An event detection framework for the representation of the AGGIR variables

International audienceIn this paper, we propose a framework to study the AGGIR (Autonomy Gerontology Iso-Resources Groups) grid model, in order to evaluate the level of independency of elderly people, according to their capabilities of performing activities and interact with their environments over the time. To model the Activities of Daily Living (ADL), we also extend a previously proposed Domain Specific Language (DSL), in order to employ operators to deal with constraints related to time and location of activities, and event recognition. Our framework aims at providing an analysis tool regarding the performance of elder-ly/handicapped people within a home environment by means of data recovered from sensors using the iCASA simulator. To evaluate our approach, we pick three of the AGGIR variables (i.e., dressing, toileting, and transfers) and evaluate their testability in many scenarios, by means of records representing the occurrence of activities of the elderly. Results demonstrate the accuracy of our framework to manage the obtained records correctly and thus generate the appropriate event information

Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market

A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care

Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies

Modeling the user state for context-aware spoken interaction in ambient assisted living

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfill these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02- 02, CAM CONTEXTS (S2009/TIC-1485

Internet of Things data contextualisation for scalable information processing, security, and privacy

The Internet of Things (IoT) interconnects billions of sensors and other devices (i.e., things) via the internet, enabling novel services and products that are becoming increasingly important for industry, government, education and society in general. It is estimated that by 2025, the number of IoT devices will exceed 50 billion, which is seven times the estimated human population at that time. With such a tremendous increase in the number of IoT devices, the data they generate is also increasing exponentially and needs to be analysed and secured more efficiently. This gives rise to what is appearing to be the most significant challenge for the IoT: Novel, scalable solutions are required to analyse and secure the extraordinary amount of data generated by tens of billions of IoT devices. Currently, no solutions exist in the literature that provide scalable and secure IoT scale data processing. In this thesis, a novel scalable approach is proposed for processing and securing IoT scale data, which we refer to as contextualisation. The contextualisation solution aims to exclude irrelevant IoT data from processing and address data analysis and security considerations via the use of contextual information. More specifically, contextualisation can effectively reduce the volume, velocity and variety of data that needs to be processed and secured in IoT applications. This contextualisation-based data reduction can subsequently provide IoT applications with the scalability needed for IoT scale knowledge extraction and information security. IoT scale applications, such as smart parking or smart healthcare systems, can benefit from the proposed method, which  improves the scalability of data processing as well as the security and privacy of data.   The main contributions of this thesis are: 1) An introduction to context and contextualisation for IoT applications; 2) a contextualisation methodology for IoT-based applications that is modelled around observation, orientation, decision and action loops; 3) a collection of contextualisation techniques and a corresponding software platform for IoT data processing (referred to as contextualisation-as-a-service or ConTaaS) that enables highly scalable data analysis, security and privacy solutions; and 4) an evaluation of ConTaaS in several IoT applications to demonstrate that our contextualisation techniques permit data analysis, security and privacy solutions to remain linear, even in situations where the number of IoT data points increases exponentially

Toolkits for the Development of Hybrid Games: from Tangible Tabletops to Interactive Spaces

