Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Existing context-aware applications are limited in their support of user personalisation. Nevertheless, the increase in the use of context-aware technologies has sparked the growth in assistive applications resulting in a need to enable adaptation to reflect the changes in user behaviours. This paper introduces a systematic approach to service personalisation for mobile users in pervasive environments and presents a service-oriented distributed system architecture. The developed approach makes use of semantic technologies for user modelling and personalisation reasoning. In the paper we characterise user behaviours and needs in pervasive environments upon which ontological user models are created with special emphasis being placed on ontological modelling of dynamic and adaptive user profiles. We develop a rule-based personalisation mechanism that exploits semantic web rule mark-up language for rule design and a combination of semantic and rule-based reasoning for personalisation. We use two case studies focusing on providing personalised travel assistance for people using Help-on-Demand services deployed on a smart-phone to contextualise the discussions within the paper. The proposed approach is implemented in a prototype system, which includes Help-on-Demand services, content management services, user models and personalisation mechanisms in addition to application specific rules. Experiments have been designed and conducted to test and evaluate the approach with initial results demonstrating the functionality of the approach

Learning Behaviour for Service Personalisation and Adaptation

Context-aware applications within pervasive environments are increasingly being developed as services and deployed in the cloud. As such these services are increasingly required to be adaptive to individual users to meet their specific needs or to reflect the changes of their behavior. To address this emerging challenge this paper introduces a service-oriented personalisation framework for service personalisation with special emphasis being placed on behavior learning for user model and service function adaptation. The paper describes the system architecture and the underlying methods and technologies including modelling and reasoning, behavior analysis and a personalisation mechanism. The approach has been implemented in a service-oriented prototype system, and evaluated in a typical scenario of providing personalised travel assistance for the elderly using the help-on-demand services deployed on smartphone

Ontology-based Activity Recognition Framework and Services

This paper introduces an ontology-based integrated framework for activity modeling, activity recognition and activity model evolution. Central to the framework is ontological activity modeling and semantic-based activity recognition, which is supported by an iterative process that incrementally improves the completeness and accuracy of activity models. In addition, the paper presents a service-oriented architecture for the realization of the proposed framework which can provide activity context-aware services in a scalable distributed manner. The paper further describes and discusses the implementation and testing experience of the framework and services in the context of smart home based assistive living

Motivation Modelling and Computation for Personalised Learning of People with Dyslexia

The increasing development of e-learning systems in recent decades has benefited ubiquitous computing and education by providing freedom of choice to satisfy various needs and preferences about learning places and paces. Automatic recognition of learners’ states is necessary for personalised services or intervention to be provided in e-learning environments. In current literature, assessment of learners’ motivation for personalised learning based on the motivational states is lacking. An effective learning environment needs to address learners’ motivational needs, particularly, for those with dyslexia. Dyslexia or other learning difficulties can cause young people not to engage fully with the education system or to drop out due to complex reasons: in addition to the learning difficulties related to reading, writing or spelling, psychological difficulties are more likely to be ignored such as lower academic self-worth and lack of learning motivation caused by the unavoidable learning difficulties. Associated with both cognitive processes and emotional states, motivation is a multi-facet concept that consequences in the continued intention to use an e-learning system and thus a better chance of learning effectiveness and success. It consists of factors from intrinsic motivation driven by learners’ inner feeling of interest or challenges and those from extrinsic motivation associated with external reward or compliments. These factors represent learners’ various motivational needs; thus, understanding this requires a multidisciplinary approach. Combining different perspectives of knowledge on psychological theories and technology acceptance models with the empirical findings from a qualitative study with dyslexic students conducted in the present research project, motivation modelling for people with dyslexia using a hybrid approach is the main focus of this thesis. Specifically, in addition to the contribution to the qualitative conceptual motivation model and ontology-based computational model that formally expresses the motivational factors affecting users’ continued intention to use e-learning systems, this thesis also conceives a quantitative approach to motivation modelling. A multi-item motivation questionnaire is designed and employed in a quantitative study with dyslexic students, and structural equation modelling techniques are used to quantify the influences of the motivational factors on continued use intention and their interrelationships in the model. In addition to the traditional approach to motivation computation that relies on learners’ self-reported data, this thesis also employs dynamic sensor data and develops classification models using logistic regression for real-time assessment of motivational states. The rule-based reasoning mechanism for personalising motivational strategies and a framework of motivationally personalised e-learning systems are introduced to apply the research findings to e-learning systems in real-world scenarios. The motivation model, sensor-based computation and rule-based personalisation have been applied to a practical scenario with an essential part incorporated in the prototype of a gaze-based learning application that can output personalised motivational strategies during the learning process according to the real-time assessment of learners’ motivational states based on both the eye-tracking data in addition to users’ self-reported data. Evaluation results have indicated the advantage of the application implemented compared to the traditional one without incorporating the present research findings for monitoring learners’ motivation states with gaze data and generating personalised feedback. In summary, the present research project has: 1) developed a conceptual motivation model for students with dyslexia defining the motivational factors that influence their continued intention to use e-learning systems based on both a qualitative empirical study and prior research and theories; 2) developed an ontology-based motivation model in which user profiles, factors in the motivation model and personalisation options are structured as a hierarchy of classes; 3) designed a multi-item questionnaire, conducted a quantitative empirical study, used structural equation modelling to further explore and confirm the quantified impacts of motivational factors on continued use intention and the quantified relationships between the factors; 4) conducted an experiment to exploit sensors for motivation computation, and developed classification models for real-time assessment of the motivational states pertaining to each factor in the motivation model based on empirical sensor data including eye gaze data and EEG data; 5) proposed a sensor-based motivation assessment system architecture with emphasis on the use of ontologies for a computational representation of the sensor features used for motivation assessment in addition to the representation of the motivation model, and described the semantic rule-based personalisation of motivational strategies; 6) proposed a framework of motivationally personalised e-learning systems based on the present research, with the prototype of a gaze-based learning application designed, implemented and evaluated to guide future work

