(2022) 9:24
Hsia et al. ROBOMECH Journal
https://doi.org/10.1186/s40648-022-00238-4

Open Access

RESEARCH ARTICLE

One‑touch calibration of hum‑noise‑based
touch sensor for unknown users utilizing
models trained by different users
Tzu‑Hsuan Hsia1†, Shogo Okamoto2*† , Yasuhiro Akiyama3 and Yoji Yamada4

Abstract
Hum-noise-based touch sensors (HumTouch) are capable of recognizing human touch on semiconductive materi‑als using the current leaking from the finger to the surface. Thus far, calibration for these hum-noise-based touch
sensors has been performed for individual users because of the individual differences in hum-driven electric currents
in human bodies. However, for applications designed for unknown users, time-consuming calibration for individual
users is not preferred, and a new user should be able to use the sensor immediately. For this purpose, we propose a
new calibration method for HumTouch. In this method, learning datasets collected from multiple people and a few
extra samples from a new user are collectively used to establish a touch localization estimator. The estimator is com‑puted using the kernel regression method with weighted samples from the new user. For a 20 × 18 cm2 paper, themean localization error is reduced from 1.24 cm to 0.90 cm with only one sample from the new user. Hence, a newuser can establish a semipersonalized localization estimator by touching only one point on the surface. This method
improves the localization performance of HumTouch sensors in an easy-to-access manner.
Keywords: Touch localization, Human antenna, Touch sensor
Introduction
Capacitive sensing has been adopted in most commonly-used and commercially successful touch-sensitive
methods. This sensing method detects the changes inthe electric charges within a uniform electrostatic field[1–3], and is extensively used in touch panels and buttons. Recently, touch localization technology that canbe applied to nonspecialized surfaces or objects has
attracted the attention of researchers. Most of thesetechniques are aimed at furniture and cloth, rather thantouch panels for electronic appliances. An example is theelectrical tomography method [4–7], which uses several
pairs of electrodes to apply voltages to a resistive surface.
†

Tzu-Hsuan Hsia and Shogo Okamoto are equal contributor*Correspondence: okamotos@tmu.ac.jp
2
Department of Computer Science, Tokyo Metropolitan University, Hino,
Japan
Full list of author information is available at the end of the article

The touched location can be computed by recording thechanges in the electrical impedance between the electrodes. Optical methods such as cameras, which detectand trace human fingertips, can be applied to any surface
as long as the occlusion problem is not severe [8–12]. In
addition, soft or thin pressure-sensitive sheets have beenused for constructing touch-sensitive areas on free-form
surfaces [13–17]. Other approaches include radio-frequency identification tags for sensing touch events andlocalization [18], inertial measurement unit sensors for
tracking fingertips and recognizing gestures [19], andmethods utilizing the propagation of acoustic waves insolid materials [20]. Multi-axial force sensors can also
turn objects such as furniture into touch sensitive interfaces [21–23]. Furthermore, a method based on spraycoating conductive substances can be easily applied toobjects with complex shapes for forming a touch-sensitive surface [6, 7, 24]. Moreover, wearable sensors, such

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Hsia et al. ROBOMECH Journal(2022) 9:24

