Yuan, X. and Mahmoud, M. (2020) ALANet:Autoencoder-LSTM for Pain and Protective Behaviour Detection. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina, 16-20 Nov 2020, pp. 824-828. ISBN 9781728130798 (doi: 10.1109/FG47880.2020.00063)
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Abstract
Automatic detection of pain and protective behaviour can help chronic pain patients to get proper assistance and helpful treatment with the help of medical professionals. Using the EmoPain datasetwe study how autoencoder-based and attention-based deep learning models can be used to automatically detect pain and protective behavior that is usually associated with it. We propose a deep learning architecture called Autoencoder-LSTM-Attention-Net (ALANet), which can improve the automatic detection of pain and protective behaviors. Through comparative experiments with other machine learning models trained on the EmoPain dataset, we found that by using a combination of autoencoder and attention mechanisms, we can not only improve the recognition performance, but also greatly increase the speed of training the model. In addition, we analyse the effect of extracting temporal information from each body part separately compared to all body parts combined.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Mahmoud, Dr Marwa |
Authors: | Yuan, X., and Mahmoud, M. |
College/School: | College of Science and Engineering > School of Computing Science |
ISBN: | 9781728130798 |
Published Online: | 18 January 2021 |
Copyright Holders: | Copyright © 2020 IEEE |
First Published: | First published in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020): 824-828 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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