Inference of Activities with Unexpected Actions Using Pattern Mining

Book chapter

Nasreen, Shamila, Azam, Muhammad Awais, Naeem, U. and Ghazanfar, Mustansar Ali 2015. Inference of Activities with Unexpected Actions Using Pattern Mining. in: UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 1479-1488
AuthorsNasreen, Shamila, Azam, Muhammad Awais, Naeem, U. and Ghazanfar, Mustansar Ali
Abstract

Recognition of activities in an unobtrusive manner has attracted the attention of context aware systems, which provide end users with services based on everyday activities that are recognised without infringing the privacy of the end user. Current work has generally focused on applying a range of traditional classification and semantic reasoning based techniques in order to recognise these activities. However, the ability to recognise unexpected actions while the activity is being conducted remains a challenge. In this paper, we present an approach that is able to recognise activities regardless of the order of tasks/actions used to perform the activity. The proposed recognition framework extends an existing activity recognition approach by deploying a frequent pattern mining technique to find patterns among different streams of captured sensor events in order to increase the adaptive learning of the proposed recognition approach.

Book titleUbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers
Page range1479-1488
Year2015
PublisherAssociation for Computing Machinery (ACM)
Publication dates
Print07 Sep 2015
Publication process dates
Deposited09 Nov 2018
EventThe 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
ISBN978-1-4503-3575-1
Digital Object Identifier (DOI)https://doi.org/10.1145/2800835.2801619
Web address (URL)https://doi.org/10.1145/2800835.2801619
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