Flexible and self-adhesive strain sensor based on GNSs/MWCNTs coated stretchable fabric for gesture monitoring and recognition

被引:24
|
作者
Zhou, Zaiwei [1 ]
Zhang, Wanli [1 ]
Zhang, Jiatong [2 ]
Zhang, Yue [1 ,3 ]
Yin, Xiangyu [3 ,4 ]
He, Bingwei [1 ,3 ]
机构
[1] Fuzhou Univ, Coll Mech Engn & Automat, Fuzhou 350108, Peoples R China
[2] Fuzhou Univ, Maynooth Int Engn Coll, Fuzhou 350108, Peoples R China
[3] Fujian Engn Res Ctr Joint Intelligent Med Engn, Fuzhou 350108, Peoples R China
[4] Fuzhou Univ, Coll Chem Engn, Fuzhou 350108, Peoples R China
基金
中国国家自然科学基金;
关键词
Strain sensor; Self-adhesive fabric; Screen printing; Deep learning;
D O I
10.1016/j.sna.2022.114004
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Fabrics are considered to be promising alternatives to silicon wafers in wearable electronics, and the strain sensors developed based on them have attracted widespread attentions for their promising applications in health and motion monitoring, soft robotics and human-computer interaction, etc. However, existing fabric-based strain sensors (FSS) are usually prepared by complicated methods which greatly limit their practical applicability. Herein, high-performance FSSs were prepared by transferring graphene nanosheets (GNSs)/multi-walled carbon nanotubes (MWCNTs) mixing ink onto stretchable cloth adhesive tape via a simple one-step screen-printing method. The sensing range and sensitivity of the prepared FSSs were adjustable by designing different sensing patterns, and the FSS with a high GF (40) and a suitable strain range (30%) was used for detection of finger bending angles. The optimized FSS features excellent linearity (0.99), outstanding durability (>5000), superior breathability, good adhesion and ease of storage and use. Combined with morphological characterization and tensile tests, the sensing principle of the FSS was explained. Furthermore, a FSS-based sensing glove was inte-grated, and combined with LSTM deep learning model; it achieves a high accuracy (95%) of dynamic gesture recognition and can be used to control manipulator which demonstrates its promising applications in the field of smart wearable electronics and human-computer interaction.
引用
收藏
页数:10
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