An artificial neural tactile sensing system

Sungwoo Chun, Jong Seok Kim, Yongsang Yoo, Youngin Choi, Sung Jun Jung, Dongpyo Jang, Gwangyeob Lee, Kang Il Song, Kum Seok Nam, Inchan Youn, Donghee Son, Changhyun Pang, Yong Jeong, Hachul Jung, Young Jin Kim, Byong Deok Choi, Jaehun Kim, Sung Phil Kim, Wanjun Park, Seongjun Park

Research output: Contribution to journalArticlepeer-review

Abstract

Humans detect tactile stimuli through a combination of pressure and vibration signals using different types of cutaneous receptor. The development of artificial tactile perception systems is of interest in the development of robotics and prosthetics, and artificial receptors, nerves and skin have been created. However, constructing systems with human-like capabilities remains challenging. Here, we report an artificial neural tactile skin system that mimics the human tactile recognition process using particle-based polymer composite sensors and a signal-converting system. The sensors respond to pressure and vibration selectively, similarly to slow adaptive and fast adaptive mechanoreceptors in human skin, and can generate sensory neuron-like output signal patterns. We show in an ex vivo test that undistorted transmission of the output signals through an afferent tactile mouse nerve fibre is possible, and in an in vivo test that the signals can stimulate a rat motor nerve to induce the contraction of a hindlimb muscle. We use our tactile sensing system to develop an artificial finger that can learn to classify fine and complex textures by integrating the sensor signals with a deep learning technique. The approach can also be used to predict unknown textures on the basis of the trained model.

Original languageEnglish
Pages (from-to)429-438
Number of pages10
JournalNature Electronics
Volume4
Issue number6
DOIs
StatePublished - 2021 Jun

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