Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors
Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sens...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society Press
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/11457 |
| _version_ | 1848747811101736960 |
|---|---|
| author | Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha |
| author2 | M Palaniswami |
| author_facet | M Palaniswami Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha |
| author_sort | Tran, Dung |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sensors for improving recognition performance. The proposed FS-AHMM is an extension of the Abstract Hidden Markov Model which applies the concept of factored state representations to compactly represent the state transitions. The parameters of the FS-AHMM are estimated using the EM algorithm from the data acquired through multiple multi-modal sensors and cameras. The model is evaluated and compared with other existing models on real-world data. The results show that the proposed model outperforms other models and that the integrated sensor information helps in recognizing activity more accurately. |
| first_indexed | 2025-11-14T06:55:04Z |
| format | Conference Paper |
| id | curtin-20.500.11937-11457 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:55:04Z |
| publishDate | 2005 |
| publisher | IEEE Computer Society Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-114572018-12-14T00:48:20Z Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha M Palaniswami Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sensors for improving recognition performance. The proposed FS-AHMM is an extension of the Abstract Hidden Markov Model which applies the concept of factored state representations to compactly represent the state transitions. The parameters of the FS-AHMM are estimated using the EM algorithm from the data acquired through multiple multi-modal sensors and cameras. The model is evaluated and compared with other existing models on real-world data. The results show that the proposed model outperforms other models and that the integrated sensor information helps in recognizing activity more accurately. 2005 Conference Paper http://hdl.handle.net/20.500.11937/11457 10.1109/ISSNIP.2005.1595601 IEEE Computer Society Press restricted |
| spellingShingle | Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors |
| title | Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors |
| title_full | Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors |
| title_fullStr | Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors |
| title_full_unstemmed | Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors |
| title_short | Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors |
| title_sort | factored state-abstract hidden markov models for activity recognition using pervasive multi-modal sensors |
| url | http://hdl.handle.net/20.500.11937/11457 |