Detection of speech signal in strong ship-radiated noise based on spectrum entropy

Comparing the frequency spectrum distributions calculated from several successive frames, the change of the frequency spectrum of speech frames between successive frames is larger than that of the ship-radiated noise. The aim of this work is to propose a novel speech detection algorithm in strong ship-radiated noise. As inaccurate sentence boundaries are a major cause in automatic speech recognition in strong noise background. Hence, based on that characteristic, a new feature repeating pattern of frequency spectrum trend (RPFST) was calculated based on spectrum entropy. Firstly, the speech is detected roughly with the precision of 1 s by calculating the feature RPFST. Then, the detection precision is up to 20 ms, the length of frames, by method of frame shifting. Finally, benchmarked on a large measured data set, the detection accuracy (92 %) is achieved. The experimental results show the feasibility of the algorithm to all kinds of speech and ship-radiated noise

Detection of speech signal in strong ship-radiated noise based on spectrum entropy

Comparing the frequency spectrum distributions calculated from several successive frames, the change of the frequency spectrum of speech frames between successive frames is larger than that of the ship-radiated noise. The aim of this work is to propose a novel speech detection algorithm in strong ship-radiated noise. As inaccurate sentence boundaries are a major cause in automatic speech recognition in strong noise background. Hence, based on that characteristic, a new feature repeating pattern of frequency spectrum trend (RPFST) was calculated based on spectrum entropy. Firstly, the speech is detected roughly with the precision of 1 s by calculating the feature RPFST. Then, the detection precision is up to 20 ms, the length of frames, by method of frame shifting. Finally, benchmarked on a large measured data set, the detection accuracy (92 %) is achieved. The experimental results show the feasibility of the algorithm to all kinds of speech and ship-radiated noise

a systematic review

Funding Information: This study is part of an interdisciplinary research project, funded by the Special Research Fund (Bijzonder Onderzoeksfonds) of Ghent University.Introduction: Ontologies are a formal way to represent knowledge in a particular field and have the potential to transform the field of health promotion and digital interventions. However, few researchers in physical activity (PA) are familiar with ontologies, and the field can be difficult to navigate. This systematic review aims to (1) identify ontologies in the field of PA, (2) assess their content and (3) assess their quality. Methods: Databases were searched for ontologies on PA. Ontologies were included if they described PA or sedentary behavior, and were available in English language. We coded whether ontologies covered the user profile, activity, or context domain. For the assessment of quality, we used 12 criteria informed by the Open Biological and Biomedical Ontology (OBO) Foundry principles of good ontology practice. Results: Twenty-eight ontologies met the inclusion criteria. All ontologies covered PA, and 19 included information on the user profile. Context was covered by 17 ontologies (physical context, n = 12; temporal context, n = 14; social context: n = 5). Ontologies met an average of 4.3 out of 12 quality criteria. No ontology met all quality criteria. Discussion: This review did not identify a single comprehensive ontology of PA that allowed reuse. Nonetheless, several ontologies may serve as a good starting point for the promotion of PA. We provide several recommendations about the identification, evaluation, and adaptation of ontologies for their further development and use.publishersversionpublishe

Starting with small health data opportunities for mHealth in Africa

The need to obtain data to understand effective and available child mortality-reducing control measures in rural areas of developing countries is great. Evidence shows that this challenge can potentially be overcome with the increased availability of Information and Communication Technology (ICT) to support the data/information/ knowledge needs of healthcare delivery services in low resource settings. Recognising the benefits of ICT and the need for improvements in the Nigerian health sector, this paper outlines the plans for the technical feasibility assessment of the IMPACT (usIng Mobile Phones for Assessing, Classifying and Treating sick children) smartphone application to capture, store and analyse of child health assessment data. IMPACT is a secure, scalable, user friendly mobile health (mHealth) innovation that is being developed to support ‘small data’ capabilities within the context of healthcare in the community in Enugu State, Nigeria, Africa. Notwithstanding the heightened focus on ‘big data’ in health, this research is interested in investigating the opportunities associated with doing ‘small healthcare data’ well, with the long term view of building to the big data scenario for healthcare in the community in Enugu. This paper outlines the plan for the IMPACT project considering the implications for health data, knowledge management in healthcare and the big data opportunities to support disease surveillance, healthcare delivery and resourcing and healthcare practitioner education

