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

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

Nudging lifestyles for better health outcomes: crowdsourced data and persuasive technologies for behavioural change

For at least three decades, a Tsunami of preventable poor health has continued to threaten the future prosperity of our nations. Despite its effective destructive power, our collective predictive and preventive capacity remains remarkably under-developed This Tsunami is almost entirely mediated through the passive and unintended consequences of modernisation. The malignant spread of obesity in genetically stable populations dictates that gene disposition is not a significant contributor as populations, crowds or cohorts are all incapable of experiencing a new shipment of genes in only 2-3 decades. The authors elaborate on why a supply-side approach: advancing health care delivery cannot be expected to impact health outcomes effectively. Better care sets the stage for more care yet remains largely impotent in returning individuals to disease-free states. The authors urge an expedited paradigmatic shift in policy selection criterion towards using data intensive crowd-based evidence integrating insights from system thinking, networks and nudging. Collectively these will support emerging potentialities of ICT used in proactive policy modelling. Against this background the authors proposes a solution that stated in a most compact form consists of: the provision of mundane yet high yield data through light instrumentation of crowds enabling participative sensing, real time living epidemiology separating the per unit co-occurrences which are health promoting from those which are not, nudging through persuasive technologies, serious gaming to sustain individual health behaviour change and intuitive visualisation with reliable simulation to evaluate and direct public health investments and policies in evidence-based waysJRC.DDG.J.4-Information Societ

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

SEMPER: A Web-Based Support System for Patient Self-Management

The paper discusses an eHealth project which is currently developing an interactive web-based platform that assists patients to self-manage work-related disorders and alcoholism. The focus is on motivating long-term behaviour change. This is supported by an online assessment component based on the technique of motivational interviewing and a feedback component which visualizes actual behaviour in relation to intended behaviour. Disease-specific information is provided through an information portal that utilizes lightweight ontologies (associative networks) in combination with text mining. Emotional support is provided via virtual communities. The paper discusses the design rationales underlying the approach taken and outlines some implementational aspects. The paper also briefly outlines how the effectiveness of the self-management tool will be measured based on an outcome model particularly suited for health promotion

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach