Citation

Chua, Sook Ling and Foo, Lee Kien and Guesgen, Hans W. and Marsland, Stephen (2022) Incremental Learning of Human Activities in Smart Homes. Sensors, 22 (21). p. 8458. ISSN 1424-8220

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Abstract

first_pagesettingsOrder Article Reprints Open AccessArticle Incremental Learning of Human Activities in Smart Homes by Sook-Ling Chua 1,*ORCID,Lee Kien Foo 1,Hans W. Guesgen 2 andStephen Marsland 3ORCID 1 Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia 2 School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand 3 School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand * Author to whom correspondence should be addressed. Sensors 2022, 22(21), 8458; https://doi.org/10.3390/s22218458 Received: 16 October 2022 / Revised: 25 October 2022 / Accepted: 31 October 2022 / Published: 3 November 2022 (This article belongs to the Special Issue Human Activity Recognition in Smart Sensing Environment) Download Browse Figures Versions Notes Abstract Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Item Type:	Article
Uncontrolled Keywords:	Incremental learning, prediction by partial matching, novelty detection, activity recognition, smart homes
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Jan 2023 03:13
Last Modified:	04 Jan 2023 03:13
URII:	http://shdl.mmu.edu.my/id/eprint/10743

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