Triboelectric bending sensor based smart glove towards intuitive multi-dimensional human-machine interfaces. (November 2021)

• Record Type: Journal Article
• Authors: Luo, Yang; Wang, Zihan; Wang, Jiyu; Xiao, Xiao; Li, Qian; Ding, Wenbo; Fu, H.Y.
• Is Part Of: Nano energy. Volume 89(2021)Part A
• Publication Date: 2021-11
• Subjects: Triboelectric nanogenerator -- Angle sensor -- Glove-based HMI -- Machine learning recognition -- Robotics; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2021.106330
• Languages: English
• ISSNs: 2211-2855

Abstract

Abstract: Recent advances in human-machine interface (HMI) lead to a renewed interest in creating intuitive and immersive interaction. Here, we designed a simple-structured and high-resolution bending angle triboelectric sensor named bending-angle triboelectric nanogenerator (BA-TENG) to construct a glove-based multi-dimensional HMI. With the assistance of a customized print circuit board (PCB), the glove-based HMI exhibits high sensitivity and low crosstalk in real-time multi-channel finger motion sensing. The signal-to-noise ratio (SNR) is improved by 19.36 dB. By systematically extracting and analyzing the multi-dimensional signal features of the BA-TENG, intuitive multi-dimensional HMIs were realized for smart-home, advanced robotic control, and a virtual keyboard with user recognition functionality. The classification accuracy of the virtual keyboard for seven users reached 93.1% by leveraging the advanced machine learning technique. The proposed BA-TENG-based smart glove reveals its potential as a solution for minimalist-design and intuitive multi-dimensional HMI, promising in diversified areas, including the Internet of things (IoT), assistive technology, and intelligent recognition systems. Graphical Abstract: ga1 Highlights: Development of high bending angle resolution triboelectric nanogenerator (BA-TENG) using PDMS and silicon rubber. The signal-to-noise ratio (SNR) is improved by 19.36 dB with the assistance of a trans-impedance amplifier. Multi-dimensional signal features are extracted, including valley ( V ), width ( W ), hold time ( H ), and time interval ( T ). Light-brightness control, robotic hand control, and a virtual keyboard with user identification were achieved. Machine learning technique (SVM) is used for user identification with an accuracy of 93.1%.