Exploring entropy measurements to identify multi-occupancy in activities of daily living

Howedi, A., Lotfi, A. ORCID: 0000-0002-5139-6565 and Pourabdollah, A. ORCID: 0000-0001-7737-1393, 2019. Exploring entropy measurements to identify multi-occupancy in activities of daily living. Entropy, 21 (4): 416. ISSN 1099-4300

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Abstract

Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.

Item Type: Journal article
Publication Title: Entropy
Creators: Howedi, A., Lotfi, A. and Pourabdollah, A.
Publisher: MDPI
Date: 20 April 2019
Volume: 21
Number: 4
ISSN: 1099-4300
Identifiers:
NumberType
10.3390/e21040416DOI
e21040416Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 24 Apr 2019 08:00
Last Modified: 24 Apr 2019 08:00
URI: http://irep.ntu.ac.uk/id/eprint/36335

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