Communication

HumTouch: Localization of Touch on Semi-Conductive
Surfaces by Sensing Human Body Antenna Signal
Tzu Hsuan Hsia, Shogo Okamoto * , Yasuhiro Akiyama and Yoji Yamada
Graduate School of Engineering, Nagoya University, Nagoya 464-8603, Japan;
hsia.tzu-hsuan@i.mbox.nagoya-u.ac.jp (T.H.H.); yasuhiro.akiyama@mae.nagoya-u.ac.jp (Y.A.);
yoji.yamada@mae.nagoya-u.ac.jp (Y.Y.)* Correspondence: shogo.okamoto@mae.nagoya-u.ac.jp

Abstract: HumTouch is a touch sensing technology utilizing the environmental electromagnetic
wave. The method can be realized using conductive and semi-conductive materials by simply
attaching electrodes to the object’s surface. In this study, we compared three methods for localizing atouch on 20 × 16 cm2 and 40 × 36 cm2 papers, on which four or eight electrodes were attached torecord the voltages leaked from the human fingertip. The number and positions of the electrodes andthe data processing of the voltages differed according to the localization methods. By constructing akernel regression analysis model between the electrode outputs and the actual physical locations,the touched locations were estimated. Each of the three methods was tested via leave-one-out crossvalidation. Out of the three methods discussed, two exhibited superior performances in terms of
the estimation errors. Of these two methods, one simply uses the voltages recorded by the fourelectrodes attached on the middle of paper edges as inputs to the regression system. The other uses
differential outputs of electrode pairs as the inputs. The smallest mean location errors were 0.31 cm
on 20 × 16 cm2 paper and 0.27 cm on 40 × 36 cm2 paper, which are smaller than the size of a fingertip.
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Keywords: human touch sensing; human antenna; flexible touch sensing surfaces

Citation: Hsia, T.H.; Okamoto, S.;
Akiyama, Y.; Yamada, Y. HumTouch:
Localization of Touch on
Semi-Conductive Surfaces by Sensing
Human Body Antenna Signal. Sensors
2021, 21, 859. https://doi.org/
10.3390/s21030859
Received: 23 December 2020
Accepted: 26 January 2021
Published: 28 January 2021
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Touch sensing techniques are widely used in consumer electronics such as smartphones and laptop computers. These touch sensing techniques provide a more intuitive
way to interact with the computer interface. Capacity sensing methods are a common technology used in these commercial devices [1–3]. These sensing methods use a specialized
surface that is constructed using multiple layers of different materials. This method has
been developed for years and is considerably mature and reliable. However, the numberof studies focusing on transforming daily objects into touch-sensitive objects has been
increasing [4–20]. Acoustic waves are used for localizing touch on rigid planes [4,5]. The
disturbance source, i.e., finger, is located on a vibrating surface. This method requires
transmitters to generate the acoustic wave and receivers to record the vibration of thesurface. For flexible objects, the resistance and capacitance of materials, e.g., conductive
fabrics and conductive rubber, are used for touch sensing [6–10]. When the surface deforms,
the electrical properties of such materials change accordingly. Therefore, these materials
are often used as pressure sensors and flexion sensors. Cameras are also used for touch
localization [11–15]. This optical method does not depend on the surface material but islimited owing to the issue of occlusion. Electric tomography is used for conductive materials or insulative objects with conductive coating [16–19]. This method uses a relatively
large number of electrodes. The current from one electrode to another changes with theconductive state of the surface. These methods [4–13,16–21] can potentially make daily
objects, such as furniture and toys, touch-sensitive.
In this study, we focus on a passive sensing method named HumTouch. Unlike theprominent methods that require surface activation, HumTouch utilizes environmental

