Employing entropy measures to identify visitors in multi-occupancy environments

Howedi, A., Lotfi, A. ORCID: 0000-0002-5139-6565 and Pourabdollah, A. ORCID: 0000-0001-7737-1393, 2020. Employing entropy measures to identify visitors in multi-occupancy environments. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

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Abstract

Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

Item Type: Journal article
Publication Title: Journal of Ambient Intelligence and Humanized Computing
Creators: Howedi, A., Lotfi, A. and Pourabdollah, A.
Publisher: Springer
Date: 22 December 2020
ISSN: 1868-5137
Identifiers:
NumberType
10.1007/s12652-020-02824-zDOI
1397279Other
Rights: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 05 Jan 2021 13:57
Last Modified: 31 May 2021 15:07
URI: http://irep.ntu.ac.uk/id/eprint/41948

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