Please use this identifier to cite or link to this item: https://doi.org/10.1073/pnas.2010989117
Title: Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning
Authors: Yao, Haicheng
Yang, Weidong 
Cheng, Wen 
Tan, Yu Jun 
See, Hian Hian 
Li, Si
Ali, Hashina Parveen Anwar 
Lim, Brian ZH
Liu, Zhuangjian 
Tee, Benjamin CK
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
sensor
electronic skin
machine learning
robotics
wearable
PULSE-WAVE VELOCITY
PRESSURE SENSORS
ARTERIAL STIFFNESS
Issue Date: 13-Oct-2020
Publisher: NATL ACAD SCIENCES
Citation: Yao, Haicheng, Yang, Weidong, Cheng, Wen, Tan, Yu Jun, See, Hian Hian, Li, Si, Ali, Hashina Parveen Anwar, Lim, Brian ZH, Liu, Zhuangjian, Tee, Benjamin CK (2020-10-13). Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 117 (41) : 25352-25359. ScholarBank@NUS Repository. https://doi.org/10.1073/pnas.2010989117
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 pressuresensing 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 (> 107 Ω . kPa-1), 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.
Source Title: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
URI: https://scholarbank.nus.edu.sg/handle/10635/248927
ISSN: 0027-8424
1091-6490
DOI: 10.1073/pnas.2010989117
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