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

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 117; no. 41; pp. 25352 - 25359
• Main Authors: Yao, Haicheng, Yang, Weidong, Cheng, Wen, Tan, Yu Jun, See, Hian Hian, Li, Si, Ali, Hashina Parveen Anwar, Lim, Brian Z. H., Liu, Zhuangjian, Tee, Benjamin C. K.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 13.10.2020
• Subjects: Algorithms; Cracks; Deep learning; Elastomers; Engineering; Flaw detection; Humans; Hysteresis; Learning algorithms; Machine Learning; Materials Science; Physical Sciences; Pressure; Pulse Wave Analysis; Sensitivity; Sensor arrays; Sensors; Surface layers; Tactile discrimination; Tactile perception; Tactile sensors (robotics); Touch; Viscoelasticity; Wave velocity; Wearable Electronic Devices; robotics; electronic skin; wearable; sensor; machine learning
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2010989117

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 (> 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.