The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

An ontology-driven architecture for data integration and management in home-based telemonitoring scenarios

The shift from traditional medical care to the use of new technology and engineering innovations is nowadays an interesting and growing research area mainly motivated by a growing population with chronic conditions and disabilities. By means of information and communications technologies (ICTs), telemedicine systems offer a good solution for providing medical care at a distance to any person in any place at any time. Although significant contributions have been made in this field in recent decades, telemedicine and in e-health scenarios in general still pose numerous challenges that need to be addressed by researchers in order to take maximum advantage of the benefits that these systems provide and to support their long-term implementation. The goal of this research thesis is to make contributions in the field of home-based telemonitoring scenarios. By periodically collecting patients' clinical data and transferring them to physicians located in remote sites, patient health status supervision and feedback provision is possible. This type of telemedicine system guarantees patient supervision while reducing costs (enabling more autonomous patient care and avoiding hospital over flows). Furthermore, patients' quality of life and empowerment are improved. Specifically, this research investigates how a new architecture based on ontologies can be successfully used to address the main challenges presented in home-based telemonitoring scenarios. The challenges include data integration, personalized care, multi-chronic conditions, clinical and technical management. These are the principal issues presented and discussed in this thesis. The proposed new ontology-based architecture takes into account both practical and conceptual integration issues and the transference of data between the end points of the telemonitoring scenario (i.e, communication and message exchange). The architecture includes two layers: 1) a conceptual layer and 2) a data and communication layer. On the one hand, the conceptual layer based on ontologies is proposed to unify the management procedure and integrate incoming data from all the sources involved in the telemonitoring process. On the other hand, the data and communication layer based on web service technologies is proposed to provide practical back-up to the use of the ontology, to provide a real implementation of the tasks it describes and thus to provide a means of exchanging data. This architecture takes advantage of the combination of ontologies, rules, web services and the autonomic computing paradigm. All are well-known technologies and popular solutions applied in the semantic web domain and network management field. A review of these technologies and related works that have made use of them is presented in this thesis in order to understand how they can be combined successfully to provide a solution for telemonitoring scenarios. The design and development of the ontology used in the conceptual layer led to the study of the autonomic computing paradigm and its combination with ontologies. In addition, the OWL (Ontology Web Language) language was studied and selected to express the required knowledge in the ontology while the SPARQL language was examined for its effective use in defining rules. As an outcome of these research tasks, the HOTMES (Home Ontology for Integrated Management in Telemonitoring Scenarios) ontology, presented in this thesis, was developed. The combination of the HOTMES ontology with SPARQL rules to provide a flexible solution for personalising management tasks and adapting the methodology for different management purposes is also discussed. The use of Web Services (WSs) was investigated to support the exchange of information defined in the conceptual layer of the architecture. A generic ontology based solution was designed to integrate data and management procedures in the data and communication layer of the architecture. This is an innovative REST-inspired architecture that allows information contained in an ontology to be exchanged in a generic manner. This layer structure and its communication method provide the approach with scalability and re-usability features. The application of the HOTMES-based architecture has been studied for clinical purposes following three simple methodological stages described in this thesis. Data and management integration for context-aware and personalized monitoring services for patients with chronic conditions in the telemonitoring scenario are thus addressed. In particular, the extension of the HOTMES ontology defines a patient profile. These profiles in combination with individual rules provide clinical guidelines aiming to monitor and evaluate the evolution of the patient's health status evolution. This research implied a multi-disciplinary collaboration where clinicians had an essential role both in the ontology definition and in the validation of the proposed approach. Patient profiles were defined for 16 types of different diseases. Finally, two solutions were explored and compared in this thesis to address the remote technical management of all devices that comprise the telemonitoring scenario. The first solution was based on the HOTMES ontology-based architecture. The second solution was based on the most popular TCP/IP management architecture, SNMP (Simple Network Management Protocol). As a general conclusion, it has been demonstrated that the combination of ontologies, rules, WSs and the autonomic computing paradigm takes advantage of the main benefits that these technologies can offer in terms of knowledge representation, work flow organization, data transference, personalization of services and self-management capabilities. It has been proven that ontologies can be successfully used to provide clear descriptions of managed data (both clinical and technical) and ways of managing such information. This represents a further step towards the possibility of establishing more effective home-based telemonitoring systems and thus improving the remote care of patients with chronic diseases

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling

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Expert System for Nutrition Care Process of Older Adults

This paper presents an expert system for a nutrition care process tailored for the specific needs of elders. Dietary knowledge is defined by nutritionists and encoded as Nutrition Care Process Ontology, and then used as underlining base and standardized model for the nutrition care planning. An inference engine is developed on top of the ontology, providing semantic reasoning infrastructure and mechanisms for evaluating the rules defined for assessing short and long term elders’ self-feeding behaviours, to identify unhealthy dietary patterns and detect the early instauration of malnutrition. Our expert system provides personalized intervention plans covering nutrition education, diet prescription and food ordering adapted to the older adult’s specific nutritional needs, health conditions and food preferences. In-lab evaluation results are presented proving the usefulness and quality of the expert system as well as the computational efficiency, coupling and cohesion of the defined ontology

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

The OCareCloudS project: toward organizing care through trusted cloud services

The increasing elderly population and the shift from acute to chronic illness makes it difficult to care for people in hospitals and rest homes. Moreover, elderly people, if given a choice, want to stay at home as long as possible. In this article, the methodologies to develop a cloud-based semantic system, offering valuable information and knowledge-based services, are presented. The information and services are related to the different personal living hemispheres of the patient, namely the daily care-related needs, the social needs and the daily life assistance. Ontologies are used to facilitate the integration, analysis, aggregation and efficient use of all the available data in the cloud. By using an interdisciplinary research approach, where user researchers, (ontology) engineers, researchers and domain stakeholders are at the forefront, a platform can be developed of great added value for the patients that want to grow old in their own home and for their caregivers

First steps of asthma management with a personalized ontology model

Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. Formal knowledge is selected as one of the most promising solutions to deal with these challenges. This paper presents a new personalized approach to manage asthma. An ontology-driven model supported by Semantic Web Rule Language (SWRL) medical rules is proposed to provide personalized care for an asthma patient by identifying the risk factors and the development of possible exacerbations

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S