Ontology of Smart Homes and Elderly Self Care Home - Essay Example

?Table of Contents TABLE OF FIGURES 2 2 Chapter 2 1 Introduction 2 2 project aims and objectives 3 2 The aim of the project 3 2.2 Project objectives 4 1.2.3 Academic objectives 4 1.2.4 Personal objectives 4 chapter 2 5 CHAPTERS preview 5 Chapter 1 5 Chapter 2 5 Chapter 3 5 Chapter 4 5 Chapter 5 5 Chapter 6 5 Chapter 7 5 Chapter 8 6 Chapter 9 6 Chapter 10 6 Chapter 3 6 3.1 Project environment 6 3.2 Project problem domain 8 Chapter 4 9 Literature review 9 Chapter 5 12 Justification of the literature review method 12 Chapter 6 13 6.1 Ontology 13 6.2 Protege-OWL 13 Chapter 7 Ontology implementation 14 7.1 Ontology Taxonomy 14 7.2 Ontology reasoning 15 7.3 Ontology value partition class 16 7.4 Building ontology 16 Chapter 8 Evaluation 17 8.1 project approach evaluation 18 8.2 project objectives evaluation 18 8.3 Academic objectives evaluation 19 8.4 Personal objectives evaluation 20 Chapter 9 21 OWL ontology and UML 21 Chapter 10 22 10.1 conclusion 22 10.2 Recommendation 23 Appendix 1 - Taxonomy 23 Appendix 2 - prototype Ontology 23 Glossary 25 TABLE OF FIGURES Figure 1.1: A snapshot of ontology classes. ABSTRACT Pervasive environment is the emerging computing paradigm that aims to provide users access to services all the time, everywhere, in a transparent way, by means of devices installed in the surrounding physical environment or worn by the user. Smart environment represents the evolutionary development of pervasive environment. Web ontology language has become a promising technology to capture the knowledge of smart environment and information about its inhabitants. In this project, an ontological framework (taxonomy) of a smart home for the elderly has been developed. Protege-OWL 4.1 was used to build OWL representation of the elderly smart home. The presented ontology implements reasoning set on the rules of the ontology and elimination of the rules outside of the ontology. Chapter 1 1.1 Introduction In recent years, rapid advances in technology have paved the way for the creation of pervasive environments. A pervasive environment [1] is a user-centric environment in which there are a number of devices (sensors, computers) and services (such as Web services) that help users to achieve their various application goals. Smart environments [2] represent the evolutionary development of pervasive environments. Smart environment [2] is able to acquire knowledge about its inhabitants and their surroundings, and adapt to the inhabitants’ behaviour or preferences in order to improve their living experiences. The type of inhabitants’ experiences may vary from the safety of the users to the progress of an independent living environment. An important characteristic of smart environments is the interaction of smart devices including sensors and computer systems used for acquiring inhabitants’ contexts such as locations, activities, or vital signs. Therefore, context awareness [7] is a key issue for improving inhabitants’ independent living experience. Context awareness is about the situation an inhabitant is in and also about adapting the smart devices’ reaction to that situation. Ontology language [3] is the context representation technology which maximises the context awareness of the smart devices. It is a set of vocabulary to describe the conceptualisation of a particular domain. One of the goals of this project is to build a context-aware [7] ontology based on the acquired context from a smart environment and the inhabitants. The context in this project consists of location, time, and information on inhabitant’s vital signs. The ontology represents concepts and relations for the acquired context. The aim is to represent all the acquired information in context [7] form to reduce the dependency of the framework on rules outside of the ontology. 1.2 project aims and objectives 1.2.1 The aim of the project The essence of pervasive healthcare is in the creation of smart environments integrated with their respective inhabitants. A characteristic of such an intelligent pervasive environment lies in the autonomous interaction of smart devices used for important contexts such as vital signs. In this sense, context awareness is a key issue for enhancing users’ living experience during their daily interaction with smart devices like sensors and computer systems. The aim of this project is to build a context-aware ontology for an elderly care home. The context-aware [7] ontology provides information to the inhabitants and the caretakers by exploiting context. By context [7], it means information about the care home (e.g., temperature), the inhabitants (e.g., vital signs), and the devices. The ontology can provide a uniform way for specifying the concepts and consequently enable contextual knowledge sharing in pervasive systems. The second aim of the project is to make the context-aware ontology less dependent on additional rules outside of the ontology. 