A Data Adjustment Method of Low-priced Data-glove based on
Representative Hand Motion Using Medical Knowledge
Hiromasa Takahashi∗

Kenji Funahashi†

Department of Computer Science and Engineering,

Department of Computer Science and Engineering,

Nagoya Institute of Technology

Nagoya Institute of Technology

A BSTRACTA data glove is one of the interfaces which are used in the field of
virtual reality. An expensive data glove has many sensors to capture
a variety of human hand motion. On the other hand, low-priced dataglove does not have enough sensors to capture hand motion directly.
We have proposed the method to obtain all finger joint angles by estimating the pattern of user’s hand motion from the sensor value. In
our pilot experiment system, we assumed three representative handmotions as grasping behavior; grip, pinch, and nip. And we assumed that other hand motions can be represented by synthetic motion of them. However we have not discussed whether these handmotions are appropriate or not in order to express other hand motions. In this paper, we reconsider the representative hand motion
using medical knowledge for more accurate finger joint angles.
Index Terms: Data-glove, hand motion estimation, finger jointangles estimation.
1I NTRODUCTION

In recent years, VR researches that targets to households have been
attracted. It is preferable that an interface is small scale and low
cost. In order to obtain accurate hand motions, it is necessary to
use a data glove which has many sensors, but it is expensive. On
the other hand, there is a low cost data glove which measures anangle of a finger through one sensor. But it cannot get detailed
data directly. We have proposed the method to get plausible user
hand motions from the low-cost one. This method estimates thekind of hand motions using each relation among angles of fingers
during operation. Then it estimates all finger angles using the correlation between each finger angle in the hand motion assumed inadvance[1]. Specifically, we assume the representative hand motions as grasping behavior; grip for a cube/cylinder type object, nip
for a thin object, and pinch for a small object held by a thumb andan index finger. And we calculate the ratio of each representative
motion. Moreover estimating each finger angle using the result, weexpress any hand motions other than the representative hand motions. However we have not discussed whether these hand motions
are appropriate or not in order to express a variety of other hand motions. Thus, we try to find better representative hand motions fromthe research on the classification of the holding object pattern[2].
2DATA A DJUSTMENT FOR L OW- PRICED G LOVE

2.1

Method of Hand Motion Estimation

First we sampled the sensor values for each representative handmotions and conducted D’Agostino-Pearson test at the 5 % significance level concerning these, and there was no significant differences. So we suppose distribution of each sensor value follows normal distribution and each sensor value as feature amount in five∗ e-mail: hiromasa@center.nitech.ac.jp† e-mail: kenji@nitech.ac.jp

dimensional feature amount space also follows multivariate normal distribution when representative hand motions were performed.
Then we set the following formula based on the probability density
function of the multivariate normal distribution for n points in thefive dimensional feature amount space.
{)T()}
1(L pn (SS : µ pn , Σ pn ) = exp − S − µ pn Σ −1
(1)
pn S − µ pn2
Where S is the sensor value vector. µ pn and Σ pn represent mean
vector of sensor values, variance covariance matrix of point n (aninteger satisfying 1 ≤ n ≤ 25) in representative hand motion p.
Then if the sensor values are obtained actually from the glove, weselect the maximum value according to the following formula.
{()}
L p = max L pn S : µ pn , Σ pn
(2)nThus, we get the likelihood on representative hand motion p in current sensor values. After that, we decide the ratio r p of hand motion
p according to the following formula.
rp =Lp2∑ p=1 L p

(3)

2.2 Method of Finger Angles Determination
We calculate the weighted average of finger joint angles in eachhand motion using finger joint angle equations obtained by experiment that utilize the ratio r p as weighting factor to estimate thefinger joint angles of the user.

θi1=

θi2=

2
θi23E pi2 Si 3 + Fpi2 Si 2 + G pi2 Si + H pi2

θi3=E pi3 Si 3 + Fpi3 Si 2 + G pi3 Si + H pi3

(4)
(5)
(6)

Where θi1 , θi2 and θi3 express the DIP, PIP and MP joint angle ofthe finger i. Si is sensor value of finger i. And E pi j , Fpi j , G pi j andH pi j are constant for the motion p obtained by pre-experiment.
3 R ECONSIDER R EPRESENTATIVE H AND M OTIONS
3.1 Candidate Selection
We had set the three representative hand motions as grip, pinch
and nip. In the following, we reconsider them through the researchon the grasping behavior of human hand[2]. They had observed
daily grasping forms in experimental condition and classified theminto 14 types to help reference in clinical. We select 10 candidates
as representative hand motion from these 14 types, because theychange enough sensor values of a data-glove respectively (Fig. 1).
And we obtained the transitions of each finger joint angle of the10 motions from open hand to each form using data-glove which
has many sensor (Immersion CyberTouch). It was also confirmedthat Parallel Flex and Circular Flex can be represented in a part ofStandard (Fig. 2), and Phalangeal Ext can be represented in a part ofLateral Contact. Therefore we selected 7 motions as representative
ones of candidate No.1.

