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http://dx.doi.org/10.7746/jkros.2020.15.3.240

Knitted Data Glove System for Finger Motion Classification  

Lee, Seulah (Department of Electrical and Electronic Engineering, Hanyang University)
Choi, Yuna (Department of Electrical and Electronic Engineering, Hanyang University)
Cha, Gwangyeol (Department of Electrical and Electronic Engineering, Hanyang University)
Sung, Minchang (Department of Electrical and Electronic Engineering, Hanyang University)
Bae, Jihyun (Department of Clothing and Textiles, Hanyang University)
Choi, Youngjin (Department of Electrical and Electronic Engineering, Hanyang University)
Publication Information
The Journal of Korea Robotics Society / v.15, no.3, 2020 , pp. 240-247 More about this Journal
Abstract
This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.
Keywords
Data Glove; Wearable Sensor; Fabric Strain Sensor; Motion Classification;
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Times Cited By KSCI : 2  (Citation Analysis)
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