Discrete separation of patients’ profiles for chronical obstructive pulmonary disease context-aware healthcare efficient systems

According to the Public Health Agency of Canada (PHAC), the symptoms of chronic obstructive pulmonary disease (COPD) are shortness of breath, coughing, and sputum production. Many studies estimate that COPD will become the third-leading cause of death worldwide by 2030 (WHO, 2008). Pervasive healthcare systems cover healthcare issues, including chronic diseases; they help patients to manage their own health information and healthcare services at any time and in any place. We developed a COPD healthcare system based on a combination of the parameters of patients. The main goal is to avoid the severe phases of the disease by monitoring them. This combination of risk factors provides in total 600 profiles from data, with 88.5% accuracy. However, many studies have focused on and shown the issues of the effectiveness and accuracy of these systems. The problem is to apply a new classification model to detect the severe phases of the disease early. Therefore, instead of working on COPD parameters, we design and validate a profile-based classification model of patients. This model will facilitate the building of a rule-based framework. In addition, the accuracy of our extended COPD system is improved using the classification and separation of patients’ profiles

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

Modèle ontologique contextuel pour les patients atteints de la maladie pulmonaire obstructive chronique

L'informatique ubiquitaire est considérée comme l'une des réalisations scientifiques les plus marquantes de la dernière décennie. Cette vision a créé une révolution dans les interactions des utilisateurs finaux à partir le concept de sensibilité au contexte. L'informatique ubiquitaire offre une nouvelle opportunité pour remodeler la forme des solutions conventionnelles en fournissant des services personnalisés en fonction des situations contextuelles de chaque environnement. Des centaines d'architectures théoriques ont été développées dans le but de mettre en oeuvre l'idée de systèmes sensible au contexte. Cependant, l'informatique ubiquitaire est encore pratiquement non applicable en raison de nombreux défis, surtout que les architectures proposées se présentent toujours comme une solution générale qui permet de satisfaire n'importe quel type d'application et toutes sortes d'utilisation. OBJECTIFS: Cette thèse vise à concevoir et valider un modèle contextuel pour les systèmes de soins de santé ubiquitaires et spécifiquement destinés à aider les patients souffrant de la maladie pulmonaire obstructive chronique (MPOC). LA MÉTHODE: Les informations contextuelles sont très importantes pour les applications de soins de santé sensibles au contexte, en particulier celles utilisées pour surveiller les patients atteints de maladies chroniques qui sont affectées par des conditions concevables. Dans cette thèse, nous proposons une nouvelle classification de contexte pour le domaine médical qui couvre tous les aspects influençant la santé des patients. La grande échelle de cette classification le rend apte pour être une référence générale pour de divers projets de recherche s'intéressant au contexte médical. Ensuite, nous proposons un modèle contextuel à base d’ontologies capable de gérer la structure complexe du domaine de la MPOC de manière cohérente, en proportion de la nature dynamique de cet environnement. Ce nouveau modèle ontologique constitue le noyau de notre perception pour la mise en oeuvre de la solution de soins de santé ubiquitaire. Le modèle présenté examine son efficacité dans la gestion de l’une des maladies les plus vulnérables au contexte, où il prouve ainsi sa capacité à adapter les services de soins de santé à titre personnel et en fonction des conditions actuelles et prévues. Le modèle proposé a montré des résultats prometteurs dépassant 85% approuvé par un groupe de spécialistes expérimentés dans le domaine des maladies pulmonaires. Ubiquitous computing is considered one of the most impactful scientific achievements in the last decade. This conception created tremendous revolution in the end-user interactions through the concept of context-awareness. Ubiquitous computing offers a new opportunity to redesign the pattern of conventional solutions where it can easily tailor its processes upon existing contextual situations. Hundreds of theoretical architectures have been developed to enable context-awareness computing in pervasive settings. However, ubiquitous computing is still practically not feasible due to many challenges, but most importantly, that the proposed models always present themselves as a general solution to all kinds of real-life applications. OBJECTIVES: This thesis aims to design and validate a contextual model for health-care context-aware systems to support patients suffer from Chronic Obstructive Pulmonary Disease (COPD). METHODS: The contextual information is important for developing Context-Aware Healthcare Applications, especially those used to monitor patients with chronic diseases which are affected by perceived conditions. In this thesis, we propose a novel context categorization within the medical domain which covers all the context aspects. Then, we propose an ontology-based model able to handle the complex contextual structure of the COPD domain coherently, and in proportion to the dynamic nature of that environment. This new ontological context is the core of our perception for implementing the ubiquitous healthcare solution. The presented model examines its effectiveness in managing one of the most context-sensitive diseases, thereby demonstrating its ability to adapt health care services on a personal basis and in accordance with current and projected events. The proposed model has shown promising results exceeding 85% approved by a group of experienced specialists in respiratory and lung diseases

