Publikation: Fingerprints : detecting meaningful moments for mobile health intervention
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Personalized and contextual interventions are promising techniques for mobile persuasive technologies in mobile health. In this paper, we propose the "fingerprints" technique to analyze the users' daily behavior patterns to find the meaningful moments to better support mobile persuasive technologies, especially mobile health interventions. We assume that for many persons, their behaviors have patterns and can be detected through the sensor data from smartphones. We develop a three-step interactive machine learning workflow to describe the concept and approach of the "fingerprints" technique. By this we aim to implement a practical and light-weight mobile intervention system without burdening the users with manual logging. In our feasibility study, we show results that provide first insights into the design of the "fingerprints" technique.
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WANG, Yunlong, Le DUAN, Simon BUTSCHER, Jens MÜLLER, Harald REITERER, 2016. Fingerprints : detecting meaningful moments for mobile health intervention. MobileHCI '16 : 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. Florence, Italy, 6. Sept. 2016 - 9. Sept. 2016. In: MobileHCI '16 : Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. New York, NY: ACM, 2016, pp. 1085-1088. ISBN 978-1-4503-4413-5. Available under: doi: 10.1145/2957265.2965006BibTex
@inproceedings{Wang2016Finge-38189, year={2016}, doi={10.1145/2957265.2965006}, title={Fingerprints : detecting meaningful moments for mobile health intervention}, isbn={978-1-4503-4413-5}, publisher={ACM}, address={New York, NY}, booktitle={MobileHCI '16 : Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct}, pages={1085--1088}, author={Wang, Yunlong and Duan, Le and Butscher, Simon and Müller, Jens and Reiterer, Harald} }
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