Interaction-feedback network for multi-task daily activity forecasting

Abstract

The category and occurrence time of daily activity are two basic tasks of daily activity forecasting in a smart home. Multi-task learning has been employed for daily activity forecasting. However, the category and occurrence time of daily activity are weakly correlated to each other, and the loss functions of the two tasks are difficult to achieve at the same time in multi-task learning. In this paper, a daily activity forecasting approach based on an interaction-feedback network is presented. The proposed method combines the multi-head attention mechanism, convolutional neural networks (CNNs), and bidirectional long short-term (Bi-LSTM) memory units to generate a parallel multi-task forecasting model. In addition, a feedback mechanism is introduced to supervise the learning of the two involved tasks. Public datasets are utilized to evaluate the proposed method. Experimental findings show its effectiveness for multi-task daily activity forecasting in terms of precision, recall, F-score, MAE, RMSE, and R2.