Machine learning (ML) models trained on subjective self-report scores struggle to objectively classify pain accurately due to the significant variance between real-time pain experiences and recorded scores afterwards. This study developed two devices for acquisition of real-time, continuous in-session pain scores and gathering of ANS-modulated endodermal activity (EDA).The experiment recruited N = 24 subjects who underwent a post-exercise circulatory occlusion (PECO) with stretch, inducing discomfort. Subject data were stored in a custom pain platform, facilitating extraction of time-domain EDA features and in-session ground truth scores. Moreover, post-experiment visual analog scale (VAS) scores were collected from each subject. Machine learning models, namely Multi-layer Perceptron (MLP) and Random Forest (RF), were trained using corresponding objective EDA features combined with in-session scores and post-session scores, respectively. Over a 10-fold cross-validation, the macro-averaged geometric mean score revealed MLP and RF models trained with objective EDA features and in-session scores achieved superior performance (75.9% and 78.3%) compared to models trained with post-session scores (70.3% and 74.6%) respectively. This pioneering study demonstrates that using continuous in-session ground truth scores significantly enhances ML performance in pain intensity characterization, overcoming ground truth sparsity-related issues, data imbalance, and high variance. This study informs future objective-based ML pain system training.

翻译：基于主观自评分数训练的机器学习（ML）模型在客观分类疼痛时，因实时疼痛体验与事后记录分数之间存在显著差异而难以准确分类。本研究开发了两种设备用于采集实时、连续的会话内疼痛评分及自主神经调控的外胚层电活动（EDA）数据。实验招募了24名受试者，通过运动后循环闭塞联合拉伸诱发不适感。受试者数据存储于定制疼痛平台，用于提取时域EDA特征与会话内真实评分。此外，每个受试者还采集了实验后视觉模拟评分（VAS）。分别采用客观EDA特征结合会话内评分与会话后评分训练多层感知器（MLP）和随机森林（RF）机器学习模型。经10折交叉验证，宏平均几何均值显示：基于客观EDA特征与会话内评分训练的MLP与RF模型（75.9%和78.3%）相比基于会话后评分训练的模型（70.3%和74.6%）性能更优。本开创性研究表明，使用连续会话内真实评分能够显著提升ML模型在疼痛强度表征中的性能，有效克服了真实评分稀疏性、数据不平衡及高方差等问题，为未来基于客观数据的ML疼痛系统训练提供了依据。