智能背景下机器学习在柔性应变传感器中的应用研究进展  

Research progress on applications of machine learning in flexible strain sensors in context of material intelligence

作　　者：卢妍 洪岩[1,2] 方剑 LU Yan;HONG Yan;FANG Jian(College of Textile and Clothing Engineering,Soochow University,Suzhou,Jiangsu 215021,China;National Engineering Laboratory for Modern Silk,Soochow University,Suzhou,Jiangsu 215123,China)

机构地区：[1]苏州大学纺织与服装工程学院,江苏苏州215021 [2]苏州大学现代丝绸国家工程实验室,江苏苏州215123

出　　处：《纺织学报》2024年第5期228-238,共11页Journal of Textile Research

基　　金：国家重点研发计划项目(2022YFB3805800);国家自然科学基金面上项目(52173059);江苏省高校自然科学研究项目重大项目(21KJA540002)。

摘　　要：为深入研究智能纺织品中柔性应变传感器的发展,探讨了其在检测人体运动轨迹、力学/声学特征以及各类生理指标信息方面的应用,着重阐述了机器学习在提升整个柔性应变传感系统性能方面的作用。通过系统综述最新研究进展,旨在深化对机器学习在基于智能纺织品的柔性应变传感器领域应用的理解。介绍了几种常见柔性应变传感器的原理结构和相关研究,并概述了与柔性应变传感器阵列相结合的先进机器学习算法;系统分析了基于智能纺织品的柔性应变传感器结合机器学习在不同领域中的最新研究,强调了在柔性应变传感器中使用机器学习的益处;最后针对基于智能纺织品的柔性应变传感器结合机器学习的应用所面临的挑战以及如何提升整个传感系统的实用性进行展望,以期能够推动机器学习在柔性智能可穿戴领域的广泛应用,从而进一步推动智能材料与智能纺织品的发展。Significance Because of the rapid progress and growth of smart materials and smart textiles,increasing attention hasbeen focused on the research,development,and optimization of flexible strain sensors.Flexible strain sensors for smart textiles are capable of detecting the precise motion trajectory of the human body,mechanical-acoustic characteristics,and information on various physiological indicators.With the continuous optimization of the performance of flexible strain sensors,the flexible sensor devices need to achieve the acquisition and analysis of high-dimensional and high-frequency complex superimposed signals in very complex application environments,which in turn puts forward higher requirements for data processing algorithms.The implementation of machine learning,a more advanced method,has significantly contributed to the improvement in the overall performance of the flexible strain sensing system.This paper presents a systematic review of the research progress of flexible strain sensors based on smart textiles combined with machine learning.The goal of the review is to understand and broaden the application of machine learning in the field of flexible strain sensors.Progress This paper firstly made an in-depth analysis of the fundamental structure and previous research on a variety of conventional flexible strain sensors such as piezoresistive,piezoelectric,capacitive,optical,magnetic,and triboelectric.In addition,this paper introduced the workflow of machine learning,which can be divided into the following four main steps:data preprocessing,machine learning and model training,model evaluation,and prediction of new data.According to the learning method,machine learning can be classified into supervised learning,unsupervised learning,reinforcement learning,and a mixture of the above three types.This paper then paper provided a detailed description of the information processing process of flexible strain sensors based on machine learning,as well as summarized the advantages and disadvantages of some typic

关 键 词：柔性应变传感器 机器学习 信号处理 智能纺织品 智能材料 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TP181[自动化与计算机技术—控制科学与工程]