University of Oulu

Aku Visuri, Niels van Berkel, Chu Luo, Jorge Goncalves, Denzil Ferreira, and Vassilis Kostakos. 2017. Predicting interruptibility for manual data collection: a cluster-based user model. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '17). ACM, New York, NY, USA, Article 12, 14 pages. DOI: https://doi.org/10.1145/3098279.3098532

Predicting interruptibility for manual data collection : a cluster-based user model

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Author: Visuri, Aku1; Ferreira, Denzil1; van Berkel, Niels2;
Organizations: 1Center for Ubiquitous Computing, University of Oulu
2The University of Melbourne
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201902195404
Language: English
Published: Association for Computing Machinery, 2017
Publish Date: 2019-02-19
Description:

Abstract

Previous work suggests that Quantified-Self applications can retain long-term usage with motivational methods. These methods often require intermittent attention requests with manual data input. This may cause unnecessary burden to the user, leading to annoyance, frustration and possible application abandonment. We designed a novel method that uses on-screen alert dialogs to transform recurrent smartphone usage sessions into moments of data contributions and evaluate how accurately machine learning can reduce unintended interruptions. We collected sensor data from 48 participants during a 4-week long deployment and analysed how personal device usage can be considered in scheduling data inputs. We show that up to 81.7% of user interactions with the alert dialogs can be accurately predicted using user clusters, and up to 75.5% of unintended interruptions can be prevented and rescheduled. Our approach can be leveraged by applications that require self-reports on a frequent basis and may provide a better longitudinal QS experience.

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ISBN Print: 9781450350754
Article number: 12
DOI: 10.1145/3098279.3098532
OADOI: https://oadoi.org/10.1145/3098279.3098532
Host publication: MobileHCI '17. Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. Vienna, Austria Sept. 4-, 2017
Conference: International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI)
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work is partially funded by the Academy of Finland (Grants 276786-AWARE, 286386-CPDSS, 285459-iSCIENCE, 304925-CARE), the European Commission (Grant 6AIKA-A71143-AKAI), and Marie Skłodowska-Curie Actions (645706-GRAGE).
Academy of Finland Grant Number: 276786
286386
285459
304925
Detailed Information: 276786 (Academy of Finland Funding decision)
286386 (Academy of Finland Funding decision)
285459 (Academy of Finland Funding decision)
304925 (Academy of Finland Funding decision)
Copyright information: © 2017 Copyright is held by the owner/author(s). | ACM 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MobileHCI '17. Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. Vienna, Austria Sept. 4-, 2017, http://dx.doi.org/10.1145/3098279.3098532.