Sensors 2021, 21, 859. https://doi.org/10.3390/s21030859

https://www.mdpi.com/journal/sensors

Sensors 2021, 21, 859

2 of 13noise. Nowadays, AC power lines are widespread in our surroundings that generate
50/60 Hz electromagnetic waves. Further, the human body contains minerals and otherconductive materials that react to the electromagnetic waves, inducing currents on theskin. These currents can be used for gesture recognition and human localization in a 3D
space [22–24]. When a finger touches a conductive or semi-conductive material, the current
leaks from the human body into the material surface. The voltage of this current canbe detected via electrodes attached on the material, as shown in Figure 1a,b. Note thatthe positive electrodes are attached to the material surface, while the negative ones are
grounded. Further, the human body is virtually coupled to the ground. In our pervious
works, the application of HumTouch was validated for the development of a keyboard [25].
This method can be used with natural conductive materials such as granite [26,27]. For
insulative materials, a semi-conductive hydrogel-paint for application to a 2D or 3D surface
is available [28–30]. HumTouch does not require surface activation, which renders it
different from other touch sensing methods. Hence, it can be potentially used with largearea surfaces and objects with limited volumes.
In our previous study, touch localization was achieved by constructing a kernel
regression model with preprocessed voltages recorded at the electrodes [29,30]. The mean
estimated errors in our former studies were 0.88 cm on a 16 × 19 cm2 paper and 0.22 cm
on a 30-cm-long cylinder with the dimeter of 3 cm. These results motivated us to extend
our research and investigate the preprocess methods for the collected voltages and thenumber and locations of electrodes. Herein, we demonstrate three different localization
methods that use multiple electrodes and kernel regression analyses. The three methods
are different in terms of the preprocessing of recorded voltages. One method does not
apply any preprocessing. For the other two methods, linear combinations of the voltages
are used for the input to the kernel regression analysis. Each of the three methods is tested
on both 20 × 18 cm2 and 40 × 36 cm2 papers. Finally, we determine the best localization
method for 2D HumTouch surfaces.

(a)

(b)
Oscilloscope

Oscilloscope

(c)

(d)

Figure 1. (a) Eight electrodes were attached to a semi-conductive paper. (b) A circuit was formed
when a human touched the paper. (c) Photo of the paper (40 × 36 cm2 ) with the electrodes attached.
(d) The paper was dry, flexible, and durable.

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2. Materials and Methods
We painted a semi-conductive hydrogel on two different-sized wiping papers(20 × 18 cm and 40 × 36 cm, Kimtowel, Nippon Paper Crecia, Tokyo, Japan). The hydrogel [28] was prepared using the following constituents: 15 g of polyvinyl alcohol,
300 mL of ultrapure water, 75 mL polyethylene glycol 400, and 37.5 mL Glutaraldehyde.
The solution was stirred for approximately 3 h at 90 ◦ C. Then, the wiping papers weredried at room temperature for a week. After being dried, the papers remained flexible, andthus the strength does not apparently change. Nevertheless, we did not measure the exact
change in the strength and stiffness after painting the hydrogel.
To measure the current, we attached eight electrodes to the paper as shown in Figure 1.
An oscilloscope (HS6DIFF, TiePie, Netherlands; sampling frequency: 500 kHz) was used torecord the voltage at each electrode. For each channel of the oscilloscope, one electrode
was connected to the paper, and the other electrode was grounded.
We marked 7 × 7 points (49 total points) on both papers with a marker pen. As Figure 2
shows, the intervals between each point were 2.5 cm and 2.25 cm for the smaller paperand 5 cm and 4.5 cm for the larger paper. The papers were placed on a non-conductive
wooden table inside a regular office room. A participant was asked to touch each of the
49 points with his bare index finger. The contact force was not fixed because the voltage
barely changed with the contact force as shown in Figure 3, where the point that was
12.8 cm away from an electrode on a paper was repeatedly touched with the normal forces
varying 1–8 N. The contact period for each point was approximately 1 s. This procedure
was repeated seven times (seven data sets).

Figure 2. Points marked on the 20 × 16 and 40 × 36 cm2 paper.
0.4

0.35

Voltage (V)

0.3
0.25
0.2
0.15
0.1
0.05

0

1

2

3

4

5

6

7

8

Force (N)Figure 3. Voltages recorded at an electrode for different contact forces. The distance between theelectrode and touched location was 12.8 cm. Five voltage values were recorded for each force.

Sensors 2021, 21, 859

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3. Localization Using Kernel Regression Model
Kernel Regression Analysis
We applied kernel regression analysis as the model structure for our observations
is unknown; however, the voltage at an electrode nonlinearly changes with the distance
between the finger and electrode. The independent observations for learning the modelare (x1 , y1 ), ..., (xj , y j ), ..., (xn , yn ) where xj is the input vector, y j is the output scalar for thejth sample and n is the number of samples for learning. When the input vector is x, theestimated output ỹ is defined asnỹ = ∑ α j k(xj , x)

(1)

j =1

where k(xj , x) is a kernel function. Coefficient αj is a component of vector α = (α1, ..., αj , ..., αn )T,
which is determined using
y1
 .. 
.
 −1  
α = (K + λI )  y j .
. .. 
(2)ynMatrix K ∈ Rn×n is formed by k(xi , x j )(i, j = 1, 2, ..., n), λ is the regularization value,and I is the identity matrix of n × n.
In our study, the location for each point was defined by the Cartesian coordinate
system. We applied the kernel regression for each axis. Therefore, for the input vector x,
the estimated two-dimensional location (l˜x , l˜y )T for each point can be written as˜   n∑ j=1 α xj k(xj , x)
lx

 =


l˜y
∑nj=1 αyj k(xj , x)(3)where the coefficients α xj and αyj are the jth components of vectors α x and αy , which arecomputed by
α x = (K + λI)−1 lx
αy = (K + λI)

−1

ly .

