Flexible strain sensing percolation networks towards complicated wearable microclimate and multi-direction mechanical inputs

A dramatic proliferation of research is placed on wearable and skin-mountable sensing devices because of the prominent serviceability in motion and health recognition, man-machine interaction, as well as artificial intelligence. State-of-the-art wearable sensors, however, lack sensing reliability towards either fickle wearable microclimate or multi-direction mechanical inputs, which leads to a suboptimal sensing accuracy throughout the implementation. In this work, we propose an assembly-flexible strain sensing network based on a carbon nanotube percolated configuration. The sensor possesses high reliability upon microenvironment change of wearable interfaces by taking advantage of the sensing stability in various temperatures, humidity, aqueous acid, and alkaline solutions. The response to bending, twisting, and pressuring is also marginal, guaranteeing sensing dependability against multi-direction mechanical inputs in practical wearable scenarios. By being integrated with deep learning and control systems, the high-performance and biocompatible strain gauges can precisely identify hand gestures and manipulate the upwards/downwards bending of a robot wrist. It demonstrates huge potential in motion identification and man-machine interaction.