A cognitive model for recognizing human behaviours in smart homes

Mastrogiovanni, Fulvio; Sgorbissa, Antonio; Zaccaria, Renato

doi:10.1007/s12243-010-0171-5

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Roy et al (2010) discussed an initial framework of activity recognition based on possibility theory and description logic (DL). Mastrogiovanni, Sgorbissa, and Zaccaria (2010) integrated ontology and logic based approaches for context representation and recognition to map numerical data to symbolic representations. Chen and Nugent (2010) discussed the concept of semantically enhanced situation awareness for activity of daily living (ADL) assistance.…”

Section: Introductionmentioning

confidence: 99%

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

Ray

Leng

McGinnity

et al. 2013

Proceedings of the 5th International Joint Conference on Computational Intelligence

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A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoning in a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

Ray

Leng

McGinnity

et al. 2013

Proceedings of the 5th International Joint Conference on Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Wang et al (2005) proposed a context-aware system, CASSHA (Context-Aware System for Smart Home Applications), for processing, representation, provision, and coordination of smart home applications. Mastrogiovanni et al (2010) have integrated ontology and logic based approaches to context representation and recognition to map numerical data to symbolic representations. A resource-aware smart home management system is reported by Son et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Development of Cognitive Capabilities for Smart Home using a Self-Organizing Fuzzy Neural Network

Ray

Leng

McGinnity

et al. 2012

IFAC Proceedings Volumes

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Exploring the effect of a user’s personality traits on tactile communication with a robot using Bayesian networks

Hwang

Lee

2015

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Because robots are physically embodied agents, touch is one of the important modalities through which robots communicate with humans. Among the several factors that affect human-robot interaction, this research focuses on the effect of a user’s personality traits on tactile interactions with a robot. Participants interacted freely with a robot and their tactile interaction patterns were analyzed. Several classifiers were used to examine the effect of a participant’s degree of extroversion on tactile communication patterns with the robot and our results showed that a user’s personality traits affected the way in which they interacted with the robot. Specifically, important features of Bayesian networks, such as the Markov blanket and what-if/goal-seeking power were tested and showed the effect of personality on tactile interaction with respect to where and how participants touched the robot. We also found that, by using Bayesian network classifiers, a user’s personality traits can be inferred based on tactile communication patterns.

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A cognitive model for recognizing human behaviours in smart homes

Cited by 4 publications

References 25 publications

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

Development of Cognitive Capabilities for Smart Home using a Self-Organizing Fuzzy Neural Network

Exploring the effect of a user’s personality traits on tactile communication with a robot using Bayesian networks

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