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One of the main objectives of smart homes is healthcare monitoring and assistance, especially for elderly and disabled people. Therefore, an accurate prediction of the inhabitant behavior is very helpful to provide the required assistance. This work aims to propose a prediction model that satisfies the accuracy as well as the rapidity of the learning phase. To do so, we propose to improve the existing extreme learning machine (ELM) model by defining a recurrent form. This form ensures a temporal relationship of inputs between observations at different time steps. The new model uses feedback connections to the input layer from the output layer which allows the output to be included in the long-term prediction. A recurrent dynamic network, with feedback connections of the output of the network, is proposed to predict the future series representing future activities of the inhabitant. The resulting model, called …