A wearable yarn-based piezo-resistive sensor

被引:164
|
作者
Huang, Ching-Tang [1 ,2 ]
Shen, Chien-Lung [2 ,3 ]
Tang, Chien-Fa [2 ]
Chang, Shuo-Hung [1 ,4 ]
机构
[1] Natl Taiwan Univ, Dept Mech Engn, Taipei, Taiwan
[2] TTRI, Dept Prod Dev, Taipei, Taiwan
[3] Natl Yang Ming Univ, Inst Biomed Engn, Hsinchu, Taiwan
[4] ITRI, Mech & Syst Lab, Hsinchu, Taiwan
关键词
smart textile; yarn-based sensor; piezoresistive;
D O I
10.1016/j.sna.2007.10.069
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
Smart textiles using fabric-based sensors to monitor gesture, posture or respiration have been exploited in many applications. Most of fabric-based sensors were fabricated by either coating piezo-resistive materials on a fabric or directly knitting conductive fibers into fabrics. Obviously, structures of textiles, including yam structure and fabric structure, will affect the performances of sensors. However, researches on the effects of the structures have not been explored yet. In this paper, yarn-based sensors were fabricated by using piezo-resistive fibers, elastic, and regular polyester fibers. Single and double wrapping methods were employed to fabricate the yarn-based sensors. Performances of the designed yam-based sensors were evaluated by measuring their resistance changes under variable loading. It is shown that slippage occurs between the piezo-resistive fibers and the core fibers. The relationship of the resistance versus the strain cannot be described as a linear function and should be modeled as a second order equation. Due to the symmetric structure, the double wrapping yam could resist the slippage and higher linearity in the resistance curve can be provided. Thus it can be served as a better sensing element. The study also investigates the issue of the twist per meter (TPM) and finds that there are no significant effects for using different TPM. Finally, experiments were conducted on a respiration monitoring system to prove the feasibility of the yam-based sensors and the results demonstrate that the yarn-based sensor can track the respiratory signals precisely. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:396 / 403
页数:8
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