Classification of Embroidered Conductive Stitches Using a Structural Neural Network

This study presents a machine learning-based framework for classifying five embroidered stitch patterns—straight, zigzag, joining, satin, and wave—under 10% tensile strain, aiming to enhance their utility in smart textile circuits. Electrical conductivity was derived from resistance data and standardized using Z-score normalization. Conductivity sequences were first analyzed with PCA and Random Forest classifiers, then classified using a structural artificial neural network model. The model employed a structurally informed filter design, reflecting stitch-wise signal periodicity to capture time-varying electrical patterns under cyclic strain. It achieved a test accuracy of 97.33%, with F1-scores above 0.83 for all classes and perfect scores in three. Partial confusion between wave and zigzag patterns was observed due to their similar curved geometry and signal profiles. These results validate the discriminative power of conductivity-based features and demonstrate the potential of structure-aware neural networks for identifying dynamic stitched circuits in smart textiles.