Visual Analytics for Health Monitoring and Risk Management in CARRE

Zhao, Youbing; Parvinzamir, Farzad; Wei, Hui; Liu, Enjie; Deng, Zhikun; Dong, Feng; Third, Allan; Lukoševičius, Arūnas; Marozas, Vaidotas; Kaldoudi, Eleni and Clapworthy, Gordon (2016). Visual Analytics for Health Monitoring and Risk Management in CARRE. E-Learning and Games; 10th International Conference, Edutainment 2016, Hangzhou, China, April 14-16, 2016, Revised Selected Papers, 9654 pp. 380–391.

DOI: https://doi.org/10.1007/978-3-319-40259-8_33

Abstract

With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people’s health in such a consistent way that the sensors may become people’s lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people’s health.

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