Multi-modal emotive computing in a smart house environment
We determine hazards within a smart house environment using an emotive computing framework. Representing a hazardous situation as an abnormal activity, we model normality using the concept of anxiety, using an agent based probabilistic approach. Interactions between a user and the environment are de...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier Science Inc
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/6323 |
| _version_ | 1848745043362316288 |
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| author | Moncrieff, Simon Venkatesh, Svetha West, Geoffrey Greenhill, Steward |
| author_facet | Moncrieff, Simon Venkatesh, Svetha West, Geoffrey Greenhill, Steward |
| author_sort | Moncrieff, Simon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We determine hazards within a smart house environment using an emotive computing framework. Representing a hazardous situation as an abnormal activity, we model normality using the concept of anxiety, using an agent based probabilistic approach. Interactions between a user and the environment are determined using multi-modal sensor data. The anxiety framework is a scalable, real-time approach that is able to incorporate data from a number of sources, or agents, and able to accommodate interleaving event sequences. In addition to using simple sensors, we introduce a method for using audio as a pervasive sensor indicating the presence of an activity. The audio data enabled the detection of activity when interactions between a user and a monitored device didn’t occur, successfully preventing false hazardous situations from being detected. We present results for a number of activity sequences, both normal and abnormal. |
| first_indexed | 2025-11-14T06:11:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-6323 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:11:05Z |
| publishDate | 2007 |
| publisher | Elsevier Science Inc |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-63232017-09-13T15:53:52Z Multi-modal emotive computing in a smart house environment Moncrieff, Simon Venkatesh, Svetha West, Geoffrey Greenhill, Steward We determine hazards within a smart house environment using an emotive computing framework. Representing a hazardous situation as an abnormal activity, we model normality using the concept of anxiety, using an agent based probabilistic approach. Interactions between a user and the environment are determined using multi-modal sensor data. The anxiety framework is a scalable, real-time approach that is able to incorporate data from a number of sources, or agents, and able to accommodate interleaving event sequences. In addition to using simple sensors, we introduce a method for using audio as a pervasive sensor indicating the presence of an activity. The audio data enabled the detection of activity when interactions between a user and a monitored device didn’t occur, successfully preventing false hazardous situations from being detected. We present results for a number of activity sequences, both normal and abnormal. 2007 Journal Article http://hdl.handle.net/20.500.11937/6323 10.1016/j.pmcj.2006.07.003 Elsevier Science Inc restricted |
| spellingShingle | Moncrieff, Simon Venkatesh, Svetha West, Geoffrey Greenhill, Steward Multi-modal emotive computing in a smart house environment |
| title | Multi-modal emotive computing in a smart house environment |
| title_full | Multi-modal emotive computing in a smart house environment |
| title_fullStr | Multi-modal emotive computing in a smart house environment |
| title_full_unstemmed | Multi-modal emotive computing in a smart house environment |
| title_short | Multi-modal emotive computing in a smart house environment |
| title_sort | multi-modal emotive computing in a smart house environment |
| url | http://hdl.handle.net/20.500.11937/6323 |