by 144 | Viewed by 12075

Abstract

The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a [...] Read more.

The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this ﬁeld. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Open AccessArticle

A Hospital Recommendation System Based on Patient Satisfaction Survey

by Mohammad Reza Khoie, Tannaz Sattari Tabrizi, Elham Sahebkar Khorasani, Shahram Rahimi and Nina Marhamati

Appl. Sci. 2017, 7(10), 966; https://doi.org/10.3390/app7100966 - 21 Sep 2017

Cited by 22 | Viewed by 8732

Abstract

Surveys are used by hospitals to evaluate patient satisfaction and to improve general hospital operations. Collected satisfaction data is usually represented to the hospital administration by using statistical charts and graphs. Although such visualization is helpful, typically no deeper data analysis is performed [...] Read more.

Surveys are used by hospitals to evaluate patient satisfaction and to improve general hospital operations. Collected satisfaction data is usually represented to the hospital administration by using statistical charts and graphs. Although such visualization is helpful, typically no deeper data analysis is performed to identify important factors which contribute to patient satisfaction. This work presents an unsupervised data-driven methodology for analyzing patient satisfaction survey data. The goal of the proposed exploratory data analysis is to identify patient communities with similar satisfaction levels and the major factors, which contribute to their satisfaction. This type of data analysis will help hospitals to pinpoint the prevalence of certain satisfaction factors in specific patient communities or clusters of individuals and to implement more proactive measures to improve patient experience and care. To this end, two layers of data analysis is performed. In the first layer, patients are clustered based on their responses to the survey questions. Each cluster is then labeled according to its salient features. In the second layer, the clusters of first layer are divided into sub-clusters based on patient demographic data. Associations are derived between the salient features of each cluster and its sub-clusters. Such associations are ranked and validated by using standard statistical tests. The associations derived by this methodology are turned into comments and recommendations for healthcare providers and patients. Having applied this method on patient and survey data of a hospital resulted in 19 recommendations where 10 of them were statistically significant with chi-square test’s p-value less than 0.5 and an odds ratio z-test’s p-value of more than 2 or less than −2. These associations not only are statistically significant but seems rational too. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Reformulation-Linearization Technique Approach for Kidney Exchange Program IT Healthcare Platforms

by Junsang Yuh, Seokhyun Chung and Taesu Cheong

Appl. Sci. 2017, 7(8), 847; https://doi.org/10.3390/app7080847 - 17 Aug 2017

Cited by 4 | Viewed by 5415

Abstract

Kidney exchange allows a potential living donor whose kidney is incompatible with his intended recipient to donate a kidney to another patient so that the donor’s intended recipient can receive a compatible kidney from another donor. These exchanges can include cycles of longer [...] Read more.

Kidney exchange allows a potential living donor whose kidney is incompatible with his intended recipient to donate a kidney to another patient so that the donor’s intended recipient can receive a compatible kidney from another donor. These exchanges can include cycles of longer than two donor–patient pairs and chains produced by altruistic donors. Kidney exchange programs (KEPs) can be modeled as a maximum-weight cycle-packing problem in a directed graph. This paper develops a new integer programming model for KEPs by applying the reformulation-linearization technique (RLT) to enhance a lower bound obtained by its linear programming (LP) relaxation. Given the results obtained from the proposed model, the model is expected to be utilized in the integrated KEP IT (Information Technology) healthcare platform to obtain plans for optimized kidney exchanges. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Open AccessArticle

Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification

by Sicong Kuang and Brian D. Davison

Appl. Sci. 2017, 7(8), 846; https://doi.org/10.3390/app7080846 - 17 Aug 2017

Cited by 23 | Viewed by 4609

Abstract

Twitter is a popular source for the monitoring of healthcare information and public disease. However, there exists much noise in the tweets. Even though appropriate keywords appear in the tweets, they do not guarantee the identification of a truly health-related tweet. Thus, the [...] Read more.

