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.

本研究检查是否能将深度学习的特征表示从一个实验性诱发疼痛转移到另一个实验性诱发疼痛，以分类三种疼痛程度。通过使用 AI4Pain 挑战数据集，我们利用了现有的热痛卷积神经网络作为特征提取器，从而提高了验证和测试分组的性能，展示了在其他疼痛数据集上训练的模型进行可靠特征提取的潜力。