as [25, 26], are a potential approach to detect a touch on
objects without sensory instruments.
We focus on a sensing method that utilizes environmental AC-hum-noise [27–33]. For example, Cohn et al.
demonstrated that the touch positions on a wall could bediscriminated by monitoring the voltage at an electrodeon human skin [27]. In their experiment, the hum-noisein the wall was detected through human skin when aperson touched the wall. The hum-noise-based sensing method employed in this study is called HumTouch[34–37], which uses the environmental noise leaking
from human fingers onto the surfaces of conductive or
semiconductive objects. Touch or finger localization isachieved based on the voltages recorded at multiple electrodes attached to the surface [35–37]. In previous studies on hum-noise-based touch sensors, calibration for
localization was performed for individual users, using
machine learning or statistical approaches comprising
support vector machines and regression analysis [27–30,
35–37]. Because of the individual differences in humanbodies, the voltages recorded on material surfaces differ
among users, leading to inaccurate localization of touch.
For certain applications, calibration for individual users
before using the sensors is acceptable or suitable; however, other applications require easy access to hum-noisebased touch sensors by unknown users with less effort for
sensor calibration.
Herein, we propose a calibration method in which
only one or a few samples from a new unknown user andmany samples collected from various people are combined to build a semipersonalized calibration model. The
extra samples from the new user are weighted to tailor
the calibration model to the user. We examine a methodthat uses 20 × 18 cm2 paper coated with conductive ink.
We investigate the suitable weighting values and the extra
samples required for improving the localization accuracy. To the best of our knowledge, a method similar toour semipersonalized method for touch sensors has not
yet been developed. This is mainly because, in principle,
most of the previous touch sensors do not require calibration for individuals because their measurement accuracy does not depend on individual users. For example,
electrical tomography methods utilize the changes in theelectrical impedance caused by the deformation of thesurface touched by the user. Hence, calibration models
are required for individual surfaces, but not for individual
users.
Touch-sensing methods utilizing hum-noise have distinctive features from other sensing methods. First, thismethod does not require any sensing structures on thesurface to be measured. It does not require any resistive
or capacitive materials and layers installed on the surface
as long as the surface material is semiconductive. Hence,

Page 2 of 12

HumTouch may allow us to turn wooden products [36],such as furniture, and those covered with paper [35, 37]
or cloth into touch-sensitive interfaces. Furthermore,
HumTouch can localize a human body in water [38]. The
second prominent feature is the passivity. Methods basedon hum-noise do not excite object surfaces by applying voltage or vibration, for example; hence, they may
be suitable for sensing large-area surfaces such as a walland floor. Camera-based touch-sensing methods [8–12]
and acoustic methods [20] also share these two features
whereas the former suffers from occlusions and the latter
is not applied for soft materials. Our calibration methodfor HumTouch is expected to realize touch-detection
functions for a variety of objects described above in public and domestic spaces.

Methods
General principle of the HumTouch sensor

Electrical grids are an essential infrastructure in modern
cities. AC power lines constantly generate electromagnetic waves known as hum-noise during the transmissionof electricity, which react with conductive or semiconductive objects nearby. The human body includes conductive substances such as minerals, which react withthese hum-noise-producing currents in the body, as
shown in Fig. 1. Therefore, when a human touches a
conductive object, current leaks from the finger onto
the object. These currents can be detected using voltage
acquisition devices attached to the surface of the object.
HumTouch sensing technology uses these currents for
touch localization on papers [35, 37], woods [36], and inwater [38] and gesture recognition [34].
Material and setting

We used a 20 × 18 cm2 wiping paper (Kimtowel, Nippon Paper Crecia Co. Ltd., Japan) as the experimental

Fig. 1 Principle of HumTouch. HumTouch utilizes the current leaking
from a human body when conductive substances in the body reactwith the electromagnetic waves generated by AC power lines inbuildings

Hsia et al. ROBOMECH Journal(2022) 9:24

material. The paper was painted with a semiconductive
ink [34], which contained 15 g of polyvinyl alcohol, 300
mL of ultrapure water, 75 mL of polyethylene glycol 400,
and 37.5 mL of glutaraldehyde. After painting, the paper
was dried at room temperature for a week. After theseprocesses, the paper remained dry and flexible, as shownin Fig. 2.
Further, 7 × 7 points were marked on the paper atintervals of 2.5 cm and 2.25 cm. The ratio of these intervals follows the dimensions of the paper. An electrode
was attached to the center of each side of the paper (totalof four electrodes), following [37], as depicted in Fig. 2a.
These electrodes were then connected to an oscilloscope(HS6DIFF, TiePie Engineering, Netherlands; sampling
frequency: 500 kHz) to record the voltages on the surface.
Experimental procedure

Experiments were conducted in an office room. Six participants (male university students above 20 years), whoprovided written informed consent, were involved in theexperiments. Each participant was asked to touch each of
the 49 marked points on the paper with their right index
finger for approximately 1 s. This procedure was repeatedfive times to collect five sample sets from the individual participants. The contact force was not restrictedbecause the voltage rarely changed with the contact force[37]. In addition, to confirm that the participants were
well grounded, they sat still with their two feet firmly on
the ground. At least, one foot needed to be on the groundto record the voltages driven by hum noise. Further, they
removed their shoes and remained in socks becausesome shoes deterred the data collection.
Data analysis