Twitter is a popular source for the monitoring of healthcare information and public disease. However, there exists much noise in the tweets. Even though appropriate keywords appear in the tweets, they do not guarantee the identification of a truly health-related tweet. Thus, the traditional keyword-based classification task is largely ineffective. Algorithms for word embeddings have proved to be useful in many natural language processing (NLP) tasks. We introduce two algorithms based on an existing word embedding learning algorithm: the continuous bag-of-words model (CBOW). We apply the proposed algorithms to the task of recognizing healthcare-related tweets. In the CBOW model, the vector representation of words is learned from their contexts. To simplify the computation, the context is represented by an average of all words inside the context window. However, not all words in the context window contribute equally to the prediction of the target word. Greedily incorporating all the words in the context window will largely limit the contribution of the useful semantic words and bring noisy or irrelevant words into the learning process, while existing word embedding algorithms also try to learn a weighted CBOW model. Their weights are based on existing pre-defined syntactic rules while ignoring the task of the learned embedding. We propose learning weights based on the words’ relative importance in the classification task. Our intuition is that such learned weights place more emphasis on words that have comparatively more to contribute to the later task. We evaluate the embeddings learned from our algorithms on two healthcare-related datasets. The experimental results demonstrate that embeddings learned from the proposed algorithms outperform existing techniques by a relative accuracy improvement of over 9%. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Open AccessArticle

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

by Babagana Modu, Nereida Polovina, Yang Lan, Savas Konur, A. Taufiq Asyhari and Yonghong Peng

Appl. Sci. 2017, 7(8), 836; https://doi.org/10.3390/app7080836 - 17 Aug 2017

Cited by 39 | Viewed by 6925

Abstract

Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third [...] Read more.

Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria. Full article

(This article belongs to the Special Issue Smart Healthcare)

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An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems

by Yuhuai Peng, Xiaojie Wang, Lei Guo, Yichun Wang and Qingxu Deng

Appl. Sci. 2017, 7(8), 817; https://doi.org/10.3390/app7080817 - 10 Aug 2017

Cited by 37 | Viewed by 6917

Abstract

As a key technology in smart healthcare monitoring systems, wireless body area networks (WBANs) can pre-embed sensors and sinks on body surface or inside bodies for collecting different vital signs parameters, such as human Electrocardiograph (ECG), Electroencephalograph (EEG), Electromyogram (EMG), body temperature, blood [...] Read more.

As a key technology in smart healthcare monitoring systems, wireless body area networks (WBANs) can pre-embed sensors and sinks on body surface or inside bodies for collecting different vital signs parameters, such as human Electrocardiograph (ECG), Electroencephalograph (EEG), Electromyogram (EMG), body temperature, blood pressure, blood sugar, blood oxygen, etc. Using real-time online healthcare, patients can be tracked and monitored in normal or emergency conditions at their homes, hospital rooms, and in Intensive Care Units (ICUs). In particular, the reliability and effectiveness of the packets transmission will be directly related to the timely rescue of critically ill patients with life-threatening injuries. However, traditional fault-tolerant schemes either have the deficiency of underutilised resources or react too slowly to failures. In future healthcare systems, the medical Internet of Things (IoT) for real-time monitoring can integrate sensor networks, cloud computing, and big data techniques to address these problems. It can collect and send patient’s vital parameter signal and safety monitoring information to intelligent terminals and enhance transmission reliability and efficiency. Therefore, this paper presents a design in healthcare monitoring systems for a proactive reliable data transmission mechanism with resilience requirements in a many-to-one stream model. This Network Coding-based Fault-tolerant Mechanism (NCFM) first proposes a greedy grouping algorithm to divide the topology into small logical units; it then constructs a spanning tree based on random linear network coding to generate linearly independent coding combinations. Numerical results indicate that this transmission scheme works better than traditional methods in reducing the probability of packet loss, the resource redundant rate, and average delay, and can increase the effective throughput rate. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs

by Sheng Zhang, Xin Zhang, Hui Wang, Jiajun Cheng, Pei Li and Zhaoyun Ding

Appl. Sci. 2017, 7(8), 767; https://doi.org/10.3390/app7080767 - 28 Jul 2017

Cited by 64 | Viewed by 11339

Abstract

This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end [...] Read more.

This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end character-level multi-scale convolutional neural framework in which character embeddings instead of word embeddings are used to avoid Chinese word segmentation in text preprocessing, and multi-scale convolutional neural networks (CNNs) are then introduced to extract contextual information from either question or answer sentences over different scales. The proposed framework can be trained with minimal human supervision and does not require any handcrafted features, rule-based patterns, or external resources. To validate our framework, we create a new text corpus, named cMedQA, by harvesting questions and answers from an online Chinese health and wellness community. The experimental results on the cMedQA dataset show that our framework significantly outperforms several strong baselines, and achieves an improvement of top-1 accuracy by up to 19%. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Discrimination of Aortic and Pulmonary Components from the Second Heart Sound Using Respiratory Modulation and Measurement of Respiratory Split

by Hong Tang, Huaming Chen and Ting Li

Appl. Sci. 2017, 7(7), 690; https://doi.org/10.3390/app7070690 - 4 Jul 2017

Cited by 12 | Viewed by 5632

Abstract

The second heart sound consists of aortic and pulmonary components. Analysis on the changes of the second heart sound waveform in respiration shows that the aortic component has little variation and the delay of the pulmonary component is modulated by respiration. This paper [...] Read more.

