An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.</p

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Review on smartphone sensing technology for structural health monitoring

Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology

Mapping and analysing prospective technologies for learning – Results from a consultation with European stakeholders and roadmaps for policy action

EU policies call for the strengthening of Europe’s innovative capacity and it is considered that the modernisation of Education and Training systems and technologies for learning will be a key enabler of educational innovation and change. This report brings evidence to the debate about the technologies that are expected to play a decisive role in shaping future learning strategies in the short to medium term (5-10 years from now) in three main learning domains: formal education and training; work-place and work-related learning; re-skilling and up-skilling strategies in a lifelong-learning continuum. This is the final report of the study ‘Mapping and analysing prospective technologies for learning (MATEL)' carried out by the MENON Network EEIG on behalf of the European Commission, Joint Research Centre, Institute for Prospective Technological Studies. The report synthesises the main messages gathered from the three phases of the study: online consultation, state-of-the-art analysis and a roadmapping workshop. Eight technology clusters and a set of related key technologies that can enable learning innovation and educational change were identified. A number of these technologies were analysed to highlight their current and potential use in education, the relevant market trends and ongoing policy initiatives. Three roadmaps, one for each learning domain, were developed. These identified long-term goals and specific objectives for educational change, which in turn led to recommendations on the immediate strategies and actions to be undertaken by policy and decision makers.JRC.J.3-Information Societ