Poster: Activity prediction for mapping contextual-temporal dynamics

Abstract

Existing activity recognition technologies empower the smart home for perceiving the ambient environment. Efficient activity prediction, based on activity recognition, can enable the smart home to provide timely personalized services. However, predicting the next activity and its precise occurrence period are challenging due to the complexity of modelling human behaviour. In this work, we aim to understand whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing. We develop two Long Short-Term Memory (LSTM) models, both with deep contextualized word representation on sensor labels, one with temporal information and one without. Our results highlight that if temporal information is used appropriately, the model with timestamp can outperform the model without this information. While modelling human activity prediction, comprehending the contextual-temporal dynamics is highly important.