Self-healing triboelectric nanogenerators (TENGs) with flexibility, robustness, and conformability are highly desirable for promising flexible and wearable devices, which can serve as a durable, stable, and renewable power supply, as well as a self-powered sensor. Herein, an entirely self-healing, flexible, and tailorable TENG is designed as a wearable sensor to monitor human motion, with infrared radiation from skin to promote self-healing after being broken based on thermal effect of infrared radiation. Human skin is a natural infrared radiation emitter, providing favorable conditions for the device to function efficiently. The reversible imine bonds and quadruple hydrogen bonding (UPy) moieties are introduced into polymer networks to construct self-healable electrification layer. UPy-functionalized multiwalled carbon nanotubes are further incorporated into healable polymer to obtain conductive nanocomposite. Driven by the dynamic bonds, the designed and synthesized materials show excellent intrinsic self-healing and shape-tailorable features. Moreover, there is a robust interface bonding in the TENG devices due to the similar healable networks between electrification layer and electrode. The output electric performances of the self-healable TENG devices can almost restore their original state when the damage of the devices occurs. This work presents a novel strategy for flexible devices, contributing to future sustainable energy and wearable electronics.

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Accurate plantar pressure mapping systems with low dependence on the external power supply are highly desired for preventative healthcare and medical diagnosis. Herein, we propose a self-powered smart insole system that can perform both static and dynamic plantar pressure mapping with high accuracy. The smart insole system integrates an insole-shaped sensing unit, a multi-channel data acquisition board, and a data storage module. The smart insole consists of a 44-pixel sensor array based on triboelectric nanogenerators (TENGs) to transduce pressure to the electrical signal. By optimizing the sensor architecture and the system's robustness, the smart insole achieves high sensitivity, good error-tolerance capability, excellent durability, and short response-recovery time. Various gait and mobility patterns, such as standing, introversion/extraversion, throwing, and surpassing obstacles, can be distinguished by analyzing the acquired electrical signals. This work paves the way for self-powered wearable devices for gait monitoring, which might enable a new modality of medical diagnosis.

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