Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning.

Journal: Proceedings of the National Academy of Sciences of the United States of America
Published Date:

Abstract

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 10 Ω ⋅ kPa), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0-20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.

Authors

  • Haicheng Yao
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
  • Weidong Yang
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
  • Wen Cheng
    Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang 110004, China.
  • Yu Jun Tan
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
  • Hian Hian See
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
  • Si Li
    School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
  • Hashina Parveen Anwar Ali
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
  • Brian Z H Lim
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
  • Zhuangjian Liu
    Institute of High Performance Computing, A*STAR Research Entities, 138632 Singapore, Singapore.
  • Benjamin C K Tee
    Materials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore; benjamin.tee@nus.edu.sg.