1.2.2 Project objectives Objective 1 : to build an ontology in order to reflect the context of a typical pervasive environment Objective 2: to minimise dependency on rules outside of the ontology Objective 3: to define restrictions and constraints in the ontology and where the rules are needed to be applied 1.2.3 Academic objectives Objective 1: to learn what pervasive environment is Objective 2: to learn the OWL ontology language Objective 3: to learn how to make full use of OWL potential capabilities 1.2.4 Personal objectives Objective 1: to gain new technological skills Objective2: to be able to add some values to projects that have already been done in this field Objective 3: to improve one’s employability in the technology field chapter 2 CHAPTERS preview Chapter 1 This chapter is the introduction of the project, which discusses pervasive environment, smart environment. It also includes the aim and objective of this endeavour in both academic and personal aspects. Chapter 2 This chapter will preview the different topics of each chapter. Chapter 3 This chapter will explain project environment and project problem domain extensively. Chapter 4 This chapter will review the literature review of previous related researches and projects. Chapter 5 This chapter will discuss the justification of the literature review method. Chapter 6 This chapter will discuss Ontology and Protege-OWL as the editor of ontology. Chapter 7 This chapter will explain the ontology implementation (taxonomy, reasoning, value partition class, and building ontology) for the self-care elderly home. Chapter 8 This chapter will evaluate the project approach, project objectives, personal objectives, and academic objectives. Chapter 9 This chapter will compare OWL Ontology and UML (Unified Modeling Language). Chapter 10 This chapter will conclude the project and suggest some recommendations. Chapter 3 3.1 Project environment The world is facing a major shift in its demographic composition, and this poses a potential threat to the economy of the existing healthcare systems. This change is a result of many factors all related to the increased wealth and social security. This increased wealth has led to major advances in sanitation and other engineering fields related to public health. Increasing the number of potential active work force or more work hours for caretakers might be solutions for a better service to the people in need. However, the other path suggested is through the use of technology in order to increase the efficiency of limited resources. The new paradigm in technology has been named pervasive environment [1] and for the healthcare, in particular, is named pervasive healthcare [6]. The concept of pervasive environment (previously known as ubiquitous computing) was introduced by Mark Weiser in 1990. Pervasive environment is the method of improving computer use by making computers available throughout the physical environment [16]. Pervasive environment is the emerging computing paradigm that aims to provide users with access to services all the time, everywhere, in a transparent way, by means of devices embedded in the surrounding physical environment or carried by the user [7]. Pervasive environment is also about intelligent decision making, access to data, and contextual awareness [7]. Context-awareness is concerned with the situation a user is in. Examples of context include user activity and vital signs. The goal in this project is to provide the system with information about the current context and with the predictions of future context in order to make the system richer in terms of independency on human in decision making. An important step for pervasive environment is the integration of intelligent agents employing knowledge and reasoning to understand the local context and share this information in support of intelligent applications and interfaces. Pervasive healthcare [6] is an advancing discipline that applies pervasive environment features to applications deployed in the healthcare domain. Pervasive healthcare [6] or smart healthcare environment [22] offers opportunities for future healthcare provision. Smart healthcare environment is able to acquire and apply knowledge about its inhabitants and their surroundings. For instance, remote sensors and monitoring technology might allow the continuous capture and analysis of patients’ physiological data. Improved methods for monitoring health and well-being could allow people to live independently in their own homes or live less dependent in care homes. Sensors embedded throughout the home could detect movement and fluctuations within the ambient environment (such as temperature change) to alert caretakers or the patient of any irregularities. 