  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.
