Modelling weightlifting “training-diet-competition” cycle ontology with domain and task ontologies

Studies in weightlifting have been characterized by unclear results, and paucity of information. This is due to the fact that enhancing the understanding of the mechanics of successful lift requires collaborative contributions of several stakeholders such as coach, nutritionist, biomechanist, and physiologist as well as the aid of technical advances in motion analysis, data acquisition, and methods of analysis. Currently, there are still a lack of knowledge sharing between these stakeholders. The knowledge owned by these experts are not captures, classified or integrated into an information system for decision-making. In this study, we propose an ontology-driven weightlifting knowledge model as a solution for promoting a better understanding of the weightlifting domain as a whole. The study aims to build a knowledge framework for Olympic weightlifting, bringing together related knowledge subdomains such as training methodology, biomechanics, and dietary while modelling the synergy among them. In so doing, terminology, semantics, and used concepts will be unified among researchers, coaches, nutritionists, and athletes to partially obviate the recognized limitations and inconsistencies. The whole weightlifting "training-diet-competition" (TDC) cycle is semantically modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising reasoning capability toward an automated and tailored weightlifting TDC cycle.- (undefined

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

publishedVersio

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

An ontology to integrate multiple knowledge domains of training-dietary-competition in weightlifting: A nutritional approach

This study is a part of weightlifting “TrainingDietary-Competition” (TDC) cycle ontology. The main objective of TDC-cycle is to build a knowledge framework for Olympic weightlifting, bringing together related fields such as training methodology, weightlifting biomechanics, and nutrition while modelling the synergy among them. In so doing, terminology, semantics, and used concepts are unified among athletes, coaches, nutritionists, and researchers to partially obviate the problem of unclear results and paucity of information. The uniqueness of this ontology is its ability to solve the knowledge sharing problem in which the knowledge owned by these experts in each field are not captures, classified or integrated into an information system for decision-making. The whole weightlifting TDC-cycle is semantically modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising reasoning capability toward an automated and tailored weightlifting TDC-cycle. However, this study will focus mainly on the nutrition domain. The intended application of this part of ontology is to provide a useful decision-making platform for a sport nutritionist who gathers and integrate relevant scientific information, equation, and tools necessary when providing nutritional services. The system is constructed by using Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), and Semantic Query-Enhanced Web Rule Language (SQWRL). The use of weightlifting TDC-cycle ontology can be helpful for nutritionists to create a well-planned nutrition program for athletes (especially, in the process of nutrition monitoring to identify energy imbalance in athletes) by reducing time consumption and calculation errors.The authors would like to thank Prof.Adriano Tavares for his guidance and providing necessary in formation regarding the project

DDSS: Dynamic decision support system for elderly

To provide robust healthcare services and personalized recommendations details relating to a patient’s daily life activities, profile information, and personal experience is of vital importance. This paper focuses on improvement in general health status of elderly patients through the use of an innovative service which align dietary intake with activity information. Personalized healthcare services based on the patient’s activities of daily living and their shared experience, are provided as outputs. A knowledge driven approach has been used where all the daily life activities, social interactions, and profile information are modeled in an ontology. The semantic context is exploited that enables fine-grained situation analysis for recommendation of personalized services and decision support. Preliminary experimental results for the dynamic nature of the systems and its corresponding personalized recommendations have been found to be encouraging

Ontology-based personalized performance evaluation and dietary recommendation for weightlifting.

Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology.Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology

An End-to-End Semantic Platform for Nutritional Diseases Management

The self-management of nutritional diseases requires a system that combines food tracking with the potential risks of food categories on people’s health based on their personal health records (PHRs). The challenges range from the design of an effective food image classification strategy to the development of a full-fledged knowledge-based system. This maps the results of the classification strategy into semantic information that can be exploited for reasoning. However, current works mainly address the single challenges separately without their integration into a whole pipeline. In this paper, we propose a new end-to-end semantic platform where: (i) the classification strategy aims to extract food categories from food pictures; (ii) an ontology is used for detecting the risk factors of food categories for specific diseases; (iii) the Linked Open Data (LOD) Cloud is queried for extracting information concerning related diseases and comorbidities; and, (iv) information from the users’ PHRs are exploited for generating proper personal feedback. Experiments are conducted on a new publicly released dataset. Quantitative and qualitative evaluations, from two living labs, demonstrate the effectiveness and the suitability of the proposed approach

Ontology-based personalized system to support patients at home

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Chronic diseases are incurable diseases that require long term supervision and treatments by medical professionals. The most common chronic diseases are cardiovascular disease, obesity, diabetes respiratory diseases and cancer. With information and communication technology many applications have been implemented to facilitate different clinical decision making process. With new technology, personalized healthcare systems are in place to enable patients with chronic diseases to acquire continuous and long-term medical services at home. This improves healthcare delivery since medical services can be accessed at any place. Today high prevalence of chronic diseases poses technological challenges to existing personalized healthcare systems including data integration and personalized recommendation plan. This research investigates how semantic technologies could be used to address the above challenges. The goal of this thesis is to use semantic technology for building ontology knowledge repository to provide data integration and medical recommendations for home based diabetes management systems. This ontology focuses on organizing knowledge related to vital sign measurement, questionnaire and recommendations for diabetic patients. To enter and link concepts and data for diabetes ontology, we used Protégé-owl. The ontology model provides knowledge into which information on individual patient including vital-sign data, questionnaires based information and recommendation are derived. Based on ontology’s structure, the model can collect, store and share information from heterogeneous sources, Reason over knowledge. Furthermore, ontology has been proven to be a better way of describing managed data. Therefore ontology based technology could be implemented in the personalized systems to enhance remote care for home-patient. Keywords

Lipoprotein ontology: a formal representation of Lipoproteins

Lipoproteins serve as a mode of transport for the uptake, storage and metabolism of lipids. Dysregulation in lipoprotein metabolism, known as dyslipidaemia, is strongly correlated to various diseases such as cardiovascular disease. Lipoprotein Ontology provides a formal representation of lipoprotein concepts and relationships that can be used to support the intelligent retrieval of information, faciliate collaboration between research groups, and provide the basis for the development of tools for the diagnosis and treatment of dyslipidaemia

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