Smart yarns and textiles are an active field of researches nowadays due to their potential applications in flexible and stretchable electronics, wearable devices, and electronic sensors. Integration of ordinary yarns with conductive fillers renders the composite yarns with new intriguing functions, such as sensation and monitoring of strain and stress. Here we report a low cost scalable fabrication for highly reliable, stretchable, and conductive composite yarn as effective strain sensing material for human motion monitoring. By incorporating highly conductive single-wall carbon nanotubes (SWCNTs) into the elastic cotton/polyurethane (PU) core-spun yarn through a self-designed coating approach, we demonstrated that the yarn is able to detect and monitor the movement of human limbs, such as finger and elbow, and even the wink of eyes. By virtue of the covered structure of the cotton/PU yarn and the reinforcement effect of SWCNTs, the composite yarn can bear up to 300% strain and could be cycled nearly 300,000 times under 40% strain without noticeable breakage. It is promising that this kind of conductive yarn can be integrated into various fabrics and used in future wearable devices and electronic skins.

Capacitive‐type strain sensors based on hydrogel ionic conductors have undergone rapid development benefited from their robust structure, drift‐free sensing, higher sensitivity, and precision. However, the unsatisfactory electro‐mechanical stability of the conventional hydrogel conductors, which are normally vulnerable to large deformation and severe mechanical impacts, remains a challenge. In addition, there is not enough research regarding the adhesiveness and mechanical properties of the dielectric layer, which is also critical for the mechanical adaptability of the whole device. Here, a dynamically super‐tough capacitive‐type strain sensor based on energy‐dissipative dual‐crosslinked hydrogel conductors and an organogel dielectric with high adhesive strength is developed. Combining with the mechanical advantages of the hydro/organo‐gels, the capacitive strain sensor exhibits high stretchability and superior linear dependence of sensitivity with a gauge factor of ≈0.8% at 100% strain. Moreover, the sensor displayed ultrastability against various severe mechanical stimuli that can even survive unprecedentedly from extremely catastrophic car run‐over by 20 times. With these synergistic mechanical advantages, the capacitive strain sensor is successfully applied as a highly‐reliable wearable sensing system to monitor diverse faint physiological signals and large‐range human motions.

In the fields of electronic skin and soft wearable sensors, intrinsically stretchable conductors undergo rapid development; however, practical applications of artificial skinlike materials/devices have not been realized because of the difficulty in combining the electromechanical properties and sensing performance. Contrarily, insoluble inorganic conductive domains in the hydrogel matrix are generally incompatible with surrounding elastic networks, decreasing the mechanical strength. Usually, the hydrogels are vulnerable either to severe mechanical stimuli or large deformation, especially when notches are induced. In this study, based on an energy-dissipative dual-crosslinked conductive hydrogel, a mechanically durable and super-tough strain sensor was developed. The highly soft yet dynamically tough hydrogel demonstrated high ionic conductivity (30.2 mS cm −1 ), ultrastretchability (>600% strain), and superior linear dependence of strain sensitivity with a maximum gauge factor of 1.2 at 500% strain. Because of these advantageous synergistic effects, the resultant hydrogel strain sensor demonstrated reliable and stable detection of a large range of human motion and subtle vibrations. Moreover, it impressively exhibited super toughness that could endure consecutive treading pressure and even retain normal operation after 20 times of car run-over on the road. These demonstrations highly confirm the sensor's superior mechanical durability and reliability, displaying great potential in developing next-generation mechanically adaptable sensors.

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The bridge bearing stress and deformation can eflect the safety of the bridge structure significantly. For bridge bearing status monitoring, identification and early warning technology, in recent years, is an important research direction for structural health monitoring. This paper is based on a model pot rubber bearing , combines with the practical application of bridge bearing condition, chooses specific sensors as data acquisition nodes, designs and develops the signal adjusting module, and uses the data acquisition card to build the hardware platforms of the data acquisition system. Furthermore here also uses virtual instrument technique to develop the the upper computer of this data acquisition system. The system is validated by performing laboratory loading test on a model bridge bearing.The experiment result and data are analyzed