Data adjustment methods of a low-priced data glove
Shinichi Hamaguchi∗

Sanshiro Yamamoto

Kenji Funahashi†

Department of Computer Science and Engineering, Nagoya Institute of Technology

1I NTRODUCTION

A data glove is one of devices which are used in the field of virtual
reality. We must use a data glove which has many sensors to capture a variety of human hand motions. However it is expensive anda low-priced data glove does not have enough sensors to capture
hand data correctly. There are researches about a data adjustment
method with low-priced glove [1], it used finger angle correlations
only for the grip motion. In this paper, we propose two data adjustment methods. One is based on object shape knowledge which isheld [2], another is based on a hand motion pattern estimation [3].
In our experiment system we chose three representative motions tohold; grip for a cube/cylinder type object, nip for a thin object, andpinch for a small object held by a thumb and an index finger. Then
we calculate all finger joint angles from each hand motion patternwhich is surveyed in advance. Using our new methods, we canadjust finger joint angles from just five sensors of a glove.
2Figure 2: Difference of two hand motions

F INGER RELATION METHOD

The hand motions can be expressed with difference of finger angles.
It means that each hand motion has each relations among angles offingers during operation. Therefore we can estimate a hand motion
pattern using these relations. For a preliminary survey, we sampled
the angles of five finger sensors and all joints for the representative motions, then we matched sensor value relation to each motion
which can obtain all angles. However a hand does not behave according to exact representative motions actually. So an interpolated
relation is made for the motion among the representative motions.
Then we obtain the equation for the present motion and calculate
proper angles of all finger joints.
4

Figure 1: Interpolate hand motion

O BJECT KNOWLEDGE METHOD

When we hold an object, our hand motion pattern is usually affectwith a shape of the object. Thus we can estimate a hand motion
pattern from an object shape knowledge. We supposed an object
shape to be a rectangular solid in an experiment system. From apreliminary survey we decided basic sizes of an object and madesix appropriate hand motion patterns for each size object. Whenthe object is not any basic dimension, an interpolated motion ismade as shown in figure 1. Although these patterns are assumed
that a hand confronts the object directly, a hand does not always
confront directly it. In this case we also make an interpolated handmotion pattern. Then we obtain equations for the pattern which issuitable for an unknown size object and a hand motion. Using thisequations, we calculate proper angles of all finger joints.
3Hidenori Kanazawa
NTT COMWARE Tokai Co.

C ONCLUSION

Figure 2 shows differences of grip and pinch operation on the system based on our methods. Although sensor angles of index finger
are the same, calculated angles of all joints are different as shown∗ e-mail: hama@center.nitech.ac.jp† e-mail: kenji@nitech.ac.jp

in CG images. As a result of evaluation experiments, both methods are effective to capture different hand motions. In the future,
we should improve these methods to adopt other hold styles, and tointegrate each other. We have also researched about VBDG (visionbased data glove) which capture a hand motion from camera imageof the hand. We are going to combine these adjustment method andVBDG together.
R EFERENCES[1] D. G. Kamper and E. G. Cruz, and M. P. Siegel. Stereotypical fingertip trajectories during grasp. In J Neurophysiol 90, pages 3702-3710,
2003.
[2] S. Yamamoto, K. Funahashi and H. Kanazawa. A data adjustment
method of low-priced data-grove based on object shape knowledge. In
Proceedings o f the 16th Annual Con f erence o f the Virtual Reality Society Japan, 33D-5 (DVD-ROM, in Japanese), 2011.
[3] S. Hamaguchi and K. Funahashi. A data adjustment method oflow-priced data-grove based on hand motion pattern estimation. In
Proceedings o f the 16th Annual Con f erence o f the Virtual Reality Society Japan, 33D-6 (DVD-ROM, in Japanese), 2011.