(4)
(5)

Here, lx = (lx1 , lx2 , ..., lxn )T and ly = (ly1 , ly2 , ..., lyn )T are the actual x and y locations
for the touched points. The kernel function used in our study is the Gaussian kernel,
which isk (xj , x) = exp(−|xj − x|2 )(6)

where |.| is the L2 norm. The regularization value λ was selected from 1 to 10−3 such thatthe smallest estimation error on average was achieved.
4. Three Voltage Processing Methods for Regression Model
4.1. Data Prepossessing Common for the Three Methods
A moving average filter with a span of five, i.e., a window size of 0.01 ms for a 500 kHZsampling frequency of the oscilloscope, was used to smooth the signals recorded by theelectrodes. The maximum voltage recorded by channel i during 1 s is vi and is used for the
localization methods introduced in Sections 4.2–4.4.

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5 of 134.2. Method 1: Simple Regression
In this method, we used the voltage preprocessed in Section 4.1 as the input for the
regression model. We constructed three slightly different regression models using thismethod. One model used the data collected by eight electrodes as the input. Therefore, theinput was xj = (v1j , v2j , ..., v8j )T , where vij is the voltage output from the ith channel of sample j. Another model used the voltages recorded on the four corners xj = (v1j , v2j , v3j , v4j )as the input, and the other used those on the middle of each edge xj = (v5j , v6j , v7j , v8j ).
The channel numbers and positions are shown in Figure 4. The model using eight channels
is expected to be more accurate than the models using four channels.

𝑦

𝑣1

𝑣6

𝑣2

𝑥

𝑣5
𝑣4

𝑣7
𝑣8

𝑣3

Figure 4. Loci of electrodes on the paper.

4.3. Method 2: Regression Using Voltage Ratio
In our previous study [29], four electrodes were attached to the corners of a square
paper. The localization was conducted via kernel regression using the voltage ratio definedby x = ( p x , py )T :
 −v +v +v −v 
1j
2j
3j
4j
 
p xj
 v1j +v2j +v3j +v4j 
xj =   = (7)
 v + v − v − v .
1j
2j
3j
4j
pyj
v1j +v2j +v3j +v4j

These voltage ratios represent the touched location if the relationship between thechannel output and distance between the electrode and fingertip is linear. However, therelationship is non-linear in reality, and we corrected this non-linearity by applying kernel
regression analysis in [29].
In this study, we increased the number of channels to eight. Hence, we added two
more parameters p x2 and py2 using the voltage records at the middle of edges, i.e., v5 , v6 , v7
and v8 . The input vector for sample j was x j = ( p x1j , py1j , p x2j , py2j )T , where −v +v +v −v 
1j

2j

3j

4j

 v1j +v2j +v3j +v4j 

 


 

v1j +v2j −v3j −v4j p  
 y1j   v1j +v2j +v3j +v4j 

 

.
xj = =  −v5j +v6j +v7j −v8j  p x2j    v5j +v6j +v7j +v8j 


 



p
v +v −v −v


p x1j

y2j



5j

6j

7j

(8)

8j

v5j +v6j +v7j +v8j

We expected the regression model built using the voltage ratio to be more accurate
than the simple regression method because the input was related to the coordinates on thepaper for the regression analysis.

Sensors 2021, 21, 859

6 of 134.4. Method 3: Regression Using Differential Output of Electrode Pair
We propose another method that utilizes the difference of preprocessed voltages
between two channels as the input for regression models. The concept of this method
is that although the magnitude of the voltage may be different among trials owing toenvironmental differences even if the same position is touched on the paper, the difference
between the two channels’ outputs may be same among trails. Hence, the use of twochannels’ difference is expected to lead to a smaller estimation error than the simple
regression method. We used three different arrangements of the electrodes. One used thevoltages measured at corners (v1 , ..., v4 ). The input for sample j was defined as
  

c1j
v1j − v2jc  v − v  2j   1j
3j 
c  v − v  3j   1j
4j 
xj =   = (9).
c4j  v2j − v3j   c5j  v2j − v4j 
c6j
v3j − v4j
Another arrangement used the voltages measured at the middle of edges (v5 , ..., v8 ).
In this case, the input for sample j was
  

d1j
v5j − v6jd  v − v  2j   5j
7j 
d  v − v  3j   5j
8j 
xj =   = (10).
d4j  v6j − v7j   d5j  v6j − v8j 
d6j
v7j − v8j
Furthermore, we tested the input computed by the eight electrodes. The input for
sample j contained 28 elements (8 C2 ) and was defined as
 

e1j
v1j − v2j
 e2j  v1j − v3j  xj =  .  = .
..
 ..  
.
e28j
v7j − v8j

(11)