Durante los últimos años, los dispositivos tabletop han sido considerados el entorno ideal para los juegos híbridos, los cuales combinan técnicas de juego tradicional, como el uso de objetos físicos para interactuar con el juego de una forma natural, con las nuevas posibilidades que los tabletops ofrecen de aumentar el espacio de juego con imágenes digitales y audio.Sin embargo, los juegos híbridos no se restringen simplemente a tabletops, pudiéndose jugar también en entornos más amplios en los que convergen otros paradigmas de interacción. Por esta razón, el uso de juegos híbridos en Espacios Interactivos está ganando fuerza, pero el número y heterogeneidad de dispositivos y estilos de interacción que se encuentran en estos entornos hace que el diseño y prototipado de juegos sea una tarea difícil. Por lo tanto, el gran reto se encuentra en ofrecer a diseñadores y desarrolladores herramientas apropiadas para la creación de estas aplicaciones.En esta línea de trabajo, el grupo Affective Lab lanzó el proyecto JUGUEMOS (TIN2015-67149-C3-1R), un proyecto nacional centrado en el desarrollo de juegos híbridos en entornos interactivos. Esta Tesis Doctoral se enmarca en este proyecto.El primer paso de la realización de esta tesis fue establecer los dos objetivos principales (Capítulo 1):1) El primer objetivo que se estableció fue profundizar en el uso de tabletops tangibles en terapia con niños con necesidades especiales. Durante los últimos años el grupo Affective Lab había visto la potencialidad de los tabletops tangibles para el trabajo con niños pequeños, pero todavía era necesario llevar a cabo más experiencias y evaluaciones en el ámbito terapéutico, así como explorar si otros grupos de usuarios (adultos con problemas cognitivos) podían beneficiarse de las características de los tabletops.2) El segundo objetivo consistió en diseñar e implementar un toolkit para el desarrollo de juegos híbridos para espacios interactivos. Se decidió que el toolkit estuviera dirigido a desarrolladores para facilitar su trabajo a la hora de crear este tipo de aplicaciones.Una vez establecidos los objetivos, se realizó un estado del arte a su vez dividido en dos partes (Capítulo 2):1) Se realizó una categorización de juegos híbridos para entender y extraer sus principales características, así como los principales retos que surgen al desarrollar este tipo de juegos. También se estudiaron toolkits cuyo objetivo era el desarrollo de juegos híbridos.2) Se estudiaron juegos híbridos desarrollados para niños con necesidades especiales y adultos con problemas cognitivos que hacían uso de la Interacción Tangible y tabletops, así como toolkits dirigidos a terapeutas o educadores para ayudarles en la creación de actividades para sus pacientes.Para llevar a cabo las experiencias y evaluaciones relacionadas con el primer objetivo, se hizo uso del tabletop tangible NIKVision, desarrollado previamente por el grupo Affective Lab, y el toolkit KitVision, una herramienta dirigida a profesionales sin conocimientos de programación para la creación de actividades tangibles y que fue desarrollado durante el Proyecto Final de Carrera de la autora. En el Capítulo 3 de esta Tesis se comenta brevemente el tabletop NIKVision y la arquitectura de KitVision, se describen las evaluaciones que se llevaron a cabo con terapeutas con el objetivo de mejorar y probar la utilidad del toolkit, y se explica una experiencia de un año durante la cual una terapeuta ocupacional de ASAPME, una asociaciónque trabaja con adultos con problemas cognitivos, estuvo usando el tabletop y el toolkit sin supervisión.En el Capítulo 4 se describen diferentes experiencias con KitVision que se llevaron a cabo:- Gracias a una colaboración con la Residencia Romareda, NIKVision y KitVision fueron instalados provisionalmente en la residencia y, tras una evaluación inicial, se desarrollaron tres nuevas actividades para los usuarios de la residencia.- Gracias a la colaboración con ENMOvimienTO y con uno de los centros de Atención Temprana del Instituto Aragonés de Servicios Sociales (IASS), ambos enfocados a trabajar con niños con problemas de aprendizaje, se pudieron realizar evaluaciones que nos permitieron mejorar KitVision y crear nuevas actividades específicamente diseñadas para ellos.- Finalmente, gracias a una colaboración con Atenciona, pudimos evaluar actividades con niños con Trastorno por Déficit de Atención e Hiperactividad (TDAH) y extraer una serie de directrices para diseñar actividades para este tipo de niños. También pudimos llevar a cabo una experiencia de Diseño Participativo con estos niños.El completo desarrollo del toolkit JUGUEMOS, para la creación de juegos híbridos en espacios interactivos, se explica en el Capítulo 5. En este apartado primero se describe el Espacio Interactivo JUGUEMOS que sirvió de base para desarrollar el toolkit. Después se explican con detalle las decisiones de diseño que se tomaron, el modelo de abstracción que se usó para diseñar los juegos, y la arquitectura del toolkit. También se detallan las distintas fases de implementación que se llevaron a cabo, basadas en los tres retos que se extrajeron en el estado del arte: (1) integrar diferentes dispositivos, (2) gestionar salidas gráficas diversas y (3) facilitar la codificación del juego. Finalmente, se presentan dos prototipos de juegos que se desarrollaron durante las dos estancias de investigación que la autora realizó.Finalmente, en el Capítulo 6 se describen los tres casos de uso que se realizaron para tener una primera valoración de la usabilidad del toolkit JUGUEMOS: (1) una evaluación con estudiantes de Máster en la que se implementó un juego completamente funcional para el Espacio Interactivo JUGUEMOS, (2) un juego que fue completamente desarrollado usando el toolkit JUGUEMOS una vez que éste se acabó de implementar, (3) una experiencia que involucró a dos grupos multidisciplinares compuestos por diseñadores y desarrolladores, en la que tuvieron que colaborar para diseñar e implementar dos prototipos de juegos híbridos para el espacio interactivo.<br /