Geometry-engineered hourglass-shaped cracks enable wide-linearity wearable strain sensors

材料科学 电阻式触摸屏 拉伤 可穿戴计算机 灵敏度(控制系统) 可穿戴技术 结构健康监测 光电子学 声学 应变计 机械工程 物联网 电容感应 复合材料
作者
SenSen Han,Yan Yue,Milad Razbin,Shuying Wu,Wenkai Chang,Shuai He,Qingshi Meng,Shuhua Peng
出处
期刊:Materials horizons [Royal Society of Chemistry]
卷期号:13 (11): 5550-5561 被引量:4
标识
DOI:10.1039/d6mh00123h
摘要

Flexible and stretchable strain sensors have attracted growing interest for applications in health monitoring and human-machine interfaces. Conventional crack-based resistive strain sensors can achieve high sensitivity and large strain ranges by exploiting the opening and closing of randomly distributed microcracks. However, the uncontrolled evolution of these cracks during prolonged use often leads to performance degradation and poor reliability. In this work, we present a novel resistive strain sensor featuring predefined hourglass-shaped cracks that precisely regulate resistance variations through controlled crack opening and closure under stretching and release. A serpentine array of hourglass-shaped cracks is laser-cut into a 3D-printed conductive TPU film, after which the crack gaps are closed by releasing a pre-stretched elastomer substrate. Through geometric design, crack contact and evolution are precisely controlled, enabling the cracks to open stably and symmetrically under tensile strain, thereby producing predictable and linear resistance changes. This design enables high sensitivity over a broad linear operating range. The sensor also exhibits excellent recoverability after overstretching well beyond its working window and shows negligible resistance variation under bending, highlighting its robustness for wearable applications. To demonstrate its practical utility, the sensor was integrated into athletes' strength-training tasks, and the acquired signals were analyzed using machine learning algorithms to recognize motion patterns and provide early warnings of over-exercise. These capabilities support real-time load adjustment and fatigue-risk management, underscoring the sensor's strong potential for wearable health monitoring and human-machine interface applications.
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