5. Results
5.1. Performance Evaluation Method
We applied the leave-one-out cross validation method to investigate the estimation
error. Six of the seven data sets (294 out of 343 points) were used to build the regression
model and the remaining data set (49 out of 343 points) was used to test the estimation
performance. This procedure was conducted for every data set (repeated seven times), andthen, the means and standard deviations of the errors were calculated.
5.2. Simple Regression
The points shown in Figure 5 are the mean estimated locations and the standard
deviations for individual points. The intersections of the grids denote the actually touchedlocations. Figure 5a,b are the results when the eight electrodes were used for localization.
The estimation error was 0.48 ± 0.10 cm for the 20 × 16 cm2 paper and 1.39 ± 0.55 cm forthe 40 × 16 cm2 paper. For both papers, the estimation error and the standard deviation forthe points near the edges were larger than those of the points near the center of the paper.
Figure 5c,d present the results when the four electrodes were placed on the corners(x = (v1 , ..., v4 )). The mean estimation errors were 0.84 ± 0.18 cm and 2.15 ± 0.63 cm forthe 20 × 16 cm2 and 40 × 36 cm2 paper, respectively.

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Figure 5e,f illustrate the results when the electrodes were placed on the middle ofedges (x = (v5 , ..., v8 )). The mean estimation errors were 0.36 ± 0.07 cm and 0.27 ± 0.10 cm
for the 20 × 16 cm and 40 × 36 cm2 paper, respectively. For the 20 × 16 cm2 paper, theestimation error was larger for the points close to the edges than those near the center ofthe paper.
Paper
size

20 × 16 cm2(a)errors = 0.48 ± 0.10 cm

40 × 36 cm2(b)errors = 1.39 ± 0.55 cm

Eightelectrodes
−5

−10

−10
−15

(c)

−20

−10

errors = 0.84 ± 0.18 cm

(d)

−10

errors = 2.15 ± 0.63 cm

Fourelectrodes(corner)

−5

−10

−10
−15

(e)

−20

−10

errors = 0.36 ± 0.07 cm

(f)

−10

errors = 0.27 ± 0.10 cm

Fourelectrodes(middle ofeach edge)

−5

−10

−10
−15

−10

−20

−10

Figure 5. Results of the simple regression methods. (a,c,e) show the results for the 20 × 16 cm2 paper. (b,d,f) show theresults for the 40 × 36 cm2 paper. (a,b) show the results obtained using eight electrodes. (c,d) shows the results obtainedusing four electrodes placed in the corners of the paper. (e,f) show the results obtained using four electrodes placed in the
middle of the edges of the paper. The points denote the mean estimated locations for the intersection points, and the error
bars denote the standard deviations.

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8 of 135.3. Voltage Ratios
Figure 6a,b present the results for the method using voltage ratios calculated by theeight electrodes. The estimation errors were 0.31 ± 0.09 cm for the smaller paper and0.69 ± 0.19 cm for the lager paper.
Figure 6c,d illustrate the results for the method using the four electrodes on corners.
The mean estimation errors were 0.93 ± 0.32 cm for the 20 × 16 cm2 paper and 3.26 ± 0.69 cm
for the 40 × 36 cm2 paper. For the 20 × 16 cm2 paper, the estimation errors for the points
near edges are larger than those near the center of the paper. The estimation errors for the
40 × 36 cm2 paper were evidently larger than those for the smaller paper.
Figure 6e,f present the results for the method using four electrodes placed on the
middle of edges. The estimation errors were 0.99 ± 0.79 cm for the smaller paper and1.08 ± 0.12 cm for the lager paper.
Paper
size

20 × 16 cm2(a)

40 × 36 cm2(b)errors = 0.31 ± 0.10 cm

errors = 0.69 ± 0.19 cm

Eightelectrodes
−5

−10

−10
−15

(c)

−20

−10

errors = 0.93 ± 0.32 cm

(d)

−10

errors = 3.36 ± 0.69 cm

Fourelectrodes(corner)
−5

−10

−10
−15

(e)

−20

−10

errors = 0.99 ± 0.79 cm

(f)

−10

errors = 1.08 ± 0.12 cm

Fourelectrodes(middle ofeach edge)

−5

−10

−10

−15

−10

−20

−10

Figure 6. Results of the voltage ratio method obtained using four or eight electrodes. (a,c,e) show the results for the
20 × 16 cm2 paper. (b,d,f) show the results for the 40 × 36 cm2 paper. (a,b) show the results obtained using eight electrodes.
(c,d) show the results obtained using four electrodes placed in the corners of the paper. (e,f) show the results obtainedusing four electrodes placed in the middle of the edges of the paper. The points denote the mean estimated locations for theintersection points, and the error bars denote the standard deviations.

