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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 31, Number 5(1) (2019)
Copyright(C) MYU K.K.
pp. 1387-1395
S&M1865 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2261
Published: May 16, 2019

Natural Hand Gesture Recognition with an Electronic Textile Goniometer [PDF]

Sang-Ho Han, Eun-Ji Ahn, Mun-Ho Ryu, and Je-Nam Kim

(Received April 16, 2018; Accepted March 13, 2019)

Keywords: gesture recognition, electronic textile, human–computer interface, biomechanics

Gesture recognition allows distinguishing specific user motions that intend to express a message. The recognized gestures can be used in various applications such as human–computer interface (HCI), clinical practice including rehabilitation, and personal identification. We propose a method of recognizing upper-limb motion gestures for HCI using electronic textile sensors, which consist of a double-layered structure with complementary resistance characteristics. For gesture recognition, we apply dynamic time warping (DTW) as it exhibits a high performance with simple computations for dynamic signals. We verified the functional feasibility of the proposed method from the data of 10 subjects performing 6 HCI gestures. The gesture classification accuracy for all subjects was 85.4%, although each subject separately achieved a higher performance. In fact, six subjects achieved a perfect recognition performance (100% recognition accuracy); three subjects achieved an accuracy of 98.6%, and one achieved an accuracy of 97.2%.

Corresponding author: Mun-Ho Ryu


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Cite this article
Sang-Ho Han, Eun-Ji Ahn, Mun-Ho Ryu, and Je-Nam Kim, Natural Hand Gesture Recognition with an Electronic Textile Goniometer, Sens. Mater., Vol. 31, No. 5, 2019, p. 1387-1395.



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