LSTM-DNN based Approach for Pain Intensity and Protective Behaviour Prediction

This paper proposes an approach for pain intensity recognition and protective behaviour prediction task from body movements as a part of the EmoPain challenge. The given dataset consists of body part based sensor data for both the tasks. The proposed network is a lightweight LSTM-DNN model, which takes the angle, angle energy and sEMG features as input and predicts pain intensity level and protective behaviour as output. The performance of LSTM, Bi-LSTM, attention-LSTM and LSTM-DNN models are compared for this problem on the same dataset. In order to enhance the model's discriminating power, joint training of all the models are performed, combining respective task labels with exercise type as an additional label. The experiments show that the proposed approach is effective and outperforms the baseline on the validation set by a margin of 35.00% for pain intensity prediction and 47.72% for protective behaviour prediction, respectively.