Activities of daily life recognition using process representation modelling to support intention analysis

Article

Naeem, U., Bashroush, R., Anthony, Richard, Azam, Muhammad Awais, Tawil, Abdel Rahman, Lee, S. and Mou-Ling, Dennis 2015. Activities of daily life recognition using process representation modelling to support intention analysis. International Journal of Pervasive Computing and Communications. 11 (3), pp. 347-371. https://doi.org/10.1108/IJPCC-01-2015-0002
AuthorsNaeem, U., Bashroush, R., Anthony, Richard, Azam, Muhammad Awais, Tawil, Abdel Rahman, Lee, S. and Mou-Ling, Dennis
Abstract

Purpose
– This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge.

Design/methodology/approach
– This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients.

Findings
– A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.

Originality/value
– The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features.

JournalInternational Journal of Pervasive Computing and Communications
Journal citation11 (3), pp. 347-371
ISSN1742-7371
Year2015
PublisherEmerald
Accepted author manuscript
Digital Object Identifier (DOI)https://doi.org/10.1108/IJPCC-01-2015-0002
Publication dates
Print07 Sep 2015
Publication process dates
Deposited27 Oct 2015
Copyright information© 2015 Emerald
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Walcott, Terry H., Palmer-Brown, Dominic, Williams, Godfried, Mouratidis, Haralambos and Lee, S. 2007. An assessment of neural network algorithms that could aid SME survival. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference University of East London pp. 120-127
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Lee, S. and Palmer-Brown, Dominic 2007. Feature discovery using snap-drift neural networks. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference University of East London pp. 61-70
A Comparison of Two Hidden Markov Approaches to Task Identification in the Home Environment
Naeem, U. and Bigham, John 2007. A Comparison of Two Hidden Markov Approaches to Task Identification in the Home Environment. Proceedings of the 2nd International Conference on Pervasive Computing and Applications, Birmingham, UK, 2007, pp. 383-388
Recognising Activities of Daily Life Using Hierarchical Plans
Naeem, U., Bigham, John and Wang, Jinfu 2007. Recognising Activities of Daily Life Using Hierarchical Plans. Proceedings of the 2nd European Conference on Smart Sensing and Context, LNCS 4793, Lake District, UK, 2007, pp. 175-189
A Hierarchal Approach to Activity Recognition in the Home Environment based on Object Usage
Naeem, U. and Bigham, John 2008. A Hierarchal Approach to Activity Recognition in the Home Environment based on Object Usage. Proceedings of the 2008 Networking and Electronic Commerce Research Conference (NAEC 2008), Lake Garda, Italy, 2008, pp. 48-54
Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home
Naeem, U. and Bigham, John 2009. Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home. Proceedings of the 3rd International Conference on Pervasive Computing Technologies for Healthcare. Technologies to Counter Cognitive Decline Workshop London
Modal Learning in a Neural Network
Lee, S. and Palmer-Brown, Dominic 2006. Modal Learning in a Neural Network. Proceedings of the AC&T, pp. 42-47
Performance-guided Neural Network for Self-Organising Network Management
Lee, S., Palmer-Brown, Dominic, Tepper, Jonathan and Roadknight, Christopher 2002. Performance-guided Neural Network for Self-Organising Network Management. Proceedings of London Communication Symposium (LCS'2002) University College London, London, UK, 9th – 10th September, pp. 269 - 272
Fast Learning Neural Nets with Adaptive Learning Styles
Palmer-Brown, Dominic, Lee, S., Tepper, Jonathan and Roadknight, Chris 2003. Fast Learning Neural Nets with Adaptive Learning Styles.
Snap-Drift: Real-time, Performance-guided Learning
Lee, S., Palmer-Brown, Dominic, Tepper, Jonathan and Roadknight, Christopher 2003. Snap-Drift: Real-time, Performance-guided Learning.
Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks
Palmer-Brown, Dominic and Lee, S. 2005. Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks. International Journal on Simulation: Systems, Science and Technology. 6 (9), pp. 11-21.
The Analysis of Network Manager’s Behaviour using a Self-Organising Neural Networks
Palmer-Brown, Dominic and Lee, S. 2005. The Analysis of Network Manager’s Behaviour using a Self-Organising Neural Networks. International Journal on Simulation: Systems, Science and Technology. 6 (9), pp. 22-32.
Phonetic Feature Discovery in Speech using Snap-Drift
Lee, S. and Palmer-Brown, Dominic 2006. Phonetic Feature Discovery in Speech using Snap-Drift.
Challenges in the Application of Feature Modelling in Fixed Line Telecommunications
Gillan, Charles, Kilpatrick, Peter, Spence, Ivor, Brown, T. John, Bashroush, R. and Gawley, Rachel 2007. Challenges in the Application of Feature Modelling in Fixed Line Telecommunications. Proceedings of the First International Workshop on Variability Modelling of Software-intensive Systems (VaMoS 2007), Lemrick, Ireland, Jan 16 -18, 2007. C Gillan
Activity Recognition using a Hierarchical Framework
Naeem, U. and Bigham, John 2008. Activity Recognition using a Hierarchical Framework. Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare, Ambient Technologies for Diagnosing and Monitoring Chronic Patients Workshop, Tampere, Finland, 2008, IEEE pp. 24-27
Early SME Market Prediction using USDNN
Walcott, Terry H., Palmer-Brown, Dominic and Lee, S. 2008. Early SME Market Prediction using USDNN. in: Proceedings of the International Conference of Computational Intelligence and Intelligent Systems (ICCIIS'2008) International Association of Engineers.
Using a Software Product Line Approach in Designing Grid Services
Bashroush, R. and Perrott, Ronald 2005. Using a Software Product Line Approach in Designing Grid Services. 4th UK e-Science All Hands Meeting (AHM2005). Nottingham, UK Sep 2005
Requirements Modelling and Design Notations for Software Product Lines
Brown, T. John, Gawley, Rachel, Spence, Ivor, Kilpatrick, Peter, Gillan, Charles and Bashroush, R. 2007. Requirements Modelling and Design Notations for Software Product Lines. Proceedings of the First International Workshop on Variability Modelling of Software-intensive Systems (VaMoS2007), Lemrick, Ireland, Jan 16-18, 2007. T J. Brown
A Neural Network Approach for Intrusion Detection Systems
Beqiri, Elidon, Lee, S. and Draganova, Chrisina 2010. A Neural Network Approach for Intrusion Detection Systems. 5th Conference in Advances in Computing and Technology (London, United Kingdom, 27th Jan), pp. 209 -217
Diagnostic Feedback by Snap-drift Question Response Grouping
Lee, S., Palmer-Brown, Dominic and Draganova, Chrisina 2008. Diagnostic Feedback by Snap-drift Question Response Grouping. in: Proceedings of 9th WSEAS International Conference on Neural Networks (NN'08) Stevens Point (WI), USA World Scientific and Engineering Academy and Society. pp. 208-214
Modal Learning Neural Networks
Palmer-Brown, Dominic, Lee, S., Draganova, Chrisina and Kang, Miao 2009. Modal Learning Neural Networks.
Snap-Drift Neural Network for Selecting Student Feedback
Palmer-Brown, Dominic, Draganova, Chrisina and Lee, S. 2009. Snap-Drift Neural Network for Selecting Student Feedback. International Joint Conference on Neural Networks, IJCNN 2009. Atlanta, Georgia, USA 14 - 19 Jun 2009 IEEE.