JRM Vol.27 No.2 pp. 167-173
doi: 10.20965/jrm.2015.p0167


Wearable Device for High-Speed Hand Pose Estimation with a Ultrasmall Camera

Motomasa Tomida and Kiyoshi Hoshino

Graduate School of Systems and Information Engineering, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibakaki 305-0006, Japan

September 10, 2014
January 14, 2015
April 20, 2015
wearable hand capture, hand pose estimation, dimension compression of image characteristic quantity, hand image without fingertip, remote robot control
Hand pose estimation with ultrasmall camera

Operating a robot intentionally by using various complex motions of the hands and fingers requires a system that accurately detects hand and finger motions at high speed. This study uses an ultrasmall camera and compact computer for development of a wearable device of hand pose estimation, also called a hand-capture device. The accurate estimations, however, require data matching with a large database. But a compact computer usually has only limited memory and low machine power. We avoided this problem by reducing frequently used image characteristics from 1,600 dimensions to 64 dimensions of characteristic quantities. This saved on memory and lowered computational cost while achieving high accuracy and speed. To enable an operator to wear the device comfortably, the camera was placed as close to the back of the hand as possible to enable hand pose estimation from hand images without fingertips. A prototype device with a compact computer used to evaluate performance indicated that the device achieved high-speed estimation. Estimation accuracy was 2.32°±14.61° at the PIP joint of the index finger and 3.06°±10.56° at the CM joint of the thumb – as accurate as obtained using previous methods. This indicated that dimensional compression of image-characteristic quantities is important for realizing a compact hand-capture device.

Cite this article as:
M. Tomida and K. Hoshino, “Wearable Device for High-Speed Hand Pose Estimation with a Ultrasmall Camera,” J. Robot. Mechatron., Vol.27, No.2, pp. 167-173, 2015.
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Last updated on Nov. 15, 2018