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9 of 135.4. Differential Output of Electrode Pair
Figure 7 shows the results for the differential output methods. Figure 7a,b show theestimation based on the corner electrodes, for which the localization of the touched points
was failed. The estimation errors were 2.42 ± 0.21 on the small paper and 4.30 ± 0.82 on thelarge paper. As shown in Figure 7c,d, the estimation errors for the method using the fourelectrodes at corners for the 20 × 16 cm2 and 40 × 36 cm2 papers were 1.16 ± 0.19 cm and2.39 ± 0.53 cm, respectively. As shown in Figure 7e,f, for the method using the four electrodes on the middle of edges, the estimation errors were 0.50 ± 0.13 cm and 0.31 ± 0.12 cm
for the 20 × 16 cm2 and the 40 × 36 cm2 paper, respectively.
5.5. Summary and Comparison of Three Methods
Table 1 shows the mean estimation error and standard deviation for each method.
The simple regression method using the electrodes on the middle of edges, voltage ratio
method using eight electrodes, and differential output method using the electrodes on the
middle of edges exhibited relatively small localization errors.
Table 1. Mean estimation error and standard deviation (cm) for each method.

Localization Method

Number of Electrodes

20 × 16 cm2 Paper

40 × 36 cm2 Paper

8
4
4
8
4
4
8
4
4

0.48 ± 0.10
0.84 ± 0.18
0.36 ± 0.07
0.31 ± 0.09
0.93 ± 0.32
0.99 ± 0.79
2.42 ± 0.21
1.16 ± 0.19
0.50 ± 0.13

1.39 ± 0.55
2.15 ± 0.63
0.27 ± 0.10
0.69 ± 0.19
3.36 ± 0.69
1.08 ± 0.12
4.30 ± 0.82
2.39 ± 0.53
0.31 ± 0.12

Simple regression
Simple regression (corner)
Simple regression (middle of edges)
Voltage ratio
Voltage ratio (corner)
Voltage ratio (middle of edges)
Differential output
Differential output (corner)
Differential output (middle of edges)

We tested the statistical differences in the mean errors between the method with thesmallest error and the others for each type of the paper, and the results are summarized inTable 2. For the small paper, the method based on the voltage ratio of the eight electrodes,
exhibited smaller errors than the other methods except for the simple regression methodand differential output method using the electrodes on the middle of edges. In contrast,
the simple regression method using the middle of edges showed the smallest error for thelarge paper. The error was smaller than those of the other methods expect for the simple
regression method using eight electrodes, the voltage ratio method using eight electrodes,
the voltage ratio method using electrodes on the middle of edges, and the differential
output method using the electrodes on the middle of edges.
Table 2. t and p values for each paper when the method with the smallest error and the others are statistically compared byt-tests without correction of p values. The degrees of freedom were six for all tests.

Localization Method
Simple regression with 8 electrodes
Simple regression (corner)
Simple regression (middle of edges)
Voltage ratio with 8 electrodes
Voltage ratio with 4 electrodes (corner)
Voltage ratio with 4 electrodes (middle of edges)
Differential output with 8 electrodes
Differential output (corner)
Differential output (middle of edges)

20 × 16 cm2 Paper

40 × 36 cm2 Paper

t

p

t

p

2.97
2.78
0.30
N/A
8.20
3.52
9.25
3.87
1.72

0.025
0.032
0.78
N/A
0.00020.013
0.00009
0.008
0.14

1.83
2.78
N/A
0.72
3.82
−0.84
3.78
2.48
1.064

0.12
0.032
N/A
0.50
0.009
0.43
0.00920.036
0.33

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Paper
size

40 × 36 cm2

20 × 16 cm2(a)error = 2.42 ± 0.21 cm(b)error = 4.30 ± 0.82 cm

Eightelectrodes
−5

−10

−10
−15

error = 1.16 ± 0.19 cm

(c)

−20

−10

−10

error = 2.39 ± 0.53 cm

(d)

Four
electrodes−5(corner)

−10

−10
−15

error = 0.50 ± 0.13 cm

(e)

−20

−10

−10

error = 0.31 ± 0.12 cm

(f)

Four
electrodes(middle ofeach edge)

−5

−10

−10
−15

−10

−20

−10

Figure 7. Results of the differential output methods. (a,c,e) show the results for the 20 × 16 cm2 paper. (b,d,f) show theresults for the 40 × 36 cm2 paper. (a,b) show the results obtained using eight electrodes. (c,d) show the results obtainedusing four electrodes placed in the corners of the paper. (e,f) show the results obtained using four electrodes placed in the
middle of the edges of the paper. The points denote the mean estimated locations for the intersection points, and the error
bars denote the standard deviations.

