Deep‐Learning‐Assisted Piezoresistive Intelligent Glove for Pressure Monitoring and Object Identification

Abstract The array of tactile information processing capabilities is an important index for modern intelligent devices advancing toward a humanoid form, and it greatly improves the recognition of different objects in human‐computer interactions. Herein, a deep‐learning‐assisted intelligent grasping recognition system based on a piezoresistive sensing glove, hardware conditioning, and acquisition circuits, and a multibranch deep‐capsule network is reported. Owing to the multiscale 3D structure of carbon nanotube (CNTs)/carbon fiber (CFs) embedded in polydimethylsiloxane (PDMS), the piezoresistive sensing glove is highly sensitive to the pressure exerted by external objects. The acquired signals are reflected on a hand‐like background map, and a combination of multiple subgraphs is used to build the dataset. A multibranch deep‐capsule network is constructed to encode spatial information while realizing object recognition with an accuracy of 99.4%. Therefore, the proposed intelligent grasping recognition system possesses good human‐robot interaction capabilities, providing a new approach for the development of intelligent robots in the field of perceptual recognition applications.