2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2020)

HumTouch: Kernel Regression-based Localizationof Touch on a Paper
Tzu-Hsuan Hsia, Shogo Okamoto, Yasuhiro Akiyama, and Yoji Yamada
Department of Mechanical Systems Engineering, Nagoya University, Nagoya, Japan

Abstract—Herein, we propose a method for localizing touchon semi-conductive materials such as paper. For this purpose, weuse the principle of HumTouch, wherein hum-originated signals
in the human body leak through the touched surface and are
detected by electrodes on the surface of the material, to turn thesurfaces of everyday objects into touch-sensitive surfaces. We
employ four electrodes and a 19 × 16 cm square sheet of paperwith hydrogel ink as the representative material. In addition,
to rectify the distortion of the signals detected at each electrode,kernel regression analysis is used. The actual locations where thepaper is touched and the detected signals are non-linearly linked,
and its performance is tested by leave-one-out cross validation.
The mean error of the predicted locations is 0.88 cm, which issmaller than the size of the human ﬁnger pad. This shows thatthe proposed method has the potential for practical applications
with regard to the localization of touch.
Index Terms—Hum noise, touch sensor, localization

I. I NTRODUCTION
The advancement of touch sensors can provide a more direct
way for humans to use computer interfaces. Although it has
been widely used in commercial products such as cellphones
and laptops, HumTouch [1], [2] can be used to provide a brandnew application for touch sensing techniques by turning objects in daily environments into touch-sensitive surfaces. The
AC power lines in buildings can generate 50/60 Hz electric
signals in human bodies, which are conductive, owing to thepresence of internal minerals and salts. When a bare ﬁnger
touches a conductive or semi-conductive material, a current
ﬂows from the human body to the surface of the material. By
detecting this signal via electrodes attached on the surface, therecognition of the occurrence of touch events [1] and ﬁnger
gestures [2] can be achieved. The HumTouch technique is apassive method that does not apply any energy on the surface;hence, it can be applied to a large surface area without theconcern of energy consumption.
In previous research, the HumTouch technique was usedfor making a keyboard [1]. Furthermore, it was demonstrated
that this technique can be applied to semi-conductive materials
such as paper by using hydrogel ink and is successful atrecognizing gestures [2]. By placing multiple electrodes on
the surface of the material, it is possible to localize touch on
the surface; however, because of the impedance properties ofthe material, the use of a simple localization method will leadto inaccurate localization [3], [4].
One quick solution to improve the localization accuracy isto increase the number of electrodes on the surface of theThis study was in part supported by the Mazda foundation.

978-1-7281-7063-3/20/$31.00 ©2020 IEEE(i, j) = (1 , 1 ) (1 , 7 )C h. 1
2 .3 5 c m

C h. 2yx(i, j) =(7 , 1 )19 cm
C h. 4

C h. 3Fig. 1. The semi-conductive wiping paper with four electrodes. The surface
was marked with 7 × 7 grid points. The distances between neighboring points
were 20 mm and 23.75 mm along the x and y-axes, respectively. The points
are speciﬁed by a pair of row and column indices (i, j).

material [3]. However, in this study, nonlinear localization
correction is applied, and its effectiveness is investigated.
With such a localization method, the HumTouch technique
is expected to function well even with a small number ofelectrodes, leading to the cost-effective and accurate touch
sensors based on hum.
II. M ATERIALS , APPARATUSES , AND EXPERIMENTS
We used a 16 × 19 cm wiping paper that was soaked
into a semi-conductive hydrogel paint and completely dried.
This material was also used in [2], in which the compounds
in the hydrogel paint are described. An oscilloscope (HS6DIFF, TiePie Engineering, Netherlands) was used to record thevoltages at the four electrodes, each of which was attached toeach corner of the paper as shown in Fig. 1. We applied a lowpass ﬁlter with the cut-off frequency of 100 Hz to the signals
and recorded the maximum magnitude that occurred at eachchannel during the ﬁnger–material contact period of 1 s asvi . The range of vi recoreded ranged approximately 6–0.7 V.
It should be noted that in principle, it is sufﬁcient to record
half a signal cycle, i.e., 8–10 ms to determine the maximum
voltage value. However, such a period is too short to ensurecomplete contact between the ﬁngertip and the material, andthe minimally required recording period is still unknown. In
the present study, to avoid such a discussion, we recorded thesignals for a longer period.

