Identifying and visualizing relevant deviations in longitudinal sensor patterns for care professionals

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Abstract

Sensor technology is increasingly applied for the purpose of monitoring elderly’s Activities of Daily Living (ADL), a set of activities used by physicians to benchmark physical and cognitive decline. Visualizing deviations in ADL can help medical specialists and nurses to recognize disease symptoms at an early stage. This paper presents possible visualizations for identifying such deviations. These visualizations have been iteratively explored and developed with three different medical specialists to better understand which deviations are relevant according to the different medical specialisms and explore how these deviations should be best presented. The study results suggest that the participants found a monthly bar graph in which activities are represented by colours as the most suitable from the ones presented. Although the visualizations of every ADL was found to be more or less relevant by the different medical specialists, the preference for focusing on specific ADL’s varied from specialist to specialist.
Original languageEnglish
Title of host publicationPervasiveHealth '13
Subtitle of host publicationProceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
PublisherInstitute for Computer Sciences, Social-Informatics and Telecommunications Engineering
Publication statusPublished - 2013

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