A hybrid approach to recognising activities of daily living from object use in the home environment

Ihianle, I. ORCID: 0000-0001-7445-8573, Naeem, U., Islam, S. and Tawil, A.-R., 2018. A hybrid approach to recognising activities of daily living from object use in the home environment. Informatics, 5 (1): 6. ISSN 2227-9709

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Abstract

Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques.

Item Type: Journal article
Publication Title: Informatics
Creators: Ihianle, I., Naeem, U., Islam, S. and Tawil, A.-R.
Publisher: MDPI AG
Date: 2018
Volume: 5
Number: 1
ISSN: 2227-9709
Identifiers:
NumberType
10.3390/informatics5010006DOI
1314861Other
Rights: c 2018 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 (http://creativecommons.org/licenses/by/4.0/).
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 15 Apr 2020 08:32
Last Modified: 15 Apr 2020 08:32
URI: http://irep.ntu.ac.uk/id/eprint/39612

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