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

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-11-17 DOI:10.1002/aisy.202470050

Pierre Kateb, Alice Fornaciari, Chakaveh Ahmadizadeh, Alexander Shokurov, Fabio Cicoira, Carlo Menon

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Abstract

High-Performance Textile-Based Capacitive Strain Sensors

In article number 2400292, Alexander Shokurov, Carlo Menon, and co-workers present high-performance textile-based capacitive strain sensors for wearable applications. Through vapor-phase polymerization of pyrrole, enhanced via addition of co-vapor and imidazole, good conductivity and robustness are achieved in a stretchable textile. A new insulation technique using polymer composites provides durability and dielectric coating. Intertwining such fibers together creates a stretchable capacitive sensor. Integrated into a textile glove, sensors precisely capture fine hand motions. A machine learning model classifies 12 gestures with 100% accuracy, showcasing its potential for wearable technology.

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通过增强吡咯气相聚合的高性能纺织品电容式应变传感器及其在机器学习辅助手势识别中的应用

基于纺织品的高性能电容式应变传感器在编号为 2400292 的文章中，Alexander Shokurov、Carlo Menon 及其合作者介绍了用于可穿戴应用的高性能纺织品电容式应变传感器。通过对吡咯进行气相聚合，并加入共蒸物和咪唑进行增强，在可拉伸纺织品中实现了良好的导电性和坚固性。使用聚合物复合材料的新型绝缘技术可提供耐用性和电介质涂层。将这些纤维交织在一起，就形成了一种可拉伸的电容式传感器。传感器集成到纺织手套中，可精确捕捉手部的细微动作。机器学习模型对 12 种手势进行了分类，准确率达到 100%，展示了其在可穿戴技术方面的潜力。

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