Localization methods are described in Section 3 that
includes three methods based on kernel regression analysis. The first method, i.e., personalized model, establishes

Fig. 2 a Semiconductive paper marked with 49 points at intervals of
2.5 and 2.25 cm. An electrode is attached to the center of each side of
the paper (total of four electrodes). b Dry and flexible paper

Page 3 of 12

estimation models by using only the samples of a certain
person for localizing his/her touch. The second method,i.e., general model, establishes the model by using thesamples of several people for localizing the touch ofanother individual. The third method, i.e., semipersonalized model, uses a few samples from a certain participantand many samples from other participants to prepare amodel for that participant. Three methods are based on
the kernel regression analysis method, and comparison
with other potential methods is not considered.

Localization methods
Voltage‑data preprocessing

The hum-noise in our experimental setting was a 50 Hz
voltage signal. To reduce the influence of noise, weapplied a moving average filter of length 0.2 ms with
cut-off frequency of 2.2 kHz. After the noise-removal
process, we recorded the maximum value detected ineach electrode during 1 s as vi,j,e , where i ∈ {1, ..., 245},j ∈ {1, ..., 6}, and e ∈ {1, ..., 4} indicate the trial, participant, and electrode, respectively. The normalized maximum voltage zi,j,e is then computed as follows:

zi,j,e =vi,j,e − v̄i,j
,
σi,j

(1)

where v̄i,j and σi,j are the mean and standard deviation for
vi,j,1-vi,j,4.
Kernel regression model for fully‑personalized localization(Personalized model)

We applied kernel regression analysis, known as nonlinear multiple regression analysis, to find the relationshipbetween the touched locations and the recorded voltages
because the relationship between the two variables isnonlinear. As a personalized localization model [35, 37],a regression model was constructed using data recorded
from a single participant as the learning data. Note thatthe learning and test data were separated in a leave-oneout cross-validation manner, where four of the five sample sets were used to build the model and the remaining
set was used as the test set.
The location of each marked point was defined using a
two-dimensional Cartesian coordinate system. The estimated location (x̂, ŷ) for the vector of normalized voltagesz ∈ (z1 , ..., z4 )T is computed as follows:

 �n

2
ˆ
x(z)
i=1 αx,i exp(−�zi − z� )

=
,
(2)
�n
2)
ˆ
α
exp(−�z
−
z�
y(z)
y,i
i
i=1
where n is the number of samples (four trials × 49 points= 196), and z i = (zi,j,1 , ..., zi,j,4 )T are the normalized voltages for the learning samples of a single participant’s i-th

Hsia et al. ROBOMECH Journal(2022) 9:24

Page 4 of 12

trial, and . is the L2 norm. Coefficients αx,i and αy,i are
the i-th elements of coefficient vectors αx ∈ Rn×1 andαy ∈ Rn×1, respectively. The least squares solution of thecoefficients are computed as follows:

αx = (K + I)−1 x(3)αy = (K + I)−1 y,(4)where x = (x1 , ..., xn )T and y = (y1 , ..., yn )T are the actualx and y locations for the learning samples, respectively,
and matrix K ∈ Rn×n is given by
exp(−�z 1 − z 1 �2 ) . . . exp(−�z 1 − z n �2 )


..
..
..
K =
.
.
.
.

exp(−�z n − z 1 �2 ) . . . exp(−�z n − z n �2 )(5)With reference to our previous study [37], the regularization value  = 0.001. I is an n × n unit matrix.
Kernel regression model built using data from various
participants (General model)

