Issue

Vol. 15 (2023)

Machine Learning-Enhanced Flexible Mechanical Sensing

https://doi.org/10.1007/s40820-023-01013-9
Yuejiao Wang
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, People’s Republic of China
Mukhtar Lawan Adam
Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People’s Republic of China
Yunlong Zhao
Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, People’s Republic of China
Weihao Zheng
School of Mechano‑Electronic Engineering, Xidian University, Xi’an 710071, People’s Republic of China
Libo Gao
Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, People’s Republic of China
Zongyou Yin
Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
Haitao Zhao
Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People’s Republic of China

Corresponding Author: Zongyou Yin

zongyou.yin@anu.edu.au

Nano-Micro Letters, Vol. 15 (2023), Article Number: 55

Abstract

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.


Highlights:


1 The latest progress on the integration of flexible mechanical sensing platforms with machine learning (ML) is reviewed.
2 The advantages, challenges, and future perspectives of the application of ML to intelligent flexible mechanical sensing technology are discussed.
3 The fundamental working mechanisms and common types of flexible mechanical sensors are reviewed.

Keywords

Flexible mechanical sensors Machine learning Artificial intelligence Data processing
Wang, Yuejiao, Mukhtar Lawan Adam, Yunlong Zhao, Weihao Zheng, Libo Gao, Zongyou Yin, and Haitao Zhao. 2023. “Machine Learning-Enhanced Flexible Mechanical Sensing”. Nano-Micro Letters 15 (February):55. https://doi.org/10.1007/s40820-023-01013-9.
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