6. Discussion
The following three methods resulted in small localization errors: the simple regression method using the middle of the edges of the paper; the voltage ratio method usingeight electrodes; and the differential output method using the middle of the edges ofthe paper. These methods did not exhibit statistical differences. The mean localization

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11 of 13errors of three methods were smaller than a fingertip. These errors can be compared withthose obtained by other touch sensing methods such as an electric tomography methodwith maximum mean localization errors of 10 mm and a method using an inertial measurement unit sensor with a maximum position error of 2.5 mm [19,31]. Among all the
localization methods, the simple regression method using the middle of the edges of thepaper is better than the other methods because of the low number of electrodes and mean
localization errors.
The points near the edges incur larger estimation errors and standard deviations thanthose near the center of the papers. This can be explained based on the relationship between
the measured voltages and finger-electrode distances, shown in Figure 8. The points shownin the figure denote the mean voltages measured at different distances from an electrode,and the error bars denote the standard deviation. For the points near the edges, the distance
between the finger and electrode is either laid on highly sensitive distances or less sensitive
distances. In the highly sensitive range, the voltage considerably varies when the location
touched by the finger slightly varies, causing large standard deviations. In contrast, in theless sensitive range, the voltage does not evidently vary even when the location touchedby the finger substantially varies. Therefore, the estimations for the points near edges
are inaccurate.

Figure 8. Voltages measured at an electrode for a finger placed at different distances. The points
denote the mean voltages for the seven measurements obtained, and the error bars denote thestandard deviation.

In general, the mean estimation error for the 40 × 36 cm2 paper was larger than thatfor the 20 × 16 cm2 paper. The mean estimation errors for the 40 × 36 cm2 and 20 × 16 cm2
papers were 1.77 cm and 0.89 cm, respectively. The dimensions of the larger paper were
nearly twice those of the smaller paper. This indicates that the lager are the papers, thegreater are the localization errors. This could be because, for the larger paper, the distances
between the touched locations and electrodes are large, and the sensitivity of the output
voltage is low.
For the differential output method, the estimation errors obtained when using fourelectrodes are smaller than those obtained using eight electrodes, which disagrees with our
initial expectation. This could potentially be because of the overlearning of the regression
model. For the method using eight electrodes, the number of explanatory variables was
28 (8 C2 ), while that for the methods using four electrodes was six. The model establishedbased on the differential output method using eight electrodes might have been extremely
specific to the data used for learning and not for general purposes.
HumTouch utilizes the environmental noise that cannot be easily replicated; thus, thelocalization results could depend on the participant and environment. In the future, theSensors 2021, 21, 859

12 of 13regression models constructed based on different participants and environments need to becompared. Further, although we used the Gaussian kernel functions, these functions were
not optimized. Better kernel functions that could help obtain more accurate results can be
determined. An equivalent circuit model of HumTouch is yet to be established and studied.
Once the model is established, a computational optimization of electrode positions andnumbers can be realized.
7. Conclusions
HumTouch is a passive sensing technology that can be used for semi-conductive
materials; furthermore, this technology does not require surface activation or specialized
surface structures. In this study, the localization of touch via the HumTouch technology
was realized on two different-sized papers (20 × 16 cm2 and 40 × 36 cm2 ). Three different
localization methods were tested on both papers for different electrode positions. The
simple regression method using electrodes at the middle of the edges of the paper, thevoltage ratio method using eight electrodes, and the differential output method using
electrodes at the middle of the edges of the paper exhibited smaller localization errors thanthe other methods. The smallest errors for the 20 × 16 cm2 and 40 × 36 cm2 paper were0.31 ± 0.09 cm and 0.27 ± 0.10 cm, respectively. These errors are smaller than a fingertip.
The simple regression method using four electrodes at the middle of the four edges of thepaper is the most commonly applied localization method since this method requires only
four electrodes and incurs small errors. In the future, we will seek applications suitablefor HumTouch.
Author Contributions: T.H.H. and S.O. have equally contributed to this study. Y.A. and Y.Y. contributed to the discussions pertaining to the analysis and experimental methods. All authors haveread and agreed to the published version of the manuscript.
Funding: This research was in part supported by Mazda Foundation.
Institutional Review Board Statement: This study was approved by the IRB of the School of Engineering, Nagoya University (#20-8).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: Data can be requested by direct email contacts to the author.
Conflicts of Interest: The authors declare no conflict of interest.