3.2 Project problem domain Applications in the advancing area of pervasive healthcare [6] employ the features of pervasive environment to advance technology in the healthcare sector. Like many industries, healthcare has recognised the advantages to be gained by the use of technology. Globally, technology has reengineered the healthcare industry resulting in increased productivity, reduced human error, and increased interoperability among various healthcare areas and facilities. The term pervasive healthcare [6] represents two aims: firstly to enable access to healthcare information anytime, anywhere, and secondly to apply pervasive environment technology in order to create intelligent applications that can apply these benefits as needed (e.g., dynamically adapting to their environment). In the smart healthcare environment, ontology is being used to propose context models for healthcare monitoring and home care. These models are based on predefined protocols to collect medical data which are used to automatically monitor the patient’s status and to detect possible alarm situations [33]. In this project, the elderly self-care home is a house occupied by a number of elderly, where they are monitored 24 hours. Each inhabitant has his/her own private living space with an on-suite bathroom. The inhabitants of the house have different health conditions. Some may be susceptible to high or low blood pressure or high or low heart rate. The house is equipped with electrical equipment such as sensors, which are either worn by the inhabitants or fixed in the environment. Information (context) regarding inhabitants’ vital signs is gained through sensors and passed to the application system for analysis. The system reacts to abnormality in the inhabitants’ vital signs (blood pressure and heart rate) in one of the following ways: 1. Issue an alarm for the medical caretakers; 2. Ask the inhabitant to do something. Chapter 4 Literature review This project is related to the research work, Experiences of Building Assisted Self Care Systems Within Smart Home Environment, by Reza Shojanoori, Radmila Juric, and Babak Tourani of the Department of Information Systems and Computing at the University of Westminster on the use of ontology and inferring mechanisms in new types of pervasive environments. Some of the related works are the following: To master the complexity in healthcare management system, this project has designed an ontology-based telemedicine task management system architecture and an ontology to represent the concepts such as actor, resource, service and medical data, and the interrelations among these concepts. The purpose of the ontology is to support the management of the message exchanges between the different actors of the telemedicine system (healthcare professionals, patients, software agents, intelligent devices). In other words, this ontology-based system shall offer solutions that are controlled according to a set of rules applied on or inferred from the knowledge represented by the ontology. Furthermore, using ontology will ease the process of analysing knowledge by creating specific rules and profiles. The main role of the ontology is to capture knowledge about the resources and actors in the telemedicine domain, by describing the different concepts and their interrelations used by the tasks [33]. This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language (OWL), is a collection of ontologies for describing places, agents, events, and their associated properties in an intelligent meeting room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning, and privacy protection supports for pervasive context-aware systems [7]. This paper presents a taxonomy of pervasive healthcare systems. The taxonomy identifies a set of fundamental properties that enable a system to be described according to user characteristics, its purpose and environment of use, as well as the technologies used. These properties define the relationships among all main feature categories. In addition, the taxonomy is based on the International Classification of Functioning, Disability and Health (ICF), which provides a standard language and a framework for the description of health and disability. The ICF aids the description of the changes in body functions and structures—what people can do in their usual environment. The ICF contains a list of body functions and structures, domains of participation, as well as environmental factors that interact with all of these components [6]. This paper introduces a system architecture to provide situation-aware Activity of Daily Living (ADL) assistances in a smart home environment. The system makes use of ontology (semantic technology) for sensor data modelling, fusion, and management, thus creating machine understandable and processable situational data. It exploits intelligent agents for interpreting and reasoning semantic situational data to enhance situation-aware decision support [28]. Wang et al. (2004) present CONON, a context ontology that is based on OWL for reasoning and representation of contexts in pervasive environments. Their approach is based on the treatment of high-level implicit contexts that are derived from low-level explicit contexts [4]. Y Yan et al. (2006) use Protege-2000 to build OWL representations of the contract ontology and make some analyses [26]. In this paper a framework for semantic indexing and detecting of events in smart spaces has been presented. The proposed framework aims to bridge the gap between what to get from sensors and what the