As a general model for a certain participant, a regression
model was constructed using the data recorded from theother five participants. Hence, the general model is the
opposite of the personalized model, which only uses thedata of a single target participant. The general model wascomputed for each of the six participants. The estimatedlocation is computed as follows:  �m2x̂
s=1 αx,s exp(−�zs − z� )
,
 =
(6)
�m
2
ŷ
s=1 αy,s exp(−�zs − z� )where m is the number of samples (five participants × five
trials × 49 points = 1470) and z s = (zi,j,1 , ..., zi,j,4 )T are
the normalized voltages of learning sample s ∈ {1, ..., m}.
Coefficients αx,s and αy,s are the s-th elements of coefficient vectors αx and αy , respectively. These coefficients are computed using (3) and (4), with K ∈ Rm×m,
x ∈ Rm×1, and y ∈ Rm×1.
Weighted kernel regression model for semipersonalized
localization (Semipersonalized model)
Addition of extra samples from the target user

To build a semipersonalized model for a certain participant, we added one or more samples recorded by the participant to the learning data set collected from the otherparticipants. This expanded learning dataset for kernel
regression analysis includes 1470 + l samples, where l isthe number of extra samples from the target participant. l
ranges from one–five (l ∈ {1, ..., 5}).

We investigated the effect of the number and locationof the extra l samples. Figure 3 shows their locations.
To add one sample from the target participant (l = 1),
we tested the nine locations indicated in Fig. 3a (points
near the corner (point 11), center (point 44), and sevenother points). To add two samples (l = 2), we tested thethree pairs of points indicated in Fig. 3b ((point 11, 44),(point 14, point 44), and (point 11, point 14)). For adding
three samples (l = 3), combinations of (points 11, 17, 44)
and (points 14, 41, 44) were tested, as shown in Fig. 3c.
For adding four samples (l = 4 ), combinations of (points
11, 17, 44, 71) and (points 14, 41, 44, 74) were tested, as
shown in Fig. 3d. To add five samples (l = 5), combinations of (points 11, 17, 44, 71, 77) and (points 14, 41, 44,
47, 74) were tested, as shown in Fig. 3e.
Weighting of the extra samples from the target user

We expected that including extra samples from the target
participant reduces the localization error. Weighted kernel regression analysis was performed by adjusting coefficients αx and αy as follows:

αx = (K T W K + I)−1 K T Wx

(7)αy = (K T W K + I)−1 K T Wy.

(8)

Matrix W ∈ R(m+l)×(m+l) is given by


I 0
,
W =
0 U

(9)

n×n
where
I
is
an
unit
matrixandU = diag(w, w, ...) ∈ Rl×l is the weight matrix, where w
is the weight value. Weight value w = 1, 10, 100, or 1000.

Results
Personalized model: touch localization with the model
constructed using samples from one participant

We established localization models for the individuals.
For each participant, four sample sets were used as thetraining dataset and one was used as the test set, applying leave-one-out cross-validation. Figure 4 shows themean and standard deviation of the localization for eachparticipant. Further, we computed the arithmetic meanof the absolute errors for each marked point and participant. The mean estimation error among the six participants was 0.60 cm. Participant P4 exhibits the least
mean localization error of 0.44 cm, whereas P6 exhibits the highest mean error of 0.83 cm. These estimation
errors are generally considered small because they arelesser than the mean width of index fingertips of Japanese males: 13.9 cm [39]. Note that the error values of the
fully personalized models can be used as the benchmark
scores for evaluating the other methods in this study.

Hsia et al. ROBOMECH Journal(2022) 9:24

Page 5 of 12

(b)

(a)
Point 11 Point 12 Point 13

Point 14

Point
22
Point
33

Point 24

Point 11

Point 14

Point 34
Point 44

Point 44(c)(d)
Point 11

Point 14

Point 41

Point 17

Point 44

Point 11

Point 14

Point 41
Point 44
Point 71

Point 74(e)
Point 11

Point 14yPoint 17

Point 41

Point 47
Point 44

Point 71

Point 77
Point 74

Point 17

O

x

Fig. 3 a–e Location of the extra samples when a one, b two, c three, d four, and e five extra samples are added from the target participant to thelearning data-set for building a semipersonalized model

The estimation errors of the semipersonalized model are
expected to be close to these errors.
General model: localization with the model constructedusing samples from other participants