References
1.
2.
3.
4.
5.
6.
7.
8.
9.

Walker, G. A Review of Technologies for Sensing Contact Location on the Surface of a Display. J. Soc. Inf. Disp. 2012, 20, 413–440.
[CrossRef]
An, J.; Hong, S.; Kwon, O. A Highly Linear and Accurate Touch Data Extraction Algorithm Based on Polar Coordinates forLarge-sized Capacitive Touch Screen Panels. IEEE Trans. Consum. Electron. 2016, 62, 341–348. [CrossRef]
Ozioko, O.; Navaraj, W.; Hersh, M.; Dahiya, R. Tacsac: A Wearable Haptic Device with Capacitive Touch-Sensing Capability for
Tactile Display. Sensors 2020, 20, 4780. [CrossRef] [PubMed]
Firouzi, K.; Nikoozadeh, A.; Carver, T.E.; Khuri-Yakub, B.T. Multitouch Touchscreen Using Reverberant Lamb Waves. In Proceedings of the IEEE International Ultrasonics Symposium, Washington, DC, USA, 6–9 September 2017; pp. 1–4.
Chang, C.S.; Lee, Y.C. Ultrasonic Touch Sensing System Based on Lamb Waves and Convolutional Neural Network. Sensors 2020,
20, 2619. [CrossRef] [PubMed]
Shimojo, M.; Namiki, A.; Ishikawa, M.; Makino, R.; Mabuchi, K. A Tactile Sensor Sheet Using Pressure Conductive Rubber with
Electrical-wires Stitched Method. IEEE Sens. J. 2004, 4, 589–596. [CrossRef]
Mazzeo, A.D.; Kalb, W.B.; Chan, L.; Killian, M.G.; Bloch, J.F.; Mazzeo, B.A.; Whitesides, G.M. Paper-based, Capacitive Touch
Pads. Adv. Mater. 2012, 24, 2850–2856. [CrossRef] [PubMed]
Hua, Q.; Sun, J.; Liu, H.; Bao, R.; Yu, R.; Zhai, J.; Pan, C.; Wang, Z.L. Skin-inspired Highly Stretchable and Conformable Matrix
Networks for Multifunctional Sensing. Nat. Commun. 2018, 9, 1–11. [CrossRef] [PubMed]
Matsuno, T.; Wang, Z.; Althoefer, K.; Hirai, S. Adaptive Update of Reference Capacitances in Conductive Fabric Based Robotic
Skin. IEEE Robot. Autom. Lett. 2019, 4, 2212–2219. [CrossRef]

Sensors 2021, 21, 859

10.

11.

12.
13.
14.
15.
16.

17.
18.
19.
20.
21.

22.

23.

24.

25.
26.

27.
28.

29.
30.
31.