The second heart sound consists of aortic and pulmonary components. Analysis on the changes of the second heart sound waveform in respiration shows that the aortic component has little variation and the delay of the pulmonary component is modulated by respiration. This paper proposes a novel model to discriminate the aortic and pulmonary components using respiratory modulation. It is found that the aortic component could be simply extracted by averaging the second heart sounds over respiratory phase, and the pulmonary component could be extracted by subtraction. Hence, the split is measured by the timing difference of the two components. To validate the measurement, the method is applied to simulated second heart sounds with known varying splits. The simulation results show that the aortic and pulmonary components can be successfully extracted and the measured splits are close to the predefined splits. The method is further evaluated by data collected from 12 healthy subjects. Experimental results show that the respiratory split can be accurately measured. The minimum split generally occurs at the end of expiration and the split value is about 20 ms. Meanwhile, the maximum split is about 50 ms at the end of inspiration. Both the trend of split varying with respect to respiratory phase and the numerical range of split varying are comparable to the results disclosed by previous physiologists. The proposed method is compared to the two previous well known methods. The most attractive advantage of the proposed method is much less complexity. This method has potential applications in monitoring heart hemodynamic response to respiration. Full article

(This article belongs to the Special Issue Smart Healthcare)

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Open AccessArticle

IoT-Based Information System for Healthcare Application: Design Methodology Approach

by Damian Dziak, Bartosz Jachimczyk and Wlodek J. Kulesza

Appl. Sci. 2017, 7(6), 596; https://doi.org/10.3390/app7060596 - 8 Jun 2017

Cited by 90 | Viewed by 12151

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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Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks

by Xianghan Zheng, Lingting Wu, Shaozhen Ye and Riqing Chen

Appl. Sci. 2017, 7(4), 412; https://doi.org/10.3390/app7040412 - 19 Apr 2017

Cited by 8 | Viewed by 6716

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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Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

by Gyoun-Yon Cho, Gyoun-Yon Lee and Tae-Ro Lee

Appl. Sci. 2017, 7(4), 366; https://doi.org/10.3390/app7040366 - 6 Apr 2017

Cited by 14 | Viewed by 4570

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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Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining

by Eric Rojas, Marcos Sepúlveda, Jorge Munoz-Gama, Daniel Capurro, Vicente Traver and Carlos Fernandez-Llatas

Appl. Sci. 2017, 7(3), 302; https://doi.org/10.3390/app7030302 - 21 Mar 2017

Cited by 58 | Viewed by 9743

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

by KeeHyun Park, Joonsuu Park and JongWhi Lee

Appl. Sci. 2017, 7(3), 260; https://doi.org/10.3390/app7030260 - 8 Mar 2017

Cited by 48 | Viewed by 8677

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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A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

by Zhifang Liao, Lingyuan Kong, Xiao Wang, Ying Zhao, Fangfang Zhou, Zhining Liao and Xiaoping Fan

Appl. Sci. 2017, 7(3), 254; https://doi.org/10.3390/app7030254 - 7 Mar 2017

Cited by 20 | Viewed by 5427

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

by Fengmei Liang, Yajun Xu, Weixin Li, Xiaoling Ning, Xueou Liu and Ajian Liu

Appl. Sci. 2017, 7(2), 192; https://doi.org/10.3390/app7020192 - 16 Feb 2017

Cited by 12 | Viewed by 4723

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

by Seo-Joon Lee, Mi Jung Rho, In Hye Yook, Seung-Ho Park, Kwang-Soo Jang, Bum-Joon Park, Ook Lee, Dong Kyun Lee, Dai-Jin Kim and In Young Choi

Appl. Sci. 2016, 6(12), 440; https://doi.org/10.3390/app6120440 - 16 Dec 2016

Cited by 19 | Viewed by 7821

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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Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

by Xiongyi Liu, Qing Wu, Wenbing Zhao and Xiong Luo

Appl. Sci. 2017, 7(10), 1051; https://doi.org/10.3390/app7101051 - 13 Oct 2017

Cited by 56 | Viewed by 9912

Abstract

The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of [...] Read more.

The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed? Full article

(This article belongs to the Special Issue Smart Healthcare)

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