Figure 5 shows the results of the regression models constructed using the data of participants other than thetested participant. The mean estimation error among thesix participants is 1.24 cm. For all the participants, theestimation errors are greater than those of the personalized model. Participant P4 exhibits a mean error below
1 cm; however, the mean errors for the other participants
are equal to or greater than 1 cm. The errors of the semipersonalized model are expected to be lesser than thoseof the nonpersonalized general model.
The aforementioned results suggest the extent of difference among individuals. P5 and P6 may be distinctivefrom the others in terms of the mean estimation errors.
Their mean estimation errors were 1.69 cm and 1.58 cm,
respectively, whereas those for the other participants
were less than 1.30 cm. Further, as shown in Fig. 5, P6
exhibited large standard deviations. For this participant,reproducibility was relatively low whereas the standard deviations were minor for the other participants.
The root cause of such variability within participants is
unclear. The number of the participants is small and wecannot estimate the properties of population.
Semipersonalized model: localization with the model
constructed using a few target‑user samples
and the sample sets of other participants

Table 1 lists the mean localization errors for all theparticipants.

Hsia et al. ROBOMECH Journal(2022) 9:24

Page 6 of 12

(a)

P1

P2

P3

P4

P5

P6

Fig. 4 Results of the personalized model constructed using samples from an individual target participant: a Actual locations of the 49 points. (P1–P6) Mean estimated location and standard deviation of each of the six participants, respectively

When single extra samples from the target participants are used for calibration, the total mean error ranges
from 0.91–0.90 cm, irrespective of the location of theextra sample. When point 44 is used, the mean errors
for P5 and P6 are 1.33 and 1.25 cm, respectively, which
are lesser than the errors when the other points are used.
Hence, point 44, which is near the center, exhibits thebest localization accuracy among all the tested points,
although the differences in the accuracy are negligible.
As shown in Table 1, when the extra sample (i.e., point
44) is weighted with w = 10, 100, and 1000, the meanerrors are 0.90 cm for all w values. Figure 6 displays themean and standard deviation of the localization whenw = 1000. The mean localization errors for the individual

Hsia et al. ROBOMECH Journal(2022) 9:24

Page 7 of 12

(a)

P1

P2

P3

P4

P5

P6

Fig. 5 Results of the general model constructed using samples from participants other than the target participant: a Actual locations of the 49
points. (P1–P6) Mean estimated location and standard deviation of each of the six participants, respectively

participants are lesser than those of the general model, as
depicted in Fig. 5.
When two extra samples from the tested target participants are used for calibration, the total mean localization
error is approximately 0.90 cm, irrespective of the combination of points. When three, four, or five extra samples
are used, the total mean errors range 0.89–0.90 cm.
Figure 7 depicts the localization errors among thegeneral, several semipersonalized, and personalized

models. The mean error of the semipersonalized
model using point 44 with no weight value ( w = 1)
is lesser than that of the general model (two-tailed
t-test with one sample, t = 15.77 , p = 1.86 × 10−5
with no p-value adjustment for multiple comparisons). The mean error of the semipersonalized modelusing point 44 weighted by 1000 is lesser than that of
the general model (two-tailed t-test with one sample,t = 15.50 , p = 2.03 × 10−5 with no p-value adjustment

Hsia et al. ROBOMECH Journal(2022) 9:24

Page 8 of 12

(a)

P1

P2

P3

P4

P5

P6

Fig. 6 Results of the semipersonalized model using point 44 with w = 1000. a Actual locations of the 49 marked points. (P1–P6) Mean estimatedlocation and standard deviation of each of the six participants, respectively

for multiple comparisons). There are significant differences between the other semipersonalized models using more than one extra point and the generalmodel, as shown in Fig. 7.

