Benefiting from the widespread potential applications in the era of the Internet of Thing and metaverse, triboelectric and piezoelectric nanogenerators (TENG & PENG) have attracted considerably increasing attention. Their outstanding characteristics, such as self-powered ability, high output performance, integration compatibility, cost-effectiveness, simple configurations, and versatile operation modes, could effectively expand the lifetime of vastly distributed wearable, implantable, and environmental devices, eventually achieving self-sustainable, maintenance-free, and reliable systems. However, current triboelectric/piezoelectric based active (i.e. self-powered) sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient output. This work systematically summarizes and evaluates the recent research endeavors to address the above challenges, with detailed discussions on the challenge origins, designing strategies, device performance, and corresponding diverse applications. Finally, conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided, proposing the necessity of future research development in this field.

Gesture recording, modeling, and understanding based on a robust electronic glove (E-glove) are of great significance for efficient human-machine cooperation in harsh environments. However, such robust edge-intelligence interfaces remain challenging as existing E-gloves are limited in terms of integration, waterproofness, scalability, and interface stability between different components. Here, we report on the design, manufacturing, and application scenarios for a waterproof E-glove, which is of low cost, lightweight, and scalable for mass production, as well as environmental robustness, waterproofness, and washability. An improved neural network architecture is proposed to implement environment-adaptive learning and inference for hand gestures, which achieves an amphibious recognition accuracy of 100% in 26 categories by analyzing 2,600 hand gesture patterns. We demonstrate that the E-glove can be used for amphibious remote vehicle navigation via hand gestures, potentially opening the way for efficient human-human and human-machine cooperation in harsh environments.