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Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors

Authors:

Manuela Züger,

Sebastian C. Müller,

André N. Meyer,

Thomas FritzAuthors Info & Claims

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Paper No.: 591, Pages 1 - 14

https://doi.org/10.1145/3173574.3174165

Published: 21 April 2018 Publication History

Abstract

Knowledge workers experience many interruptions during their work day. Especially when they happen at inopportune moments, interruptions can incur high costs, cause time loss and frustration. Knowing a person's interruptibility allows optimizing the timing of interruptions and minimize disruption. Recent advances in technology provide the opportunity to collect a wide variety of data on knowledge workers to predict interruptibility. While prior work predominantly examined interruptibility based on a single data type and in short lab studies, we conducted a two-week field study with 13 professional software developers to investigate a variety of computer interaction, heart-, sleep-, and physical activity-related data. Our analysis shows that computer interaction data is more accurate in predicting interruptibility at the computer than biometric data (74.8% vs. 68.3% accuracy), and that combining both yields the best results (75.7% accuracy). We discuss our findings and their practical applicability also in light of collected qualitative data.

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Index Terms

Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors
1. Human-centered computing

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cover image ACM Conferences

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

April 2018

8489 pages

ISBN:9781450356206

DOI:10.1145/3173574

General Chairs:
Regan Mandryk
University of Saskatchewan, Canada
,
Mark Hancock
University of Waterloo, Canada
,
Program Chairs:
Mark Perry
Brunel University London, UK
,
Anna Cox
University College London, UK

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Park ELee DHan YDiefendorff JLee U(2024)Hide-and-seek: Detecting Workers' Emotional Workload in Emotional Labor Contexts Using Multimodal SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785938:3(1-28)Online publication date: 9-Sep-2024
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