A model for human interruptability: experimental evaluation and automatic 
estimation from wearable sensors

Kern, N; Antifakos, S; Schiele, B; Schwaninger, A

doi:10.1109/ISWC.2004.3

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

A model for human interruptability: experimental evaluation and automatic estimation from wearable sensors

MPS-Authors

/persons/resource/persons84420

Schwaninger, A
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1364705
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Kern, N., Antifakos, S., Schiele, B., & Schwaninger, A. (2004). A model for human interruptability: experimental evaluation and automatic estimation from wearable sensors. In Eighth IEEE International Symposium on Wearable Computers (ISWC 2004) (pp. 158-165). Los Alamitos, CA, USA: IEEE Computer Society.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D767-6

Abstract

For the estimation of user interruptability in wearable and mobile settings, we propose in [Context-aware notfication for wearable computing] to distinguish between the users' personal and social interruptability. In this paper, we verify this thesis with a user study on 24 subjects. Results show that there is a significant difference between social and personal interruptability. Further, we present a novel approach to estimate the social and personal interruptability of a user from wearable sensors. It is scalable for a large number of sensors, contexts, and situations and allows for online adaptation during run-time. We have developed a wearable platform, that allows to record and process the data from a microphone, 12 body-worn 3D acceleration sensors, and a location estimation. We have evaluated the approach on three different data sets, with a maximal length of two days.