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The presentation of hidden Markov model for forecasting, discovering and extracting the human activities

Mohammad Hossain Rahneshin, Homan Kashanian, Batool Sabeti Noghabi

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In recent years, new functional bases were appeared, which presented new limitations and ways in the base of information extraction. The ability of human activities understanding will increase the power and the ability of predicting the human activities. In fact, the analysis of human activities throughout history was something that has attracted everyone’s attention. However, the automatic discovery of human activities causes to challenge human’s natural activities. In this article we will propose a model based on hidden Markov model or hidden Markov for understanding, discovering and predicting of human activities. We will produce the hidden Markov model to realize and discover human activities. The output of this compound model will be able to record the human activities so efficiently. We will test our model based on available popular dataset (CASAS),this dataset contains 12 different daily activities of human. Our model has useful efficiency up to 89 percent to present in the same smart homes.

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