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网格式柔性应变传感器的制备及应用 被引量:2

Fabrication and application of mesh flexible strain sensor
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摘要 鉴于柔性应变传感器在人体运动监测、健康监测等领域的广泛应用,设计出兼具高灵敏度和大应变范围的柔性应变传感器具有重要的意义。本文基于Ecoflex-石墨烯复合材料,通过模板法制备了四边形和六边形网格式柔性应变传感器。通过对比两种不同网格结构传感器的应变范围与拉伸断裂极限,发现六边形网格柔性应变传感器的综合性能更优异,并在80%应变条件下进行拉伸/释放疲劳寿命检测,此传感器表现出良好的可靠性,同时该传感器在手肘关节运动和人体不同呼吸状况监测方面表现良好。将六边形网格柔性应变传感器组合构建多通道检测系统,实现了多种手势识别,这在人工智能和运动识别领域具有广阔的市场应用前景。 In view of the wide application of strain sensors in human motion monitoring,health monitoring and other fields,it is important to design flexible strain sensors with high sensitivity and large strain range.In this paper,the quadrilateral and hexagonal mesh flexible strain sensors were prepared by template method based on Ecoflexgraphene composites.By comparing the strain range and tensile breaking limit of two different mesh sensors,it is found that the comprehensive performance of hexagonal mesh flexible strain sensor is more excellent,and under the condition of 80%strain,the tensile/releasing fatigue life detection is carried out.The sensor shows good reliability,and the sensor performs well in monitoring the elbow joint movement and different breathing conditions of the human body.A multi-channel detection system was constructed by combining hexagonal mesh flexible strain sensors to realize multiple gesture recognition,which has a broad market application prospect in the field of artificial intelligence and motion recognition.
作者 许利强 孙权 詹政 杨润洪 唐智杰 鹿业波 XU Liqiang;SUN Quan;ZHAN Zheng;YANG Runhong;TANG Zhijie;LU Yebo(Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province,Jiaxing University,Jiaxing 314001,China;Faculty of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou 310018,China;College of Information Science and Engineering,Jiaxing University,Jiaxing 314001,China)
出处 《复合材料学报》 EI CAS CSCD 北大核心 2023年第2期970-977,共8页 Acta Materiae Compositae Sinica
基金 嘉兴市科技计划项目(2021AY10061) 浙江省大学生创新活动计划(2022R417A032) 嘉兴学院大学生科研训练计划(CD8517211438)。
关键词 柔性应变传感器 石墨烯 网格结构 健康检测 手势识别 flexible strain sensors graphene mesh structure health monitoring gesture recognition
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