{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T21:54:26Z","timestamp":1755035666397,"version":"3.41.2"},"reference-count":15,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2007,12,31]],"date-time":"2007-12-31T00:00:00Z","timestamp":1199059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,4,1]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>People are subjected to a multitude of interruptions. In order to manage these interruptions it is imperative to predict a person's interruptability \u2013 his\/her current readiness or inclination to be interrupted. This paper aims to introduce the approach of direct interruptability inference from sensor streams (accelerometer and audio data) in a ubiquitous computing setup and to show that it provides highly accurate and robust predictions.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors argue that scenarios are central for evaluating the performance of ubiquitous computing devices (and interruptability predicting devices in particular) and prove this on the setup employed, which was based on that of Kern and Schiele.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The paper demonstrates that scenarios provide the foundation for avoiding misleading results, and provide the basis for a stratified scenario\u2010based learning model, which greatly speeds up the training of such devices.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>The direct prediction seems to be competitive or even superior to indirect prediction methods and no drawbacks have been observed yet.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper introduces a method for accurately predicting a person's interruptability directly from simple sensors without any intermediate steps\/symbols.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17427370710863149","type":"journal-article","created":{"date-parts":[[2008,4,5]],"date-time":"2008-04-05T07:18:29Z","timestamp":1207379909000},"page":"426-438","source":"Crossref","is-referenced-by-count":3,"title":["A scenario\u2010based approach for direct interruptability prediction on wearable devices"],"prefix":"10.1108","volume":"3","author":[{"given":"Abraham","family":"Bernstein","sequence":"first","affiliation":[]},{"given":"Peter","family":"Vorburger","sequence":"additional","affiliation":[]},{"given":"Patrice","family":"Egger","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020319445158500_b1","doi-asserted-by":"crossref","unstructured":"Bao, L. and Intille, S.S. (2004), \u201cActivity recognition from user\u2010annotated acceleration data\u201d, Proceedings of PERVASIVE 2004 (LNCS 3001), Springer\u2010Verlag, Berlin Heidelberg, pp. 1\u201017.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"key2022020319445158500_b2","doi-asserted-by":"crossref","unstructured":"Fogarty, J., Hudson, S.E. and Lai, J. (2004), \u201cExamining the robustness of sensor\u2010based statistical models of human interruptibility\u201d, Proceedings of the 2004 Conference on Human Factors in Computing Systems, ACM Press, Vienna, pp. 207\u201014.","DOI":"10.1145\/985692.985719"},{"key":"key2022020319445158500_b3","doi-asserted-by":"crossref","unstructured":"Gellersen, H.W., Schmidt, A. and Beigl, M. (2002), \u201cMulti\u2010sensor context\u2010awareness in mobile devices and smart artifacts\u201d, Mobile Networks and Applications, Vol. 7 No. 5, pp. 341\u201051.","DOI":"10.1023\/A:1016587515822"},{"key":"key2022020319445158500_b4","doi-asserted-by":"crossref","unstructured":"Grudin, J. (1994), \u201cGroupware and social dynamics: eight challenges for developers\u201d, Communications of the ACM, Vol. 37 No. 1, pp. 92\u2010105.","DOI":"10.1145\/175222.175230"},{"key":"key2022020319445158500_b5","doi-asserted-by":"crossref","unstructured":"Grudin, J. (2002), \u201cGroup dynamics and ubiquitous computing\u201d, Communications of the ACM, Vol. 45 No. 12, pp. 74\u20108.","DOI":"10.1145\/585597.585618"},{"key":"key2022020319445158500_b6","doi-asserted-by":"crossref","unstructured":"Horvitz, E. and Apacible, J. (2003), \u201cLearning and reasoning about interruption\u201d, ICMI '03: Proceedings of the 5th International Conference on Multimodal Interfaces, ACM Press, New York, NY, pp. 20\u20107.","DOI":"10.1145\/958432.958440"},{"key":"key2022020319445158500_b7","unstructured":"Horvitz, E., Koch, P., Kadie, C.M. and Jacobs, A. (2002), \u201cCoordinate: probabilistic forecasting of presence and availability\u201d, Proceedings of the Eighteenth Conference on Uncertainty and Artificial Intelligence (UAI '02), Vancouver, pp. 224\u201033."},{"key":"key2022020319445158500_b8","doi-asserted-by":"crossref","unstructured":"Hudson, S., Fogarty, J., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J. and Yang, J. (2003), \u201cPredicting human interruptibility with sensors: a wizard of oz feasibility study\u201d, Proceedings of the Conference on Human Factors in Computing Systems, ACM Press, Fort Lavderdale, FL, pp. 257\u201064.","DOI":"10.1145\/642611.642657"},{"key":"key2022020319445158500_b9","unstructured":"Kern, N. and Schiele, B. (2003), \u201cContext\u2010aware notification for wearable computing\u201d, Proceedings of the 7th International Symposium on Wearable Computing, New York, October, pp. 223\u201030."},{"key":"key2022020319445158500_b10","doi-asserted-by":"crossref","unstructured":"Pfeifer, R. and Scheier, C. (2000), Understanding Intelligence, MIT Press, Cambridge, MA.","DOI":"10.7551\/mitpress\/6979.001.0001"},{"key":"key2022020319445158500_b11","doi-asserted-by":"crossref","unstructured":"Provost, F.J. and Fawcett, T. (2001), \u201cRobust classification for imprecise environments\u201d, Machine Learning, Vol. 42 No. 3, pp. 203\u201031.","DOI":"10.1023\/A:1007601015854"},{"key":"key2022020319445158500_b12","doi-asserted-by":"crossref","unstructured":"Sawhney, N. and Schmandt, C. (1999), \u201cNomadic radio: scaleable and contextual notification for wearable audio messaging\u201d, CHI '99: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York, NY, pp. 96\u2010103.","DOI":"10.1145\/302979.303005"},{"key":"key2022020319445158500_b13","unstructured":"Siewiorek, D., Smailagic, A., Furukawa, J., Krause, A., Moraveji, N., Reiger, K., Shaffer, J. and Wong, F.L. (2003), \u201cSensay: a context\u2010aware mobile phone\u201d, ISWC '03: Proceedings of the 7th IEEE International Symposium on Wearable Computers, IEEE Computer Society, Washington, DC, p. 248."},{"key":"key2022020319445158500_b14","unstructured":"Syrj\u00e4l\u00e4, J. (2003), \u201cContext classification using audio data for wearable computer\u201d, Master's thesis, Swiss Federal Institute of Technology, Zurich."},{"key":"key2022020319445158500_b15","doi-asserted-by":"crossref","unstructured":"Ting, K.M. and Low, B.T. (1997), \u201cModel combination in the multiple\u2010databatches scenario\u201d, ECML '97: Proceedings of the 9th European Conference on Machine Learning, Springer\u2010Verlag, London, pp. 250\u201065.","DOI":"10.1007\/3-540-62858-4_90"}],"container-title":["International Journal of Pervasive Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/17427370710863149","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/17427370710863149\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/17427370710863149\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T00:22:21Z","timestamp":1753402941000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijpcc\/article\/3\/4\/426-438\/444587"}},"subtitle":[],"editor":[{"given":"Wathiq","family":"Mansoor","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2007,12,31]]},"references-count":15,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2008,4,1]]}},"alternative-id":["10.1108\/17427370710863149"],"URL":"https:\/\/doi.org\/10.1108\/17427370710863149","relation":{},"ISSN":["1742-7371"],"issn-type":[{"type":"print","value":"1742-7371"}],"subject":[],"published":{"date-parts":[[2007,12,31]]}}}