Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain

• URL: http://arxiv.org/abs/2406.11808v1
• Date: Mon, 17 Jun 2024 17:51:54 GMT
• Title: Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain
• Authors: Pooja Prajod, Dominik Schiller, Daksitha Withanage Don, Elisabeth André,
• Abstract summary: In this study, we investigate whether deep learned feature representation for one type of experimentally induced pain can be transferred to another. Challenge dataset contains data collected from 65 participants undergoing varying intensities of electrical pain. In our proposed approach, we leverage an existing heat pain convolutional neural network (CNN) - trained on BioVid dataset - as a feature extractor.
• Score: 7.205834345343974
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The limited size of pain datasets are a challenge in developing robust deep learning models for pain recognition. Transfer learning approaches are often employed in these scenarios. In this study, we investigate whether deep learned feature representation for one type of experimentally induced pain can be transferred to another. Participating in the AI4Pain challenge, our goal is to classify three levels of pain (No-Pain, Low-Pain, High-Pain). The challenge dataset contains data collected from 65 participants undergoing varying intensities of electrical pain. We utilize the video recording from the dataset to investigate the transferability of deep learned heat pain model to electrical pain. In our proposed approach, we leverage an existing heat pain convolutional neural network (CNN) - trained on BioVid dataset - as a feature extractor. The images from the challenge dataset are inputted to the pre-trained heat pain CNN to obtain feature vectors. These feature vectors are used to train two machine learning models: a simple feed-forward neural network and a long short-term memory (LSTM) network. Our approach was tested using the dataset's predefined training, validation, and testing splits. Our models outperformed the baseline of the challenge on both the validation and tests sets, highlighting the potential of models trained on other pain datasets for reliable feature extraction.

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