user hopes to know about the surroundings and internal objects in a smart space. The framework mainly focuses on processing of semantic events, which is human-understandable and easy to specify for users to use keywords in. Smart Space Event Ontology (SSEO) is developed to enable semantic indexing and detecting of machine-processable events and exchanging event data between different processes. A model named Smart Space Event Processors (SSEP) maintains and coordinates various event processes (e.g., event patterns, ontology reasoning rules, and machine-learning logarithms) for semantic events in a smart space [14]. An ontological approach for context modelling so as to enrich the proposed (subject, predicate, value, time, certainty) representation of context data by using of the Semantic Web Rule Language (SWRL) [27], a tool that exists as a plug-in service in the OWL development environment in Protege (2006) [4]. In contrast to this work, this project tries to reduce the dependency of ontology on SWRL or any other outside rules. Chapter 5 Justification of the literature review method The method adopted in this project research is related to existing research papers (literature review), mainly the research paper of Shojanoori et al. (2010) on Experiences of Building Assisted Self Care Systems Within Smart Home Environment. As this endeavour is a research project, literature review would make a platform to build the argument on, and it places the argument within the discipline. Literature review demonstrates that there is a thorough understanding of the pervasive environment and the use of ontology language in this environment. Chapter 6 6.1 Ontology There are different definitions of the word ontology, and some definitions have been represented as the following: “ An ontology is a hierarchy structures set of terms for describing a domain that can be used as a foundation for a knowledge base” (Swartout et al.,1997) “An ontology may take a variety of forms, but it will necessarily include a vocabulary of terms and specifications of their meanings. This includes definitions and how the concepts are interrelated which impose a structure on the domain and constrain the interpretations of terms.” (Uschold & Jasper, 1999) The ontology can help to address some key issues of pervasive environments such as knowledge representation, semantic interoperability, and service discovery [34]. Ontology is a key requirement for building context-aware systems for the following reasons: (i) a common ontology enables knowledge sharing in an open and dynamic distributed system, (ii) ontology with well-defined declarative semantics provides reasoning about contextual information, and (iii) explicitly represented ontology allows devices and users not expressly designed to interoperate, achieve interoperability [7]. 6.2 Protege-OWL Protege-OWL [23] is an extension of Protege that supports Web ontology language. It is the most recent development in ontology languages supported by the World Wide Web Consortium (W3C) [25]. Protege-OWL’s flexible architecture has an open source Java API for the development. Chen et al. (2003) present Context Broker Architecture (CoBrA) to support context-aware systems in smart space. They use their COBRA-ONT ontology for representations and OWL for describing places and events. The following are reasons for choosing the OWL ontology: OWL is the W3C recommendation for the Semantic Web language. It is designed based on the formal foundation of Description Logics [29]. OWL not only allows formally describing the meaning of terminology used in documents but also permits machine inference and reasoning upon presented facts. Reasoning is used to support ontology design and to improve the quality of the resulting ontology. OWL supports text-based data representation and the information representation constructs in OWL support object-oriented descriptions. OWL supports the definition of classes, individual instances, and property relationships among classes, individuals, and properties [29]. Protege-OWL [25] is a free, open source ontology editor and knowledge-base framework developed by Stanford Medical Informatics of Stanford University. OWL facilitates machine interpretability of Web content by providing additional vocabulary along with a formal semantics such as classes (stated to be disjoint from each other), cardinalities, and properties. Chapter 7 Ontology implementation 7.1 Ontology Taxonomy Taxonomy provides hierarchical classification of data structure. The taxonomy [6] is intended to provide common language for computer developers and the users for describing pervasive systems. In addition to providing a basis for describing and classifying pervasive systems, the taxonomy will lead to a better understanding of the connection between user needs and system features. The proposed taxonomy [Appendix1] is based on the scenario of the elderly home. The taxonomy identifies varieties of classes and their individuals, subclasses, and properties. All the classes have been created according to the requirements of a smart care