13 of 13

Valasma, R.; Bozo, E.; Pitkänen, O.; Jarvinen, T.; Dombovari, A.; Mohl, M.; Lorite, G.S.; Kiss, J.; Konya, Z.; Kordas, K. Gridtype Transparent Conductive Thin Films of Carbon Nanotubes as Capacitive Touch Sensors. Nanotechnology 2020, 31, 305303.
[CrossRef] [PubMed]
Akaoka, E.; Ginn, T.; Vertegaal, R. DisplayObjects: Prototyping Functional Physical Interfaces on 3D styrofoam, Paper or
Cardboard Models. In Proceedings of the Fourth International Conference on Tangible, Embedded, and Embodied Interaction,
Cambridge, MA, USA, 25–27 January 2010; pp. 49–56.
Chen, Y.L.; Liang, W.Y.; Chiang, C.Y.; Hsieh, T.J.; Lee, D.C.; Yuan, S.M.; Chang, Y.L. Vision-based Finger Detection, Tracking, andEvent Identification Techniques for Multi-touch Sensing and Display Systems. Sensors 2011, 11, 6868–6892. [CrossRef] [PubMed]
Liang, J.R.; Lin, K.C.; Chen, H.C.; Wang, W.J. Turn Any Display into a Touch Screen Using Infrared Optical Technique. IEEE
Access 2018, 6, 13033–13040. [CrossRef]
Suto, S.; Watanabe, T.; Shibusawa, S.; Kamada, M. Multi-Touch Tabletop System Using Infrared Image Recognition for User
Position Identification. Sensors 2018, 18, 1559. [CrossRef] [PubMed]
Sferrazza, C.; D’Andrea, R. Design, Motivation and Evaluation of a Full-resolution Optical Tactile Sensor. Sensors 2019, 19, 928.
[CrossRef]
Fulton, W.; Lipczynski, R. Body-support Pressure Measurement Using Electrical Impedance Tomography. In Proceedings of the
15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ, San Diego, CA, USA, 31 October
1993; pp. 98–99.
Booth, M.J.; Basarab-Horwath, I. Comparing Electrode Configurations for Electrical Impedance Tomography. Electron. Lett. 1996,
32, 648–649. [CrossRef]
Silvera-Tawil, D.; Rye, D.; Soleimani, M.; Velonaki, M. Electrical Impedance Tomography for Artificial Sensitive Robotic Skin:
A Review. IEEE Sens. J. 2015, 15, 2001–2016. [CrossRef]
Zhang, Y.; Laput, G.; Harrison, C. Electrick: Low-cost Touch Sensing Using Electric Field Tomography. In Proceedings of the 2017
CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, 6–11 May 2017; pp. 1–14.
Hanh, N.Q.; Reju, V.G.; Khong, A.W.H. Impact Localization on Rigid Surfaces Using Hermitian Angle Distribution for HumanComputer Interface Applications. IEEE Trans. Multimed. 2018, 20, 1448–1461. [CrossRef]
Mitsubayashi, H.; Yoshimoto, S.; Yamamoto, A. Adaptive Potential Scanning for a Tomographic Tactile Sensor with HighSpatio-Temporal Resolution. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas,
NV, USA, 24–30 October 2020; pp. 9827–9832.
Cohn, G.; Morris, D.; Patel, S.N.; Tan, D.S. Your Noise is My Command: Sensing Gestures Using the Body as an Antenna.
In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada, 7–12 May 2011;
pp. 791–800.
Cohn, G.; Morris, D.; Patel, S.; Tan, D. Humantenna: Using the Body as an Antenna for Real-time Whole-body Interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, TX, USA, 5–10 May 2012;
pp. 1901–1910.
Grosse-Puppendahl, T.; Dellangnol, X.; Hatzfeld, C.; Fu, B.; Kupnik, M.; Kuijper, A.; Hastall, M.R.; Scott, J.; Gruteser, M.
Platypus—Indoor Localization and Identification through Sensing Electric Potential Changes in Human Bodies. In Proceedings ofthe 14th Annual International Conference on Mobile Systems, Applications, and Services, Singapore, 26–30 June 2016; pp. 17–30.
Elfekey, H.; Bastawrous, H.A.; Okamoto, S. A Touch Sensing Technique Using the Effects of Extremely Low Frequency Fields on
the Human Body. Sensors 2016, 16, 2049. [CrossRef] [PubMed]
Elfekey, H.; Okamoto, S. Turning Surfaces into Touch Panels: A Granite-touch Pad. In Haptic Interaction: Science, Engineering and
Design; Lecture Notes in Electrical Engineering; Hasegawa, S., Konyo, M., Kyung, K.U., Nojima, T., Kajimoto, H., Eds.; Springer:
Berlin/Heidelberg, Germany, 2016; Volume 432, pp. 301–304.
Elfekey, H.; Okamoto, S.; Yamada, Y. Localization of Touch on Granite based on AC Hum Noise. In Proceeding of the IEEE
Global Conference on Consumer Electronics, Nagoya, Japan, 24–27 October 2017; pp. 163–164.
Tachi, K.; Okamoto, S.; Akiyama, Y.; Yamada, Y. HumTouch: Finger Gesture Recognition on Hydrogel-painted Paper Using
Hum-driven Signals. In Proceedings of the IEEE Global Conference on Consumer Electronic, Osaka, Japan, 15–18 October 2019;
pp. 157–159.
Hsia, T.H.; Okamoto, S.; Akiyama, Y.; Yamada, Y. HumTouch: Kernel Regression-based Localization of Touch on a Paper.
In Proceedings of the IEEE Global Conference on Life Sciences and Technologies, Kyoto, Japan, 10–12 March 2020; pp. 28–30.
Hsia, T.H.; Okamoto, S.; Akiyama, Y.; Yamada, Y. HumTouch: Localization of Touch on a Cylindrical Object. In Proceedings ofthe IEEE Global Conference on Consumer Electronic, Kobe, Japan, 13–16 October 2020; pp. 519–521.
Oh, J.Y.; Park, J.; Park, J. FingerTouch: Touch Interaction Using a Fingernail-Mounted Sensor on a Head-Mounted Display for
Augmented Reality. IEEE Access 2020, 8, 101192–101208. [CrossRef]