Towards automating smart homes: Contextual and temporal dynamics of activity prediction

Abstract

The existing smart-home ecosystem has the capability to perceiving the ambient environment by using cutting-edge sensing technologies but is limited to reacting autonomously and timely. Successfully predicting the subsequent human activity can effectively infer human intention and instruct the smart homes to react in a timely, customized and accurate way. However, predicting the next activity and its precise occurrence period are challenging due to the complexity of modelling human behaviour. In this paper, the Long Short-Term Memory (LSTM) network equipped with temporal information is investigated to understand whether integrated temporal information on the model has better prediction performance or not. Our results highlight that, accurately integrating the temporal information into the models bring better prediction accuracy. In terms of modelling and further predicting human activity, comprehending the contextual-temporal dynamics is highly significant.