home. 7.2 Ontology reasoning Reasoning rules [27] play a primary role in the process of reasoning to retrieve useful knowledge on the Semantic Web. By reasoning over instances of an ontology, it is possible to, for example, derive a certain value for an attribute applied to an object. These inference services are the equivalent of SQL query engines in databases, but they provide more “intelligent” support such as handling recursive rules. On the other hand, reasoning over concepts of an ontology makes it possible to automatically derive the correct hierarchical location of a new concept or to detect inconsistencies [32]. The reasoner can be queried for information about the ontology. The Protege-OWL “Pellet” Reasoner, used in this project, contains several methods to obtain inferred information about classes and individuals. For example, there are methods to get the inferred superclasses and subclasses of a given class. 7.3 Ontology value partition class The reason of value partitioning [25] is that in some circumstances, some modified values, such as different sizes and severity of something, need to be presented. Therefore, in this project, some classes are created as subclasses of a class called “value partition.” 7.4 Building ontology Building an ontology and customising it to meet the requirements of the elderly home can be an arduous but achievable task. In this project, a prototype ontology is developed in order to describe the semantics of the concepts of the elderly self-care home scenario. The basic goal of this ontology is to provide context-related information for applications. The ontology also is built based on the rules of the ontology, and all the rules outside of the ontology have been ignored. The ontology editor tool that is the most suitable for this purpose was Protege-OWL 4.1. A criterion for this selection was the fact that it is an open source application. Furthermore, the abilities of Protege-OWL regarding the knowledge representation and the inference mechanism were factors that led to the selection of Protege-OWL [34]. The Protege-OWL can support the description of both the basic concepts and the taxonomy that are described in the elderly self-care home. Figure 1 shows a complete list of classes of the prototype ontology. Figure1 The prototype should be able to infer the following results from defined conditions: If the person is in his/her bedroom, is asleep, has hypertension, and his/her Glasgow Coma Scale (GCS) is critical, then the alarm goes on to notify the caretaker. If the person has hypertension and tachycardia, and his/her GCS is cautious, then the person is called patient. Chapter 8 Evaluation 8.1 project approach evaluation The purpose of this evaluation is to assess if the set up objectives including project objectives, personal objectives, and academic objectives have been achieved. For this purpose and in order to get the best result of the project, it was planned to use journals and academic papers, the “building OWL ontologies tutorial” of the University of Manchester, and supervisor meetings. Journal and academic papers were available online in the library of the University of Westminster. However, the online access to some relevant papers was restricted and needed payment. In addition, only a few papers would discuss on how to build an ontology. On the other hand, “building OWL ontologies tutorial” of the University of Manchester, were not enough to tackle the problems that occurred during the development of the ontology. However, the project supervisor Mr. Shojanoori made helpful comments and explanations in every meeting to make things clearer. 8.2 project objectives evaluation Objective 1: to build an ontology in order to reflect the context of a typical pervasive environment The intention of this project was to build an ontology of a smart elderly care home. Progress to date Building ontology started on February 2011 when studying the “building OWL ontologies tutorial” of the University of Manchester was finished. To date the prototype ontology is build based on the requirements of the self-care home. Objective 2: to minimise dependency on rules outside of the ontology The purpose of this objective was to make the ontology more consistent and represent all the acquired information in the self-care home in context format to make it more understandable of the application systems. Progress to date Academic papers on ontology rules and the rules outside of the ontology were not adequate. To date, the prototype self-care home ontology is only based on ontology rules. Objective 3: to define restrictions and constraints in the ontology and where the rules are needed to be applied This objective would be met by applying built-in restrictions in ontology in different ways to get the best result. Progress to date To date, all possible constraints on properties in ontology have been understood and applied. 