Next Article in Journal
Internet of Tangible Things (IoTT): Challenges and Opportunities for Tangible Interaction with IoT
Previous Article in Journal
Acknowledgement to Reviewers of Informatics in 2017
Article Menu

Export Article

Open AccessArticle
Informatics 2018, 5(1), 6; doi:10.3390/informatics5010006

A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment

School of Architecture, Computing and Engineering (ACE), University of East London, London E16 2RD, UK
School of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UK
Author to whom correspondence should be addressed.
Received: 13 December 2017 / Revised: 5 January 2018 / Accepted: 10 January 2018 / Published: 13 January 2018
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
View Full-Text   |   Download PDF [791 KB, uploaded 13 January 2018]   |  


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. View Full-Text
Keywords: activity recognition; topic model; ontology model; Latent Dirichlet Allocation activity recognition; topic model; ontology model; Latent Dirichlet Allocation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ihianle, I.K.; Naeem, U.; Islam, S.; Tawil, A.-R. A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment. Informatics 2018, 5, 6.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Informatics EISSN 2227-9709 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top