@inproceedings{144d03cfb8934e59aea207d2f59962ff,
title = "Multiple RNN method to prediction human action with sensor data",
abstract = "Human body motion includes the complex spatiotemporal information and human body motion prediction is useful in the human-computer interaction. An Encoder-Multiple-Recurrent-Decoder (EMRD) model to learn human action from sensor data and predict the later ones is proposed in this paper. The kernel of this method is recurrent neural networks (RNN). The model is used to predict the next several frames of a set of sensor data, which is continuous data but is pre-processed by embedding method proposed in this paper. EMRD extends the previous Encoder-Recurrent-Decoder (ERD) models and Long Short Terms Memory (LSTM) model which are used in the video human body movement prediction.",
keywords = "Human body motion modeling, Human body motion prediction, Recurrent neural networks, Sensor data",
author = "Xiangru Chen and Yue Yu and Fengxia Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th International Conference on Virtual Reality and Visualization, ICVRV 2017 ; Conference date: 21-10-2017 Through 22-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICVRV.2017.00104",
language = "English",
series = "Proceedings - 2017 International Conference on Virtual Reality and Visualization, ICVRV 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "419--420",
booktitle = "Proceedings - 2017 International Conference on Virtual Reality and Visualization, ICVRV 2017",
address = "United States",
}