A multisensory-feedback tactile glove with dense coverage of sensing arrays for object recognition

Abstract

Somatosensory networks that provide sophisticated sensory feedback and enable the dexterous manipulation of the human grasp remain difficult to replicate in robots, which is attributed to the grand challenge of densely covering the hand with tactile arrays. Here, a multisensory tactile glove is reported that is capable of object recognition with dense coverage of pressure and temperature sensing arrays. The synergistic effect of the multimodal configuration allows the tactile arrays to perceive contact pressure and thermal conductivity of an object involved in grasping motion, thus enhancing the accuracy via the combination of the mechanical features with thermal properties. By leveraging the multiple scanning technology and wireless transmission system, the tactile glove achieves a recognition accuracy of 94.2% in differentiating 20 types of objects with a modified deep learning algorithm. The large-area sensing arrays with high spatiotemporal resolution and multimodal sensing capabilities, which paves the way for the development of robot grasping tools, human–machine interfacing, and advanced prosthetics.