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.

使用连续的会话中真实疼痛评分显著增强了疼痛强度的机器学习性能，克服了真实评分稀疏性、数据不平衡和高方差等问题，该研究为未来基于客观的机器学习疼痛系统培训提供了重要参考。

利用持续的会话内基准分数提高机器学习性能：客观骨骼肌疼痛强度预测的试点研究

Enhancing Machine Learning Performance with Continuous In-Session Ground  Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity  Prediction