Conference Proceedings

Predicting complex activities from ongoing multivariate time series

W Cheng, S Erfani, R Zhang, K Ramamohanarao

IJCAI : proceedings of the conference / sponsored by the International Joint Conferences on Artificial Intelligence | International Joint Conferences on Artificial Intelligence | Published : 2018

Abstract

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. The rapid development of sensor networks enables recognition of complex activities (CAs) using multivariate time series. However, CAs are usually performed over long periods of time, which causes slow recognition by models based on fully observed data. Therefore, predicting CAs at early stages becomes an important problem. In this paper, we propose Simultaneous Complex Activities Recognition and Action Sequence Discovering (SimRAD), an algorithm which predicts a CA over time by mining a sequence of multivariate actions from sensor data using a Deep Neural Network. SimRAD simultaneously learns two probabili..

View full abstract

Citation metrics