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.

翻译：疼痛数据集的有限规模是开发稳健的深度学习模型用于疼痛识别所面临的一个挑战。在这些场景中，迁移学习方法常被采用。在本研究中，我们探究一种实验诱发疼痛的深度学习特征表示能否迁移至另一种疼痛类型。通过参与AI4Pain挑战赛，我们的目标是分类三种疼痛等级（无痛、轻度疼痛、重度疼痛）。挑战赛数据集包含从65名经历不同强度电击痛的参与者收集的数据。我们利用数据集中的视频记录，研究深度学习的灼痛模型向电击痛的迁移性。在我们提出的方法中，我们利用一个现有的灼痛卷积神经网络（CNN）——该网络在BioVid数据集上训练——作为特征提取器。将挑战赛数据集中的图像输入预训练的灼痛CNN以获得特征向量。这些特征向量用于训练两种机器学习模型：一个简单的前馈神经网络和一个长短期记忆（LSTM）网络。我们的方法使用数据集预定义的训练集、验证集和测试集进行了评估。我们的模型在验证集和测试集上的表现均优于挑战赛的基线，突显了在其他疼痛数据集上训练的模型用于可靠特征提取的潜力。