2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)

Localization performance of hum-noise-based touchsensor (HumTouch) with unknown participantsTzu-Hsuan Hsia 1 , Shogo Okamoto2 , Yasuhiro Akiyama1 , and Yoji Yamada1
1. Nagoya University, Nagoya, Japan. 2. Tokyo Metropolitan University, Hino, Japan

Abstract—HumTouch is a touch sensing method that can turnobject surfaces into touch sensors. The sensing method utilizes
the current leak from the human body; therefore, the localization
performance exhibits individual differences. In this paper, weresearch the localization performance of HumTouch involving
multiple participants. A semi-conductive paper marked with 49
points was used for the experiment. Five participants participatedin the experiments and the localization results were evaluated via
leave-one-out cross validation, in which an estimation model wasestablished by using the samples collected from four participants
to localize the touch of the last participant. For four out of thefive participants, there were mean estimation errors smaller thanor equal to 1 cm, which is comparable to the size of a fingertip.
This indicates that HumTouch has the potential to be applied tomultiple participants without prior calibration conducted for aspecific person.
Index Terms—human antenna, human touch sensing, flexible
touch sensor

I. I NTRODUCTION
Nowadays, touch sensors are widely used in consumer
electronics. The usage of touch sensors allows people to operate electronic devices in a more intuitive manner. Althoughsome types of touch sensors are commercially available, HumTouch [1]–[5] provides a different approach by converting dayto-day objects into touch sensors. The human body contains
conductive elements that react to electromagnetic waves, suchas in the case of AC hum [6]. When a human touches a
conductive object, the current leaks from the finger to thesurface of the object. By attaching electrodes to the object
surface, the voltage of the current can be detected and used
for localization or gesture recognition. Therefore, surface
activation is not needed in HumTouch.
In our previous studies, we proposed a few different typesof localization methods involving the use of kernel regression
analysis [3]–[5]. In these studies, we constructed a regression
model using different datasets, and each dataset comprised
data recorded from a single participant. Owing to the differences in the electric properties of one human body and those ofanother, the leaked currents can also very from one individual
to the other; this makes it difficult to realize an accurate modelfor the localization of touch for multiple unknown participants.
In this study, we investigated the localization performance of
HumTouch by applying it to five participants. The localization
method we used is based on the one proposed in [5], in which
a nonlinear kernel regression analysis is performed using thevoltages detected at the electrodes on the object surface asThis study was in part supported by MEXT Kakenhi #20H04263.

978-1-6654-3675-5/21/$31.00 ©2021 IEEE

Fig. 1. (a) Semi-conductive paper marked with 49 points Their intervals were
2.5 and 2.25 cm. Four electrodes were attached to the center of each edge.
(b) The paper is dry and flexible.

the explanatory values. To evaluate the localization accuracy,
we applied leave-one-out cross-validation, where a dataset
constructed using the data of one person was tested by themodel constructed based on the datasets constructed using thedata collected from different persons. The results suggest thepotential of touch localization for unknown users, for which
the HumTouch sensor is not calibrated.
II. M ATERIAL , APPARATUS AND E XPERIMENT
We used a 20 × 18 cm2 wiping paper (Kimtowel, Nippon
Paper Crecia, Japan) painted with a semi-conductive ink [2].
The ink contains polyvinyl alcohol, polyethylene glycol andglutaraldehyde. The paper was dry and flexible after it waspainted with the ink. Forty-nine grid points were marked
on the paper at 2.5 cm and 2.25 cm intervals, as shownin Fig. 1. Four electrodes were located at each edge of thepaper and connected to an oscilloscope (HS6 DIFF, TiePie
Engineering, Netherlands; sampling frequency: 500kHz) torecord the signals.
Five participants were asked to touch each of the 49 marked
points with their index finger for approximately 1 s inside anormal office room. This procedure was repeated five times tocollect five sample sets for every participant.
III. L OCALIZATION M ETHOD
A. Data Preprocessing
To remove the high-frequency noise in the signals recordedby the electrodes, we applied a moving average filter with awindow size of 0.01. After the smoothing process, the maximum voltages at the electrode were recorded as vij = {vij,e },where i, j, and e specify a trial, participant (j = 1, ..., 5), and815

