Daehan Kwak

The Internet of Things for Health Care: A Comprehensive Survey

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare

Abstract The number of people with a chronic disease is rapidly increasing, giving the healthcare industry more challenging problems. To date, there exist several ontology and IoT-based healthcare systems to intelligently supervise the chronic patients for long-term care. The central purposes of these systems are to reduce the volume of manual work in recommendation systems. However, due to the increase of risk and uncertain factors of the diabetes patients, these healthcare systems cannot be utilized to extract precise physiological information about patient. Further, the existing ontology-based approaches cannot extract optimal membership value of risk factors; thus, it provides poor results. In this regards, this paper presents a type-2 fuzzy ontology–aided recommendation systems for IoT-based healthcare to efficiently monitor the patients body while recommending diets with specific foods and drugs. The proposed system extracts the values of patient risk factors, determines the patients health condition via wearable sensors, and then recommends diabetes-specific prescriptions for a smart medicine box and food for a smart refrigerator. The combination of type-2 Fuzzy Logic (T2FL) and the fuzzy ontology significantly increases the prediction accuracy of a patients condition and the precision rate for drug and food recommendations. Information about the patients disease history, foods consumed, and drugs prescribed is designed in the ontology to deliver decision-making knowledge using Protege Web Ontology Language (OWL)-2 tools. Semantic Web Rule Language (SWRL) rules and fuzzy logic are employed to automate the recommendation process. Moreover, Description Logic (DL) and Simple Protocol and RDF Query Language (SPARQL) queries are used to evaluate the ontology. The experimental results show that the proposed system is efficient for patient risk factors extraction and diabetes prescriptions.

Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots

The recent technology of human voice capture and interpretation has spawned the social robot to convey information and to provide recommendations. This technology helps people obtain information about a particular topic after giving an oral query to a humanoid robot. However, most of the search engines are keyword-matching mechanism-based, and the existing full-text query search engines are inadequate at retrieving relevant information from various oral queries. With only predefined words and sentence-based recommendations, a social robot may not suggest the correct items, if items retrieved along with the information are not predefined. In addition, the available conventional ontology-based systems cannot extract precise data from webpages to show the correct results. In this regard, we propose a merged ontology and support vector machine (SVM)-based information extraction and recommendation system. In the proposed system, when a humanoid robot receives an oral query from a disabled user, the oral query changes into a full-text query, the system mines the full-text query to extract the disabled user’s needs, and then converts the query into the correct format for a search engine. The proposed system downloads a collection of information about items (city features, diabetes drugs, and hotel features). The SVM identifies the relevant information on the item and removes anything irrelevant. Merged ontology-based sentiment analysis is then employed to find the polarity of the item for recommendation. The system suggests items with a positive polarity term to the disabled user. The intelligent model and merged ontology were designed by employing Java and Protégé Web Ontology Language 2 software, respectively. Experimentation results show that the proposed system is highly productive when analyzing retrieved information, and provides accurate recommendations.

DMTO: a realistic ontology for standard diabetes mellitus treatment

BackgroundTreatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge.ResultsThis paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients’ current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology’s BioPortal at http://bioportal.bioontology.org/ontologies/DMTO. The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs.ConclusionThe ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.

A Fuzzy Ontology and SVM–Based Web Content Classification System

The volume of adult content on the world wide web is increasing rapidly. This makes an automatic detection of adult content a more challenging task, when eliminating access to ill-suited websites. Most pornographic webpage–filtering systems are based on n-gram, naïve Bayes, K-nearest neighbor, and keyword-matching mechanisms, which do not provide perfect extraction of useful data from unstructured web content. These systems have no reasoning capability to intelligently filter web content to classify medical webpages from adult content webpages. In addition, it is easy for children to access pornographic webpages due to the freely available adult content on the Internet. It creates a problem for parents wishing to protect their children from such unsuitable content. To solve these problems, this paper presents a support vector machine (SVM) and fuzzy ontology–based semantic knowledge system to systematically filter web content and to identify and block access to pornography. The proposed system classifies URLs into adult URLs and medical URLs by using a blacklist of censored webpages to provide accuracy and speed. The proposed fuzzy ontology then extracts web content to find website type (adult content, normal, and medical) and block pornographic content. In order to examine the efficiency of the proposed system, fuzzy ontology, and intelligent tools are developed using Protégé 5.1 and Java, respectively. Experimental analysis shows that the performance of the proposed system is efficient for automatically detecting and blocking adult content.

Cyclic Prefixed Single Carrier Transmission in Intra-Vehicle Wireless Sensor Networked Control Systems

Intra-vehicle wireless sensor network, which is also called networked control system (NCS) is a promising new research area. NCS can not only provide part cost, assembly, maintenance savings, and fuel efficiency through the elimination of the wires, but also enable new sensor technologies to be integrated into vehicles. Ultra wideband (UWB) communication is a competitive candidate for the intra-vehicle NCS. In this paper, the performance of cyclic prefixed single carrier with frequency domain equalization (SC-FDE) transmission is investigated over intra-vehicle NCS propagation environment. The error-rate performance and the implementation complexity are compared among impulse based single carrier UWB (SC-UWB), multicarrier UWB (MC-UWB) employing orthogonal frequency-division-multiplexing (OFDM), and CP-SC under the same transmitting data rate conditions. Simulation results demonstrate conclusive performance advantage of the SC-FDE scheme on the communication of two different intra-vehicle NCS scenarios, especially when minimum mean square error method is taken into account.

Performance evaluation of intra-vehicle wireless sensor network systems

Intra-vehicle wireless sensor networks (IVWSNs) enable sensor technologies to be integrated into vehicles and provide part-cost, assembly, and maintenance savings, as well as fuel efficiency through the elimination of wires. Ultra-wide band (UWB) communication is considered a competitive candidate for IVWSNs. Extensive analysis has been done for indoor and outdoor propagation, but the analysis of IVWSNs environments has rarely been studied. In this paper, the error-rate performance of UWB propagation for these IVWSNs is investigated and compared under different transmission schemes. And we also study the effects of some parameters on communication performance, such as the number of rake fingers and direct sequence spread factor of an impulse radio system, the cyclic prefix size of a single carrier block transmission with frequency domain equalisation system, and the delay line time of a transmitted reference pulse cluster autocorrelation receiver. Study results offer valuable insight for the realistic IVWSNs.