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Seven by seven grids were drawn on the paper to form 49points. Each of these points were statically touched by a man
using his index ﬁnger for 1 s in a single set; this was repeatedseven times to obtain seven sets in total. The contact forceswere not restricted.

(i, j) = (1 , 1 )(i, j) = (1 , 7 )0 .4

III. L OCALIZATION ALGORITHM USING KERNELREGRESSION ANALYSIS

Four electrodes record four different voltages (va , wherea = 1, 2, 3, 4) that primarily depend on the distance between
the electrode and the ﬁnger. We calculate the location ratiop = (px , py )T :−v1 + v2 + v3 − v4
v1 + v2 + v3 + v4
v1 + v2 − v3 − v4py =v1 + v2 + v3 + v4px =p y (-)

-0 .4

m
∑

(3)

αyl k(p(l) , pij ).

(4)(7 , 7 )0p x (-)

0 .4

Fig. 2. Mean location ratios for all tested points(2)

αxl k(p(l) , pij )(7 , 1 )
-0 .4

(1)

where px and py represent the dimensionless x and y coordinates, respectively.
The actual physical coordinates of the marked point (i, j)
are denoted by tij = (tij,x , tij,y )T , where the origin lies on thegeometric center of the paper. Here, i and j are the indices of
the 49 points. (i, j) = (1, 1) and (7, 7) were the most upward
point in the left side of the paper and the most downward
point in the right side of the paper, respectively.
We linked tij and pij via a kernel regression method. The
equation to predict the actual location tij corresponding to pij
ist̃ij,x =010(i, j) = (1 , 7 )(i, j) = (1 , 1 )5t y (c m ) 0-5(7 , 7 )-1 0-50

510t x (c m )l=1t̃ij,y =m∑
l=1Fig. 3. Means and standard deviations of the predicted coordinates of 49
points after the application of leave-one-out cross validation. Intersections of
the dotted grids are the 7 × 7 points that were actually touched.

′k(p, p ) is a Gaussian kernel:k(p, p′ ) = exp(−∥ p − p′ ∥ ).
2

Furthermore, m is the number of samples used for training.
We used six out of the seven data sets for training; hence,m = 49×6 = 249. The coefﬁcients αxl and αyl are computedusing
αx = (K + λI)−1 tx
αy = (K + λI)−1 ty

IV. R ESULTS

(5)

(6)
(7)

where αx and αy are the vectors comprising αxl and αyl(l = 1, ..., m), respectively. K is a m × m matrix formed byk(pij , pi′ j ′ )(i, j, i′ , j ′ = 1, ..., 7). tx and ty are the vectors of
the actual x- and y-axial coordinates of the samples used fortraining. The regularization term λ was set to 0.001, and I
is the identity matrix. We then performed leave-one-out crossvalidation to test the effectiveness of the proposed method. We
built the model using 6 data sets and then tested the modelusing the data set that was not used for training.

Fig. 3 (top) shows the mean location ratios pij for eachof the 49 points. Each point represents the mean of theseven trials. The bottom ﬁgure shows the means and standard
deviations of tij for the seven cross validation tests. The
mean error of the predicted location was 0.88 cm, which issmaller than the width of the human ﬁngertip. Nonetheless,the prediction was still skewed, especially near the corners
and the electrodes.
V. C ONCLUSIONSIn this study, we proposed a method to localize touch ona paper using an AC-hum-based technique. By applying thekernel regression method, 7 × 7 grid points on a paper werelocalized with a mean error of 0.88 cm. This technique isapplicable to semi-conductive materials such as treated paper,and its localization error is smaller than the size of a ﬁngertip.

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R EFERENCES[1] H. Elfekey, H. A. Bastawrous, and S. Okamoto, “A touch sensing
technique using the human body extremely low frequency ﬁelds,” Sensors,
vol. 16, no. 12, 2016.
[2] K. Tachi, S. Okamoto, Y. Akiyama, and Y. Yamada, “Humtouch: Finger
gesture recognition on hydrogel-painted paper using hum-driven signals,”in Proceedings of IEEE Global Conference on Consumer Electronic,
2019, pp. 157–159.
[3] H. Elfekey, S. Okamoto, and Y. Yamada, “Localization of touch on granite
based on ac hum noise,” in Proceedings of IEEE Global Conference onConsumer Electronic, 2017, pp. 163–164.
[4] H. Elfekey and S. Okamoto, Turning surfaces into touch panels:
A Granite-touch pad, ser. Lecture Notes in Electrical Engineering.
Springer, 2016, vol. 432, pp. 301–304.

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