High-Performance Textile-Based Capacitive Strain Sensors via Enhanced Vapor Phase Polymerization of Pyrrole and Their Application to Machine Learning-Assisted Hand Gesture Recognition

Sensors based on everyday textiles are extremely promising for wearable applications. The present work focuses on high-performance textile-based capacitive strain sensors. Specifically, a conductive textile is obtained via vapor-phase polymerization of pyrrole, in which the usage of methanol co-vapor and the addition of imidazole to the iron chloride oxidant solution are shown to maximize conductivity. A technique to provide insulation and mechanical resistance using thermoplastic polyurethane and polystyrene-block-polyisoprene-block-polystyrene/barium titanate composite is developed. Such insulated conductive elastics are then used to fabricate highly sensitive twisted yarn capacitive sensors. A textile glove is subsequently embedded with such sensors. The wireless measurement and transmission system demonstrate efficacy in capturing capacitance variations upon strain and monitoring hand motions. A machine learning model to recognize 12 gestures is implemented—100% classification accuracy is obtained.