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Erschienen in:
Mobile-Application Based Cognitive Behavior Therapy (CBT) for Identifying and Managing Depression and Anxiety Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

User-Driven Semantic Classification for the Analysis of Abstract Health and Visualization Tasks

verfasst von : Sabine Theis, Peter Rasche, Christina Bröhl, Matthias Wille, Alexander Mertens

Erschienen in: Digital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety

Verlag: Springer International Publishing

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Abstract

Present article outlines characteristics of a general task analysis in terms of digital health visualization evaluation and design. Furthermore, a number of methodological approaches are discussed. One example, in which a hierarchical structure was empirically built with semantic classification by 98 users, will be discussed together with the expected benefits of its successful implementation with respect to system development and human factors research on health data visualizations. It is concluded that experimental approaches to taxonomy construction offer considerable promise in capturing tasks which are relevant but that further investigation is needed validating and iteratively extending the abstract task structures. We thus recommend based on our experiences to conduct a combination of semantic classification with users and hierarchical task analysis to capture all needed task abstraction levels.

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Vorheriges Kapitel Enhance the Use of Medical Wearables Through Meaningful Data Analytics
Nächstes Kapitel EEG Features Extraction and Classification of Rifle Shooters in the Aiming Period
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Metadaten
Titel
User-Driven Semantic Classification for the Analysis of Abstract Health and Visualization Tasks
verfasst von
Sabine Theis
Peter Rasche
Christina Bröhl
Matthias Wille
Alexander Mertens
Copyright-Jahr
2017
Buch
Digital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety

Print ISBN: 978-3-319-58465-2

Electronic ISBN: 978-3-319-58466-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-58466-9_27

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