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.