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

Using conceptual graphs for clinical guidelines representation and knowledge visualization

The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit

A service-based system for malnutrition prevention and self-management

Malnutrition is considered one of the root causes for the occurrence of other diseases. It is particularly common in the ageing population, where it requires more efficient handling and management to enable longer home independent living. However, to achieve this, a number of related challenges need to be overcome, especially those related to management of health and disease let alone other social and logistical barriers. This paper presents the design of a distributed system that enables homecare management in the context of self-feeding and malnutrition prevention through balanced nutritional intake. The design employs a service-based system that incorporates a number of services including monitoring of activities, nutritional reasoning for assessing feeding habits, diet recommendation for food planning, and marketplace invocation for automating food shopping to meet dietary requirements. The solution is deployed in a small pilot in 12 elder adult houses that, in early results, demonstrates its holistic user-centred scalable approach for malnutrition self-management

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

4th International Symposium on Ambient Intelligence (ISAmI 2013)

Ambient Intelligence (AmI) is a recent paradigm emerging from Artificial Intelligence (AI), where computers are used as proactive tools assisting people with their day-to-day activities, making everyone’s life more comfortable. Another main concern of AmI originates from the human computer interaction domain and focuses on offering ways to interact with systems in a more natural way by means user friendly interfaces. This field is evolving quickly as can be witnessed by the emerging natural language and gesture based types of interaction. The inclusion of computational power and communication technologies in everyday objects is growing and their embedding into our environments should be as invisible as possible. In order for AmI to be successful, human interaction with computing power and embedded systems in the surroundings should be smooth and happen without people actually noticing it. The only awareness people should have arises from AmI: more safety, comfort and wellbeing, emerging in a natural and inherent way. ISAmI is the International Symposium on Ambient Intelligence and aiming to bring together researchers from various disciplines that constitute the scientific field of Ambient Intelligence to present and discuss the latest results, new ideas, projects and lessons learned, namely in terms of software and applications, and aims to bring together researchers from various disciplines that are interested in all aspects of this area

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

NoMoDEI : A framework for Norm Monitoring on Dynamic Electronic Institutions

With the growth of the Internet, computational systems have become more and more complex, often including complicate interconnected networks of autonomous components. The need to bring some organisational structure into autonomous systems becomes urgent, as this allows regulating the behaviour of the different autonomous components to ensure their objectives are aligned with the holistic objectives of the system. Normative Systems are one of the mechanisms that can be applied to define and enforce acceptable behaviour within distributed electronic systems which should comply with some (human) regulations. One of the requirements to effectively implement Normative Systems is to be able to assess, at runtime, the state of the normative environment. Existing lines of research have already tried to tackle this issue on some simple scenarios. However, more complex scenarios may appear, for instance, scenarios where the normative context is not static, but it expands and contracts as new norms are added to the institution and removed from it respectively. As in human legal systems, it is easy to foresee that some of these electronic normative environments will not be static. They should be able to evolve through time as regulations change, effectively adapting to new situations and behaviours. Under these conditions, a monitoring system must be able to continue computing the state of the normative environment at runtime, as often we can not afford to perform the changes on the normative context off-line. Furthermore, it must be guaranteed the monitoring system can keep producing states of the normative environment that are consistent with the changes performed on the normative context. For instance, if a norm has been removed from the normative context, it does not make sense anymore to compute normative states where the norm has been violated. In this thesis we present NoMoDEI, a normative monitoring framework for dynamic Electronic Institutions. We formalize and develop an extended normative framework and architecture to cope with scenarios where the normative context is dynamic, therefore norms can be added, removed and updated. The operations are to be performed at run-time, without having to stop computing the normative state. The normative states computed are consistent with the expansion and contraction operations. NoMoDEI is introduced in three steps. First, we formally define the operations to be supported in order to allow for expanding and contracting the normative context. Then, we instantiate the formal operations, providing implementation details. Finally, we demonstrate our framework by applying it to two use cases: E-health systems and waste-water management on a river basin.Amb l'expansió d'Internet els sistemes computacionals han esdevingut més complexos, sovint incorporant complicades xarxes interconnectades de components autònoms. Es per això que la necessitat d'incorporar estructures organitzacionals en el sistemes autònoms s 'accentua, donat que aquestes estructures permeten regular el comportament dels diferents components autònoms, tot assegurant que els seus objectius es troben alineats amb els objectius generals del sistema. Els Sistemes Normatius (i.e. Normative Systems) són un dels mecanismes que podem aplicar per definir i imposar patrons acceptables de comportament dintre de sistemes electrònics distribuïts. Això esdevé especialment important quan el sistema es troba regimentat per regulacions (normalment humanes). Un dels requeriments per implementar Sistemes Normatius és ser capaços de determinar, en temps d'execució, l'estat de l'entorn normatiu. Existeixen línies de recerca que ja han tractat aquest problema en alguns escenaris simples. El món real però ens ofereix escenaris més complexes, com per exemple, escenaris on el context normatiu no és estàtic, si no que s'expandeix i contrau a mesura que noves normes són afegides o eliminades de la institució. Tal com passa als sistemes legals humans, és fàcil preveure que alguns contextos normatius electrònics no seran estàtics. Aquests contextos haurien de ser capaços d'evolucionar a través del temps a mesura que les regulacions canvien, adaptant-se a noves situacions i comportaments. Sota aquestes condicions, un sistema de monitorització ha de ser capaç de continuar calculant l'estat de l'entorn normatiu en temps d'execució, ja que sovint no ens podem permetre realitzar els canvis a l'entorn normatiu aturant el procés de monitorització. És més s'ha de garantir que el sistema de monitorització sigui capaç de continuar produint es tats de l’entorn normatiu de forma consistent amb els canvis realitzats. Per exemple, el fet d'eliminar una norma fa que no tingui gaire sentit continuar calculant es tats normatius on aquesta norma ha es tat violada. A aquesta Tesi presentem NoMoDEI, una infraestructura de monitorització normativa per institucions electròniques dinàmiques. Formalitzem i desenvolupem una infraestructura de monitorització normativa estesa capaç d'operar en escenaris on el context normatiu es dinàmic. Es a dir, diverses normes poden ser introduïdes, eliminades o actualitzades del context normatiu en qualsevol moment. Aquestes operacions s'han de poder realitzar en temps d'execució, es a dir, sense deixar de calcular l'estat normatiu. Es més, els estats normatius calculats han de ser consistents amb les respectives operacions d'extensió o contracció del context. Durant la Tesi presentem NoMoDEI en tres passos. Primer proporcionem una definició formal de les operacions que la infraestructura ha de suportar per permetre expandir i contraure el context normatiu. A continuació instanciem aquestes operacions proporcionant detalls d'implementació. Finalment demostrem que la nostra infraestructura pot ser aplicada a casos d'ús del món real introduint dos casos: sistemes de salut electrònics (i.e. E-health) i sistemes de tractament d’aigües residuals a la conca d’un riuPostprint (published version