Cartilage-Inspired Multidirectional Strain Sensor with High Elasticity and Anisotropy Based on Segmented Embedded Strategy

Abstract

Flexible, stretchable, and sensitive multidirectional sensing systems that can decouple different mechanical inputs and identify multidirectional signals are crucial for dynamic human signal perception and intelligent human–computer interaction. Most reported multidirectional sensors are suitable for discriminating in-plane deformation directions, and the sensing materials are difficult to balance between stretchability and mechanical strength. Here, a segmented embedded structure strategy inspired by the interlaced structure of cartilage is proposed. This strategy combines soft and hard materials in a topological and zipper-shear chain manner and balances the performance of reinforced composites with flexibility and high toughness. In the case of segmented embedded hydrogels (SEHs), a wearable multidirectional sensing system that can decouple and identify planar strain/pressure is constructed. The multidirectional sensing system exploits the inherent anisotropy and layered structure design of composites to decouple the sensing functions. Supported by machine learning algorithms, the high accuracy demonstration of the multidirectional sensors in typical multidirectional motion joint posture monitoring and recognition confirms their potential in practical applications such as personal health sensing and human–computer interaction.