MDNet: A Lightweight Multidomain 1-D CNN for Embedded Pain Assessment Using EDA Signals

Accurate pain assessment is critical for effective clinical intervention and patient management. Traditional pain assessment methods, such as self-reported scales, are subjective and often unsuitable for patients with cognitive impairments or limited communication abilities. This study proposes MDNet, a multi-domain one-dimensional convolutional neural network designed for real-time pain assessment using electrodermal activity signals. The proposed model extracts information from time, spectral, and cepstral domains to enhance pain classification accuracy. We evaluated MDNet using leave-one-subject-out cross-validation on the AI4Pain and BioVid datasets. The model achieved 92.08% accuracy for binary pain classification (No Pain vs. High Pain) and 71.91% accuracy for multiclass classification (No Pain vs. Low Pain vs. High Pain). Additionally, we deployed MDNet on a Raspberry Pi 5 (RPi-5), demonstrating its feasibility for real-time embedded healthcare applications with low end-to-end latency (102–119 ms), low CPU load (27.5%—30.6%), and efficient power consumption (4.6–4.8 W). These results highlight the potential of integrating deep learning-based pain assessment into wearable and embedded systems, offering a promising step toward automated, real-time pain monitoring in clinical and non-clinical settings.