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

Abstract

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.

Keywords

• capacitive sensors
• machine learning
• polypyrrole
• vapor phase polymerization
• wearables