We introduce the novel research problem of task recognition in daily life.We recognize tasks such as project management, planning, meal-breaks, communication, documentation, and family care. We capture Cyber, Physical, and Social (CSP) activities of 17 participants over four weeks using device-based sensing, app activity logging, and an experience sampling methodology. Our cohort includes students, casual workers, and professionals, forming the first realworld context-rich task behaviour dataset. We model CPS activities across different task categories, results highlight the importance of considering the CPS feature sets in modelling, especially workrelated tasks.
@inproceedings{DBLP:conf/mir/LionoRSRSSTSBW20,
author = {Jonathan Liono and
Mohammad Saiedur Rahaman and
Flora D. Salim and
Yongli Ren and
Damiano Spina and
Falk Scholer and
Johanne R. Trippas and
Mark Sanderson and
Paul N. Bennett and
Ryen W. White},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/mir/LionoRSRSSTSBW20.bib},
booktitle = {Proceedings of the 2020 on International Conference on Multimedia
Retrieval, ICMR 2020, Dublin, Ireland, June 8-11, 2020},
doi = {10.1145/3372278.3390703},
editor = {Cathal Gurrin and
Björn Þór Jónsson and
Noriko Kando and
Klaus Schöffmann and
Yi-Ping Phoebe Chen and
Noel E. O'Connor},
pages = {472--478},
publisher = {ACM},
timestamp = {Sun, 14 Jun 2020 12:18:46 +0200},
title = {Intelligent Task Recognition: Towards Enabling Productivity Assistance
in Daily Life},
url = {https://doi.org/10.1145/3372278.3390703},
year = {2020}
}
© 2021 Flora Salim - CRUISE Research Group.