Standard

Hook-likeIndex Ext

Lateral Contact

TripodFigure 4: Average Hand Motions (left: MC2 , right: MC3 )Parallel Ext

Tip Contact

Pharangeal Ext

Parallel Flex

Circular Flex

PIP joint angle of index finger

Figure 1: Hand Motions100806040
StandardParallel Ext
Circular Flex

200.83, candidate 2: 0.86, and candidate 3: 0.95. We can also confirm the average of candidate 3 is higher than the average 0.92 ofconventional system[1]. Table 1 shows the average of the errorsof DIP, PIP and MP between estimated finger joint angles and obtained angles by CyberTouch. The input data is Tripod motion forcandidate 1 and 2, and MC2 for candidate 3. Table 2 shows the error
when the input motion data is not representative one for each candidate, that is, the input data is MC2 motion for candidate 1 and 2,and Tripod for candidate 3.
We confirmed that the error of candidate 3 is smallest for theaverage of both results, and it can deal with any hand motions
other than the representative ones. Therefore we found that candidate No.3 is the most suitable for representative hand motions. As
a future work, we should decide the parameters as E pi j to H pi j ofequation 5 and 6 for each user’s hand shape, length and thickness.

002040
MP joint angle of index finger60Figure 2: Example of MP and PIP Joint Angle of Index Finger

3.2 Candidate Reduction
If one of the representative hand motions is similar to another one,it may not occur good estimation. If the number of the motions ofcandidate 1 can be reduced with enough result, we can remove theredundant computation. So we sampled the sensor values for thecandidate 1 and standardize them (mean 0 and variance 1). Thenwe performed hierarchical cluster analysis for them using the ward
method to create a dendrogram about the candidate 1 (Fig. 3). The
hand motions were classified into 4 classes based on the cutting
point 2.5 as a middle distance. The classes are defined as following;
C1 : Standard，C2 : Hook-like, Lateral Contact, Index Ext, C3 : Tripod, Tip Contact，and C4 : Parallel Ext. Thereby Standard, Lateral
Contact, Tripod and Parallel Ext were selected as candidate No.2
according to the score. Furthermore we constructed average handmotions MC2 and MC3 for the classes C2 and C3 respectively (Fig.
4), and obtained candidate No.3.
012345

Standard
Hook-like
Lateral Contact

Figure 5: Appearance of Experiment System
Table 1: Joint Angle Error (Input: representative one) [degree]ThumbIndex

MiddleRingLittle

average

Candidate 1

8.3

5.7

3.9

3.4

6.5

5.6

Candidate 2

5.9

2.5

3.2

4.9

3.0

3.9

Candidate 3

7.2

4.2

3.2

4.5

4.2

4.7

Table 2: Joint Angle Error (Input: except representative one) [degree]
Thumb

Index

MiddleRing

Littleaverage

Candidate 1

8.5

9.4

9.1

Candidate 2

9.4

9.6

9.6

6.5

18.5

10.4

6.5

17.55

Candidate 3

8.2

8.7

8.7

10.5

7.4

12.22

9.0

Index Ext
TripodTip Contact
Parallel ExtFigure 3: Dendrogram of the Candidata 1

ACKNOWLEDGEMENTS
We thank our colleagues in our laboratory for useful discussions.
This work was supported in part by JSPS KAKENHI Grant Number24501186.
R EFERENCES

4E XPERIMENT AND C ONCLUSION

We constructed experiment system for the three candidates using
the 5DT Data Glove 5 Ultra which has a bend sensor for each finger. It draws CG image based on the obtained finger joint angles(Fig. 5). When the input data are the representative motions inthis experiment, the averages of estimated ratio r p are; candidate 1:[1] H. Takahashi, K. Funahashi, “A data adjustment method of low-priced
data-glove considering distribution of sensor values”, Proc. IEICE General Conference 2013 (Engineering Sciences Society), p. 216, 2013. (inJapanese)[2] N. Kamakura, M. Matsuo, H. Ishii, F. Mitsuboshi, Y. Miura, “ Patternsof static prehenshion in normal hands”, Am J Occup Ther 34, pp. 437445, 1980.