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An artificial neural tactile sensing system

Nature Electronics volume 4, pages 429–438 (2021)Cite this article

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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.

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Fig. 1: Artificial tactile sensory system mimicking a biological system.
Fig. 2: Biomimetic T-skin sensors with particle-based polymer composites.
Fig. 3: Signal-converting system converting sensor signals to sensory neuron-mimicking signals.
Fig. 4: Biological applications of T-skin system.
Fig. 5: Surface texture recognition using T-skin system as active finger skin.

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Data availability

The data supporting the findings of this study are provided in the Supplementary Information. Additional relevant data are available from the corresponding author upon reasonable request.

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Acknowledgements

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF grants 2020R1C1C1007589, 2018R1A6A3A01011866 and 2019M3C1B8077201), a Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT, Ministry of Trade, Industry and Energy, Ministry of Health & Welfare, Ministry of Food and Drug Safety) (NTIS no. 9991006805), the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2021M3H4A1A03048648 and 2021M3F3A2A01037365), the Smart Project Program through the KAIST–Khalifa Joint Research Center (KK-JRC), the KAIST College of Engineering Global Initiative Convergence Research Program and the KAIST Post-AI Research Project. This work was supported by a Korea University Grant.

Author information

Author notes

  1. These authors contributed equally: Sungwoo Chun, Jong-Seok Kim.

Authors and Affiliations

  1. Department SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea

    Sungwoo Chun & Changhyun Pang

  2. School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea

    Sungwoo Chun & Changhyun Pang

  3. Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea

    Sungwoo Chun

  4. Department of Electronics and Computer Engineering, Hanyang University, Seoul, Republic of Korea

    Jong-Seok Kim, Yongsang Yoo, Byong-Deok Choi & Wanjun Park

  5. Department of Physiology, Medical School, Hanyang University, Seoul, Republic of Korea

    Youngin Choi & Sung Jun Jung

  6. Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea

    Dongpyo Jang

  7. Department of Materials Science and Engineering, Yonsei University, Seoul, Republic of Korea

    Gwangyeob Lee

  8. Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea

    Kang-Il Song, Inchan Youn & Donghee Son

  9. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

    Kum Seok Nam, Yong Jeong & Seongjun Park

  10. School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea

    Donghee Son

  11. KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea

    Yong Jeong & Seongjun Park

  12. Medical Device Development Center, Osong Medical Innovation Foundation, Cheongju, Chungbuk, Republic of Korea

    Hachul Jung & Young-Jin Kim

  13. Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

    Jaehun Kim & Sung-Phil Kim

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  1. Sungwoo Chun
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Contributions

S.C. and W.P. conceived this work and developed the design of the human-like neural T-skin system. S.C. and J.-S.K fabricated the T-skin device and analysed its properties. G.L. analysed the distribution and composition of particles. S.C., C.P. and W.P. measured and analysed tactile sensing properties. J.-S.K., Y.Y. and B.-D.C. designed and fabricated a neural stimulator. S.J.J., Y.C. and D.J. conducted ex vivo experiments and databased the SA and FA response patterns. J.K. and S.P.K. made the mathematical function from firing activity of a single-fibre recording. S.P., D.S., K.S.N., K.-I.S., I.Y. and Y.J. conducted the biological experiments for application of the T-skin system. S.C. conducted the deep learning process. All authors analysed the data, and S.C., S.P. and J.-S.K. wrote the paper. All authors reviewed the manuscript and provided feedback.

Corresponding authors

Correspondence to Sungwoo Chun or Seongjun Park.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Electronics thanks John Ho, Bozhi Tian and Gordon Cheng for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–30 and Table 1.

Reporting Summary

Supplementary Video 1

Demonstration of human-like artificial neural T-skin system, which includes a T-skin device with sensor board, a neural stimulator for converting to neural signals, the stimulation system of mouse skin and normal transmission in neurons.

Supplementary Video 2

In vivo response test for T-skin system. A vibration with a certain frequency (1, 2, 10, 20, 100, 500 Hz) is applied to the sensor and the contraction of the hindlimb muscle of the rat is investigated.

Supplementary Data 1

Zip file including raw data of the experiments in spreadsheet form.

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Chun, S., Kim, JS., Yoo, Y. et al. An artificial neural tactile sensing system. Nat Electron 4, 429–438 (2021). https://doi.org/10.1038/s41928-021-00585-x

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