2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)

B. Kernel Regression Analysis
Kernel regression analysis was applied to estimate thetouched location because the relationship between the touchedlocations and the recorded voltages was nonlinear. We used theCartesian coordinate system to define the location for eachmarked point. The estimated location (x̃, ỹ) was computed
using the observed values z = (zij,e ) as follows:  Pmx̃
s=1 αx,s k(zs , z )
 =

(2)
Pmỹ
αk(z,z)y,ss
s=1
where m is the number of samples (four participants × five
trials × 49 points = 980) and k(zs , z ) is a Gaussian kernel:k(zs , z ) = exp(−|zs − z |2 ),

(3)

(4)
(5)

where x = (x1 , ..., xm )T and y = (y1 , ..., ym )T are the actualx and y locations for the learning samples, respectively. Matrix
K ∈ Rm×m is formed by the Gaussian kernel:k(z1 , z1 ) . . . k(z1 , zm )

..
..
K=
(6)
.
.
.
k(zm , z1 )k(zm , zm )The regularization values λ and I were 0.001 and m × m,respectively. To estimate the locations of the jth participant,the learning samples were obtained from the other four participants.
IV. R ESULTS
The points presented in Fig. 2 show the mean valuesof the estimated locations and the standard deviations. Thepoints near the center of the paper appeared to have smaller
estimation errors and the smallest value was 0.15. On theother hand, expect for P5, the points near edges have large
estimation errors and the largest value was 4.4 cm. For four
(P1–P4) out of the five participants, the mean errors wereequal to or smaller than 1 cm. These errors are comparable
to those presented in our previous study, which involved only
one participant, and the mean error was 0.48 ± 0.10 cm ona 20 × 16 cm2 paper [5]. For one participant (P5), the meanlocalization error was 2.68 cm, and the other errors, overall,
were significant.

y location (cm)10−10P3

P2
10−10P5P4
10−−−10
10−10−10x location (cm)

10x location (cm)Fig. 2. (a) Actual locations for the 49 marked points. P1–P5 specify thefive participants. The dots represent the mean estimated locations for the fivetested trials with the corresponding standard deviations.

which |.| is the L2 norm. The coefficients αxs and αys are thesth elements of αx and αy , respectively. They are computed
as follows:
αx = (K + λI)−1 x
αy = (K + λI)−1 y ,P1y location (cm)where zij,e is the normalized maximum voltage for eachelectrode, and µj and σj are the mean value and standard
deviation of the recorded maximum voltages for the jthparticipant, respectively.

(a)y location (cm)electrode (e = 1, ..., 4), respectively. The recorded voltage canthen be normalized byvij,e − µjzij,e =,(1)σjV. C ONCLUSIONS
We investigated the localization performance of the HumTouch sensor for unknown participants. The localization foreach participant was realized via a regression model constructed using the data recorded the other participants. Over
all, the estimation errors for the points near the center of paper
were small but large for the points near the edges. For four outof the five participants, the model showed mean errors closeto or smaller than 1 cm, whereas that of one participant wassignificant. The results showed that the HumTouch sensor canbe potentially used for unknown users whose data are not used
for training the localization model of the sensor.
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2019, pp. 157–159.
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[4] ——, “HumTouch: Localization of touch on a cylindrical object,” inProceedings of IEEE Global Conference on Consumer Electronics, 2020,
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[5] ——, “Humtouch: Localization of touch on semi-conductive surfaces bysensing human body antenna signal,” Sensors, vol. 21, no. 3, p. 859, 2021.
[6] G. Cohn, D. Morris, S. N. Patel, and D. S. Tan, “Your noise is mycommand: Sensing gestures using the body as an antenna,” in Proceedingsof the SIGCHI Conference on Human Factors in Computing Systems,
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