Discussion
As listed in Table 1, the localization error of the personalized model is 0.60 cm on an average and substantiallylesser than that of the general (nonpersonalized) modelHsia et al. ROBOMECH Journal(2022) 9:24

Page 9 of 12(1.24 cm). For the individual participants, the errors of
the general model are nearly equal to or more than twicethat of the personalized model. The average errors of thesemipersonalized models range from 0.89–0.92 cm, indicating that the localization accuracy of the semipersonalized model developed in this study is between those of
the personalized and general models. As the size of thecontact-area of the fingertip is approximately a 1.0 cmdiameter circle, localization errors of 0.90 cm may be
acceptable for most touch-sensor applications, although
our study does not assume specific applications.
When the HumTouch technique is applied to square
paper, the points near the corner and center involve relatively large localization errors. This is because the localization accuracy is relatively poor at points far from theelectrodes [37]. The points near the corner and centerof the paper are far from all the electrodes placed at thecenter of each side of the paper. The semipersonalized
model can reduce the localization errors near an area by
incorporating an extra sample of the target participant
near the area. Thus, the calibration ability with different points has been investigated in this study. As listed
in Table 1, when single extra points are used for thesemipersonalized model, the estimation error obtainedby adding a point in the center (i.e., point 44) has theleast total mean error of 0.90 cm. In addition, by weighting the sample for point 44, the mean estimation error
is further reduced, although this reduction is practicallynull. However, adding the extra sample is effective only
for the surrounding area. The localization errors forthe points in the other areas remain large. For example,
as illustrated in Fig. 6, for the points near point 44 (thecenter point in each subfigure), the localization errors
are corrected compared to those with the general model;
however, the points near point 77 (right-down in eachsub-figure) include localization error. Nonetheless, thisstudy establishes that by including only a single sample,
the nonpersonalized general model can be converted intoa semipersonalized model with small localization errors.
Figure 7 shows that the localization errors of the nonpersonalized general model are reduced by 27.8% byadding only one sample of the target participant. However, the addition of more than one sample is not markedly effective in reducing the error. For example, adding
five extra samples improves the localization accuracy bymerely 0.007 cm, compared to the case where only one

Table 1 Mean localization errors (cm) of the personalized, general, and semipersonalized models for participants P1 to P6. For thesemipersonalized model, weight w = 1unless otherwise specified
Model type

Extra points used for semipersonalized model

P1

P2

P3

P4

P5

P6

Mean error

General model

–

1.28

1.00

1.02

0.89

1.69

1.58

1.24

Personalized model

–

0.47

0.51

0.59

0.44

0.74

0.83

0.60

Semipersonalized model

Point 11

0.85

0.72

0.75

0.51

1.39

1.25

0.91

Point 12

0.85

0.72

0.75

0.51

1.40

1.26

0.92

Point 13

0.85

0.72

0.75

0.51

1.40

1.25

0.91

Point 14

0.85

0.72

0.75

0.51

1.40

1.25

0.91

Point 22

0.85

0.72

0.75

0.51

1.40

1.25

0.91

Point 24

0.85

0.72

0.75

0.51

1.40

1.26

0.92

Point 33

0.85

0.72

0.75

0.51

1.40

1.27

0.92

Point 34

0.85

0.72

0.75

0.51

1.40

1.26

0.92

Point 44

0.85

0.72

0.75

0.51

1.33

1.25

0.90

Point 44,w=10

0.84

0.72

0.75

0.50

1.33

1.25

0.90

Point 44,w=100

0.84

0.72

0.74

0.50

1.33

1.25

0.90

Point 44,w=1000

0.84

0.72

0.74

0.50

1.33

1.25

0.90

Point 11,44

0.85

0.72

0.75

0.51

1.32

1.25

0.90

Point 14,44

0.85

0.72

0.75

0.51

1.33

1.25

0.90

Point 11,14

0.85

0.72

0.75

0.51

1.39

1.25

0.91

Point 11,17,44

0.85

0.72

0.75

0.51

1.31

1.24

0.90

Point 14,41,44

0.85

0.72

0.75

0.51

1.33

1.25

0.90

Point 11,17,44,71

0.85

0.72

0.75

0.51

1.29

1.24

0.89

Point 14,41,47,44

0.85

0.72

0.75

0.51

1.33

1.24

0.90

Point 11,17,44,71,77

0.85

0.72

0.75

0.51

1.28

1.23

0.89

Point 14,41,44,47,74

0.85

0.72

0.75

0.51

1.33

1.22

0.90

Hsia et al. ROBOMECH Journal(2022) 9:24

Page 10 of 12

Fig. 7 Comparison of the total mean errors. The general model obviously exhibits greater error than the semipersonalized one. The personalized
model with smaller error than those of the general and semipersonalized models is also shown for reference. *** indicates p < 0.001as per the
two-tailed t-test with one sample with no p-value adjustment for multiple comparisons