A Reference Computational Model to Manage the Semantics of Learning Environments using SWRL Enabled OWL Ontologies

This thesis proposes a reference model and its computational core to support the creation of software applications within educational environments, which address Differences In Learning (DiffInL) and are applicable to both learners and instructors. This work differs from others in that the strength of this model resides on the re-usable character of the reasoning mechanism enabled by the computational environment. The starting point is the definition of agreed learning goals that the learner needs to achieve. In turn, the reference model generates personalised, best-practice teaching and learning materials, suitable for achieving the individual’s learning goals. This reference model consists of MODEL and MANAGEMENT components. The MODEL components store the domain needed to create learners and instructional models, which are required for the creation of Learning Spaces (LeS). The MANAGEMENT compo- nent also manages the semantics stored in various model components in order to carry out the configuration of an LeS. The architecture of software applications generated from the reference model is illustrated and contains: Netbeans IDE 8.0.2, JavaServer Faces framework and OWL-API library. We tested this to generate teaching practices for Learning Difficulties (LDif) student. In order to prove the feasibility of creating a software application from the reference model, an example of a particular scenario in a specific educational setting for LDif Students has been shown. This proposed model has successfully proved its ability to address the needs of LDif Students through a corresponding novel and re-usable reasoning mechanism implemented in Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) computational environments. The reference model has shown its ability to integrate with different disciplines such as psychology, sociology and human-computer interactions. The main contribution to research is the creation of a novel reference computational model which addresses the needs of people with DiffInL. The strength of this model resides on the re-usable character of the reasoning mechanism enabled by the computational environment. The whole framework allows a unified implementation which takes into account classes, constraints, matching, and inference mechanisms for the complete configuration of an LeS. The suggested approach also differs from previous work in that it is personalised, and the applied reasoning rules are dynamic. Therefore this model can be constantly “tuned” according to the questions we may ask in such environments. Overall, the proposed reference model in this research offers a promising and feasible solution that can support current educational systems and benefit both learners and instructors. It also demonstrates the applicability of the latest technologies and would allow for future technologies to be incorporated, in order to enhance the model

Modélisation d'une interaction système-résident contextuelle, personnalisée et adaptative pour l'assistance cognitive à la réalisation des activités de la vie quotidienne dans les maisons connectées

