Programmable and Scalable Embroidery Textile Resistive Pressure Sensors for Integrated Multifunctional Smart Wearable Systems
Yiduo Yang , Yu Chen , Yang Liu , Rong Yin
Advanced Fiber Materials ›› 2025, Vol. 7 ›› Issue (2) : 574 -586.
Programmable and Scalable Embroidery Textile Resistive Pressure Sensors for Integrated Multifunctional Smart Wearable Systems
Conformable and breathable textile structures are ideal for flexible wearable pressure sensors, yet challenges remain in scalable fabrication, easy integration, and programmability. This study presents a cost-effective and customizable method to create fully textile-based pressure sensors using machine embroidery, enabling seamless integration into smart wearable systems. Two sensing configurations were developed: a single-layer satin block embroidered with conductive yarn, which exhibited high piezoresistivity, fast response (35 ms), quick recovery (16 ms), and robust durability over 5000 press-and-release cycles, proven effective for monitoring activities such as plantar pressure and muscle contraction, and making it suitable for personalized health and fitness applications. The second configuration, a double-layer embroidery sensor with a conductive path and two parallel spacers anchored beneath a satin block, allows for array integration with minimal wiring, demonstrated by a 3 × 3 sensing array that, with the help of a convolutional neural network (CNN) machine learning model, accurately recognized handwritten numbers (0–9) with a 98.5% accuracy, showing its potential for user authentication and secure passcode entry. These findings underscore the potential of machine embroidery for developing scalable, integrated, and high-performance intelligent textile systems, paving the way for wearable technologies that are customizable, comfortable, and aesthetically appealing for a wide range of applications.
Smart textiles / Human augmentation / Health monitoring / Interactive control / User verification / Machine learning / Information and Computing Sciences / Artificial Intelligence and Image Processing / Engineering / Electrical and Electronic Engineering
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Donghua University, Shanghai, China
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