Fusion of unobtrusive sensing solutions for home-based activity recognition and classification using data mining models and methods

Ekerete, I, Garcia-Constantino, M, Konios, A ORCID: https://orcid.org/0000-0001-5281-1911, Mustafa, MA, Diaz-Skeete, Y, Nugent, C and McLaughlin, J, 2021. Fusion of unobtrusive sensing solutions for home-based activity recognition and classification using data mining models and methods. Applied Sciences, 11 (19): 9096. ISSN 2076-3417

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Abstract

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) twearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.

Item Type:	Journal article
Publication Title:	Applied Sciences
Creators:	Ekerete, I., Garcia-Constantino, M., Konios, A., Mustafa, M.A., Diaz-Skeete, Y., Nugent, C. and McLaughlin, J.
Publisher:	MDPI AG
Date:	29 September 2021
Volume:	11
Number:	19
ISSN:	2076-3417
Identifiers:	Number Type 10.3390/app11199096 DOI 1618415 Other
Rights:	© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	14 Nov 2022 16:59
Last Modified:	14 Nov 2022 16:59
URI:	https://irep.ntu.ac.uk/id/eprint/47394

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