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ALANet: Autoencoder-LSTM for pain and protective behaviour detection

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Yuan, Xinhui 
Anis, Marwa 


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 dataset we 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.



46 Information and Computing Sciences, 32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 4611 Machine Learning, Pain Research, Chronic Pain, Neurosciences

Journal Title

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

Conference Name

EmoPain Face and Movement Behaviour Challenge, IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)

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