Transforming Cotton Yarns into Scalable 1D Wearable Sensors with Ultrahigh Sensitivity and Linear Response for Physiological Monitoring

The advancement of high-performance wearable sensors based on natural yarns has been persistently hindered by three intertwined limitations: the lack of inherent conductivity, insufficient sensing capabilities, and the absence of scalable manufacturing routes. Here, we present a scalable continuous roll-to-roll coating-reduction strategy to fabricate conductive reduced graphene oxide–cotton yarns (RGO-CYs). The resulting 1D RGO-CYs demonstrated outstanding conductivity and sensing performance, enabling real-time monitoring of diverse physiological signals. They achieve high sensitivity across a broad bending range (0–180°) with ultrahigh linear accuracy ( R 2 > 0.99), a rapid response time of 120 ms, and robust durability exceeding 10,000 cycles. Poly(dimethylsiloxane) encapsulation further endows the yarns with excellent washability (30 cycles) without compromising the sensing functionality. At the application level, the RGO-CYs enable comprehensive monitoring of human motions and physiological states, including joint kinematics (fingers, elbows, wrists, and knees), muscle activities (grasping and pressing), facial microexpressions (chewing and smiling), and heartbeats. This work establishes a complete pathway for transforming traditional yarns into 1D sensing elements, encompassing their functionalization, sensing performance characterization, and practical implementation. This strategy bridges the critical gap between traditional yarns and smart wearables, enabling their direct transition into functional devices and accelerating the development of accessible, textile-based intelligence.