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 |
| Summary: | 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. |
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