Alors que le nombre de personnes vivant avec des déficits cognitifs qui découlent d’un traumatisme craniocérébral (TCC) va en croissant, les technologies d’assistance sont de plus en plus développées pour résoudre les problèmes qu’ils induisent dans la réalisation des activités de la vie quotidienne. L’Internet des objets et l’intelligence ambiante offrent un cadre pour fournir des services d’assistance sensibles au contexte, adaptatifs, autonomes et personnalisés pour ces personnes ayant des besoins particuliers. Une revue de la littérature sur le sujet permet de constater que les systèmes existants offrent très souvent une assistance excessive, quand l’aide contient plus d’information que nécessaire ou quand elle est fournie automatiquement à chaque étape de l’activité. Cette assistance, inadaptée aux besoins et aux capacités de la personne, est contraire à certains principes de la réadaptation cognitive qui prônent la fourniture d’une assistance minimale pour encourager la personne à agir au meilleur de ses capacités. Cette thèse propose des modèles pour automatiser l’assistance cognitive sous forme de dialogue contextuel entre une personne ayant des déficits cognitifs dus au TCC et un système lui fournissant l’assistance appropriée qui l’encourage à réaliser ses activités par lui-même. Les principales contributions sont : (1) un modèle ontologique comme support de l’assistance cognitive dans les maisons connectées ; (2) un modèle d’interaction entre l’agent intelligent d’une maison connectée et une personne ayant subi un TCC, dans le cadre de l’assistance cognitive. Le modèle ontologique proposé s’appuie sur les actes de langages et les données probantes de la réadaptation cognitive afin que l’assistance reflète la pratique clinique. Il vise à fournir aux maisons intelligentes la sémantique des données nécessaires pour caractériser les situations où il y a besoin d’assistance, les messages d’assistance de gradations différentes et les réactions de la personne. Informé par le modèle ontologique, le modèle d’interaction basé sur des arbres de comportement (« behaviour trees ») permet alors à un agent intelligent de planifier dynamiquement la diffusion de messages d’assistance progressifs avec des ajustements si nécessaire, en fonction du profil et du comportement du résident de la maison connectée lors de l’accomplissement de ses activités. Une validation préliminaire montre l’applicabilité des modèles dans l’implémentation de scénarios relatifs à l’utilisation sécuritaire d’une cuisinière connectée dédiée aux personnes ayant subi un TCC

From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The global population is aging; projections show that by 2050, more than 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive smart homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive smart homes. The devised approach is based upon an intention recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the intention recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities

Semantic and Virtual Reality-Enhanced Configuration of Domestic Environments: The Smart Home Simulator

This paper introduces the Smart Home Simulator, one of the main outcomes of the D4All project. This application takes into account the variety of issues involved in the development of Ambient Assisted Living (AAL) solutions, such as the peculiarity of each end-users, appliances, and technologies with their deployment and data-sharing issues. The Smart Home Simulator - a mixed reality application able to support the configuration and customization of domestic environments in AAL systems - leverages on integration capabilities of Semantic Web technologies and the possibility to model relevant knowledge (about both the dwellers and the domestic environment) into formal models. It also exploits Virtual Reality technologies as an efficient means to simplify the configuration of customized AAL environments. The application and the underlying framework will be validated through two different use cases, each one foreseeing the customized configuration of a domestic environment for specific segments of users

Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

This research examines the problem of Information Overload (IO) and give an overview of various attempts to resolve it. Furthermore, argue that instead of fighting IO, it is advisable to start learning how to live with it. It is unlikely that in modern information age, where users are producer and consumer of information, the amount of data and information generated would decrease. Furthermore, when managing IO, users are confined to the algorithms and policies of commercial Search Engines and Recommender Systems (RSs), which create results that also add to IO. this research calls to initiate a change in thinking: this by giving greater power to users when addressing the relevance and accuracy of internet searches, which helps in IO. However powerful search engines are, they do not process enough semantics in the moment when search queries are formulated. This research proposes a semantic selection of internet sources, through SWRL enabled OWL ontologies. the research focuses on SWT and its Stack because they (a)secure the semantic interpretation of the environments where internet searches take place and (b) guarantee reasoning that results in the selection of suitable internet sources in a particular moment of internet searches. Therefore, it is important to model the behaviour of users through OWL concepts and reason upon them in order to address IO when searching the internet. Thus, user behaviour is itemized through user preferences, perceptions and expectations from internet searches. The proposed approach in this research is a Software Engineering (SE) solution which provides computations based on the semantics of the environment stored in the ontological model