extra sample is used. Furthermore, weighting the extra
samples is ineffective in reducing the error. As aforementioned, adding extra samples reduces the localization
errors in the area nearby those points; however, it does
not greatly influence other area. Hence, weighting extra
samples may not effectively reduce the mean localization
errors. Collectively, based on the results, a new unknown
user can complete his/her calibration to improve thelocalization accuracy by simply touching one designatedpoint on the material surface.
An interesting question is how only one extra sample could improve the estimation accuracy. The numberof samples used to establish generalized models is an
important factor. The extra samples can be more effective
when the sample size for the generalized model is small.
In such a case, the generalized model does not perform
well and can be substantially improved by incorporating
extra samples. In contrast, the effects of extra samples
may be relatively minor when the generalized model isestablished on the basis of a large sample. Hence, there
should be an appropriate sample size for establishing
the generalized model, which minimizes the estimation
errors of semipersonalized model.
This study has certain limitations. Only six participants
were involved in the experiment. It is unclear whether

semipersonalized models should be based on a large
number of participants. The number of participants andsamples may influence the additional number of samples
necessary for constructing semipersonalized models.
Potentially, the more the participants and samples, themore are the extra samples necessary to establish semipersonalized models because the effect of extra samplesof the target user are obscured by the large number ofsamples from others. In contrast, semipersonalized models based on a few people may not be highly adaptive
to unknown users. Hence, the number of people incorporated in the semipersonalized model is speculated to
be a crucial design parameter. This study did not aim at
optimizing the localization method. The methods basedon kernel regression analysis were not compared withothers. Kernel regression analysis is a suitable method
for the problems discussed here; however, more effective
methods must be explored. Furthermore, in hum-noisebased sensors, the causes for individual differences areunclear and have not been thoroughly discussed. If theindividual differences are determined by certain parameters, such as the body weight, then another approachcompletely different from the one adopted in this study
can be employed to build semipersonalized models.
In addition, it is to be noted that localization errors areHsia et al. ROBOMECH Journal(2022) 9:24

generally greater for larger surfaces [37]. If the method
proposed in this study is applied to a larger surface, thelocalization errors may not be as small as the fingertip
size.

Conclusion
We proposed an easy-to-use calibration method for
unknown users of HumTouch, a hum-noise-based touchsensing method. Because of the individual differences inhum-noise-driven currents in human bodies, HumTouch
sensors need to be calibrated for individual users, limiting
their range of application. The proposed method incorporates numerous samples collected from other people
and a few weighted new samples from a new target user
to establish a semipersonalized model for the new user.
When a square paper was used as a touch-sensitive surface, the mean localization error was reduced from 1.24
to 0.90 cm by adding a new point from the target user tothe sample set obtained from other people. This localization error is smaller than the size of a human fingertip.
Therefore, the semipersonalized localization model using
a weighted or nonweighted sample from the target user ispractical for hum-noise-based touch sensors.
Acknowledgements
We thank all the participants of the experiment.
Author contributions
Design, experiment, data curation, analysis: T-HH and SO. Funding: SO. Theory,
writing: T-HH, SO, YA, YY. All authors read and approved the final manuscript.
Funding
This study was supported in part by MEXT Kakenhi Grant nos. #21H05819 and#20H04263.
Availability of data and materials
The data used for analysis can be accessed by contacting the corresponding
author.

Declarations
Ethics approval and consent to participate
The experimental procedure was approved by the institutional review boardof the School of Engineering, Nagoya University (#20-8).
Competing interests
The authors declare no competing interests.
Author details
1
Department of Mechanical Systems Engineering, Nagoya University, Nagoya,Japan. 2 Department of Computer Science, Tokyo Metropolitan University,
Hino, Japan. 3 Department of Mechanics and Robotics, Shinshu University,
Ueda, Japan. 4 National Institute of Technology, Toyota College, Toyoda, Japan.
Received: 7 March 2022 Accepted: 27 November 2022

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