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From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes

Rafferty, Joseph, Nugent, Chris, Liu, Jun and Chen, Liming (2017) From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes. IEEE Transactions on Human-Machine Systems, PP (99). pp. 1-12. [Journal article]

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DOI: 10.1109/THMS.2016.2641388


The global population is aging; projections show that by 2050, over 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive Smart Homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive Smart Homes. The devised approach is based upon an Intention Recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the Intention Recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities.

Item Type:Journal article
Keywords:Activity recognition;ambient-assisted living (AAL);goal recognition;intelligent agents;intention recognition (IR);smart homes (SHs)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:36626
Deposited By: Mr Joseph Rafferty
Deposited On:09 Jan 2017 16:32
Last Modified:17 Oct 2017 16:27

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