8.3 Academic objectives evaluation Objective 1: to learn what pervasive environment is The purpose of this objective was to fully comprehend what a pervasive environment is in order to understand what a smart home is. Progress to date Many academic articles on pervasive environment have been studied and a precise background on pervasive environment has been presented on this paper. Objective 2: to learn the OWL ontology language The intention of this objective was to learn how to build an ontology. Progress to date The prototype ontology for elderly self-care home is built in Protege-OWL Objective 3: to learn how to make full use of OWL potential capabilities This objective would be met by practicing on pizza ontology (OWL ontology tutorial of the University of Manchester). Progress to date To date, all capabilities of OWL have been used to build the prototype ontology. 8.4 Personal objectives evaluation Objective 1: to gain new technological skills The intention of this objective was to learn new technological paradigm in computer system applications. Progress to date Computing is moving towards pervasiveness. An important step for computer experts is to be able to develop new architectures and applications to support pervasive environment systems. To date, the first step for new skills towards the new paradigm has been taken. Objective 2: to be able to add some values to projects that have already been done in this field The purpose of this objective was to share the knowledge and get new knowledge. Progress to date This project, if it has not added value to the previous ones, has increased the knowledge sharing between the project supervisor and me. Objective 3: to improve one’s employability in the technology field This objective would be met by finalising the project. Progress to date Pervasive environment is an emerging computing paradigm that needs new skills to be applied in. Many system developers are still not aware of pervasive environment and ontology, which will give me an advantage for employment over many developers. Chapter 9 OWL ontology and UML Building an ontology includes the task of defining basic concepts and structures (classes, properties, and individuals) which can correspond to classes, associations, and instances of a Unified Modelling Language (UML) model respectively. UML has been a standard language for domain modelling and application system design. Since the UML models include core domain knowledge in themselves, it is possible to use these models as a base knowledge for ontology building [31]. Taxonomic hierarchy of classes is realised through the element “subClassOf,” which corresponds to the generalisation/specialisation (“IS-A”) relationship type between two concepts [29]. A generalisation is a taxonomic relationship between a more general class and a more specific class [31].The UML constraints such as cardinalities and conditions are represented using OWL restriction in the part of OWL class definitions. As UML class model contains some of refined knowledge and concepts on its domain, it is expected that OWL ontology can be generated from UML [31]. Chapter 10 10.1 conclusion This project discusses pervasive environments, specifically smart homes, and pervasive healthcare. Ontology as the Web semantic language and OWL in particular are key requirements for building pervasive context-aware systems, in which sensors and devices are expected to share contextual knowledge and provide information to users based on their needs. The project presents an ontology of a smart home, in particular, elderly self-care home. Protege-OWL has been chosen to build the context-aware prototype ontology. This prototype ontology is less dependent on the rule languages outside of ontology rules. The reason for eliminating rules outside of the ontology is to make the ontology more consistent and make the most use of contexts for machine understandable terms. 10.2 Recommendation OWL provides little mechanism for representing relationships amongst properties but many powerful means for modelling relationships amongst classes [29]. Therefore, one approach to model associations among properties and more restrictions on properties is to conceptualise them as new classes. Then the specified elements in OWL for modelling relationships among classes can be used to model the transformed properties (i.e., classes). In addition, property restrictions and associations among properties actually represent concepts that need to be defined explicitly. For example, the night time or day time limit represents the concepts: minimum night time and maximum night time, which can be defined in OWL as night time class which has two properties: minimum night time and maximum night time. Appendix 1 - Taxonomy Appendix 2 - prototype Ontology Works Cited 1. Zhang K., L. Q. (2006). A Goal-Driven Approach of Service Composition for Pervasive Computing. IEEE Xplore . 2. K. Das, S. R. (2006). Learning, Prediction and Mediation of Context. IEEE Explorer . 3. Ding Zh., P. Y. (2004). A Probabilistic Extension to Ontology Language OWL. IEEE Xplore . 4. Chaari, T. et al, (2007). A comprehensive approach to model and use context. The Journal of Systems and Software . 5. 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