A Knowledge Based Educational (KBEd) framework for enhancing practical skills in engineering distance learners through an augmented reality environment

The technology advancement has changed distance learning teaching and learning approaches, for example, virtual laboratories are increasingly used to deliver engineering courses. These advancements enhance the distance learners practical experience of engineering courses. While most of these efforts emphasise the importance of the technology, few have sought to understand the techniques for capturing, modelling and automating the on-campus laboratory tutors’ knowledge. The lack of automation of tutors’ knowledge has also affected the practical learning outcomes of engineering distance learners. Hence, there is a need to explore further on how to integrate the tutor's knowledge, which is necessary for imparting and assessing practical skills through current technological advances in distance learning. One approach to address this concern is through the use of Knowledge Based Engineering (KBE) principles. These KBE principles facilitate the utilisation of standardised methods for capturing, modelling and embedding experts’ knowledge into engineering design applications for the automation of product design. Hence, utilising such principles could facilitate, automating engineering laboratory tutors’ knowledge for teaching and assessing practical skills. However, there is limited research in the application of KBE principles in the educational domain. Therefore, this research explores the use of KBE principles to automate instructional design in engineering distance learning technologies. As a result, a Knowledge Based Educational (KBEd) framework that facilitates the capturing, modelling and automating on-campus tutors’ knowledge and introduces it to distance learning and teaching approaches. This study used a four-stage experimental approach, which involved rapid prototyping method to design and develop the proposed KBEd framework to a functional prototype. The developed prototype was further refined through internal and external expert group using face validity methods such as questionnaire, observation and discussion. The refined prototype was then evaluated through welding task use-case. The use cases were assessed by first year engineering undergraduate students with no prior experience of welding from Birmingham City University. The participants were randomly separated into two groups (N = 46). One group learned and practised basic welding in the proposed KBEd system, while the other learned and practised in the conventional on-campus environment. A concurrent validity assessment was used in determining the usefulness of the proposed system in learning hands-on practical engineering skills through proposed KBEd system. The results of the evaluation indicate that students who trained with the proposed KBEd system successfully gained the practical skills equivalent to those in the real laboratory environment. Although there was little performance variation between the two groups, it was rooted in the limitations of the system’s hardware. The learning outcomes achieved also demonstrated the successful application of KBE principles in capturing, modelling and transforming the knowledge from the real tutor to the AI tutor for automating the teaching and assessing of the practical skills for distance learners. Further the data analysis has shown the potential of KBEd to be extendable to other taught distance-learning courses involving practical skills