Abstract
MyHealthAvatar is a project designed to collect lifestyle and health data to promote citizen’s wellbeing. As a lifetime companion of citizens the amount of data to be collected is large. It is almost impossible for citizens, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. Visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3d avatar, dashboard, diary, timeline, clock view and map. These components can be used cooperatively to achieve flexible visual analysis of spatial temporal lifestyle and health data.
Highlights
The MyHealthAvatar project [1] is a research project through which the feasibility of an innovative representation of the health status of citizens for future healthcare – 4D MyHealthAvatar, is studied
As a project focusing on health and lifestyle data collection, access, analysis and sharing, without proper visualisation, it is not possible for the user to select, view, understand and gain knowledge from a large collection of data
Visualisation and visual analysis is a critical part for the effective uitilisation of data collected and stored on the MyHealthAvatar platform
Summary
The MyHealthAvatar project [1] is a research project through which the feasibility of an innovative representation of the health status of citizens for future healthcare – 4D MyHealthAvatar, is studied. A 4D avatar is a unique interface that allows data access, collection, sharing and analysis by utilising modern ICT technology, overcoming the shortcomings of the existing resources in Europe, which are highly fragmented. It will become the citizen’s lifelong companion, providing long-term and consistent health status information of the individual citizen along a timeline representing the citizen’s life, starting from birth. Performing visually assisted data analysis (i.e. visual analytics) to extract clinically meaningful information from the heterogeneous data of individual/shared avatars, such as the patterns of symptoms, experience of treatments, medicines, selfcare guidelines, risk factors etc. Semantics and linked data to support the data/model searching and reasoning
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