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

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

London Creative and Digital Fusion

date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000The London Creative and Digital Fusion programme of interactive, tailored and in-depth support was designed to support the UK capital’s creative and digital companies to collaborate, innovate and grow. London is a globally recognised hub for technology, design and creative genius. While many cities around the world can claim to be hubs for technology entrepreneurship, London’s distinctive potential lies in the successful fusion of world-leading technology with world-leading design and creativity. As innovation thrives at the edge, where better to innovate than across the boundaries of these two clusters and cultures? This booklet tells the story of Fusion’s innovation journey, its partners and its unique business support. Most importantly of all it tells stories of companies that, having worked with London Fusion, have innovated and grown. We hope that it will inspire others to follow and build on our beginnings.European Regional Development Fund 2007-13

Privacy Education Effectiveness: Does It Matter?

Mobile devices are a constantly used item in a college student’s life. Students depend on them for entertainment, academics, and socializing with their friends. While they continually use them, they perhaps do not understand the impact of their use on their privacy or that the devices can be used to track them and collect their personal information. This study utilizes the Antecedent, Privacy Concern, Outcome (APCO) model, combined with the Fogg Behavior Model (FBM) to determine (1) the factors that comprise privacy concerns on a mobile device; (2) whether individuals use privacy-protective behaviors, and (3) whether education on privacy issues regarding mobile devices will increase their use of privacy-enhancing technology (PET). A longitudinal study was conducted to test whether privacy protection education increases the use of PET. While students express concern for their privacy when using mobile devices and express an intent to use additional PET, their behavior using mobile device protections does not change, even after an educational intervention. Perceived privacy control does not change their privacy concern and habit and trust outweigh the impact of privacy concern. Theoretical and practical implications are provided

Characterizing web pornography consumption from passive measurements

Web pornography represents a large fraction of the Internet traffic, with thousands of websites and millions of users. Studying web pornography consumption allows understanding human behaviors and it is crucial for medical and psychological research. However, given the lack of public data, these works typically build on surveys, limited by different factors, e.g. unreliable answers that volunteers may (involuntarily) provide. In this work, we collect anonymized accesses to pornography websites using HTTP-level passive traces. Our dataset includes about $15\,000$ broadband subscribers over a period of 3 years. We use it to provide quantitative information about the interactions of users with pornographic websites, focusing on time and frequency of use, habits, and trends. We distribute our anonymized dataset to the community to ease reproducibility and allow further studies.Comment: Passive and Active Measurements Conference 2019 (PAM 2019). 14 pages, 7 figure