Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

    Research output: Contribution to journalArticle

    • Youbing Zhao
    • Farzad Parvinzamir
    • Stephen Wilson
    • Hui Wei
    • Zhikun Deng
    • Nick Portokallidis
    • Allan Third
    • George Drosatos
    • Enjie Liu
    • Feng Dong
    • Vaidotas Marozas
    • Arūnas Lukoševičius
    • Eleni Kaldoudi
    • Gordon Clapworthy

    View graph of relations

    Patient empowerment delivers health and social care services that enable people to gain more control of their healthcare needs. With the advancement of sensor technologies, it is increasingly possible to monitor people’s health with dedicated wearable sensors. The consistent measurements from a variety of wearable sensors imply that a huge amount of data may be exploited to monitor and predict people’s health using medically proven models. In the process of health data representation and analysis, visualisation can be employed to promote data analysis and knowledge discovery via mature visual paradigms and well-designed user interactions. In this paper, we introduce the role of visualisation for individualised health monitoring and risk management in the background of a European Commission funded project, which aims to provide self-management of cardiorenal diseases with the assistance of wearable sensors. The visualisation components of health monitoring, risk model exploration, and risk analysis are presented to achieve personalised health and risk monitoring and to promote people’s wellbeing. It allows the patients not only to view existing risks, but also to gain awareness of the right pathway to change their lifestyles in order to reduce potential health risks.
    Original languageEnglish
    Pages (from-to)405-413
    JournalJournal of Visualization
    Journal publication date01 May 2017
    Issue number2
    Early online date27 Oct 2016
    Publication statusPublished - 01 May 2017

      Research areas

    • Health monitoring, Patient empowerment, Risk assessment, Visualisation, Wearable sensor

    ID: 150620340