Activity discovering and modelling with labelled and unlabelled data in smart environments

Wen, Jiahui and Zhong, Mingyang (2015) Activity discovering and modelling with labelled and unlabelled data in smart environments. Expert Systems with Applications, 42 14: 5800-5810. doi:10.1016/j.eswa.2015.04.005

Author Wen, Jiahui
Zhong, Mingyang
Title Activity discovering and modelling with labelled and unlabelled data in smart environments
Journal name Expert Systems with Applications   Check publisher's open access policy
ISSN 0957-4174
Publication date 2015-08-15
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.eswa.2015.04.005
Open Access Status Not Open Access
Volume 42
Issue 14
Start page 5800
End page 5810
Total pages 11
Place of publication Kidlington, Oxford United Kingdom
Publisher Pergamon Press
Collection year 2016
Language eng
Abstract In the past decades, activity recognition had aroused great interest for the community of context-awareness computing and human behaviours monitoring. However, most of the previous works focus on supervised methods in which the data labelling is known to be time-consuming and sometimes error-prone. In addition, due to the randomness and erratic nature of human behaviours in realistic environments, supervised models trained with data from certain subject might not be scaled to others. Further more, unsupervised methods, with little knowledge about the activities to be recognised, might result in poor performance and high clustering overhead. To this end, we propose an activity recognition model with labelled and unlabelled data in smart environments. With small amount of labelled data, we discover activity patterns from unlabelled data based on proposed similarity measurement algorithm. Our system does not require large amount of data to be labelled while the proposed similarity measurement method is effective to discover length-varying, disordered and discontinuous activity patterns in smart environments. Therefore, our methods yield comparable performance with much less labelled data when compared with traditional supervised activity recognition, and achieve higher accuracy with lower clustering overhead compared with unsupervised methods. The experiments based on real datasets from the smart environments demonstrate the effectiveness of our method, being able to discover more than 90% of original activities from the unlabelled data, and the comparative experiments show that our methods are capable of providing a better trade-off, regarding the accuracy, overhead and labelling efforts, between the supervised and unsupervised methods.
Keyword Data mining
Machine learning
Activity recognition
Similarity measurement
Labelled and unlabelled data
Smart environments
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
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