2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)

HumTouch:
Localization of Touch on a Cylindrical Object
Tzu-Hsuan Hsia, Shogo Okamoto, Yasuhiro Akiyama, and Yoji Yamada
Department of Mechanical Systems Engineering, Nagoya University, Nagoya, Japan

Abstract—HumTouch is a passive touch sensing method thatutilizes the environmental electromagnetic waves. Unlike thecommonly used touch sensing technology (e.g. capacitive sensing),
the method we propose does not need surface activation. In thispaper, we applied HumTouch to a semi-conductive wood cylinder(diameter = 3 cm, length = 30 cm) to localize the human touchon the surface. This was achieved via the model built by thekernel regression analysis and the performance was evaluatedthrough the leave-one-out cross validation. The mean estimation
error was 0.22 cm which is smaller than a ﬁngertip. This provesthat HumTouch is functional not only on a ﬂat surface but also
on a cylindrical surface.
Index Terms—Hum noise, touch sensor, localization

I. I NTRODUCTION
Touch sensors have been widely employed in commercial
devices (e.g. smartphones, laptop computers, etc.). Unlike thetraditional keyboard or mouse, they provide a more directmeans to operate a computer interface. Although touch sensors have been already commercialized, HumTouch [1]–[5]provides another approach for sensing the human touch. It
is a touch method capable of sensing touch on daily objects
including furniture and walls. A human body in a building reacts to the electromagnetic waves that cause 50/60 Hz electric
signals in his body [6]. By attaching electrodes on a conductive
or semi-conductive object surface, the voltage of these signals
can be detected when the surface is touched by a human.
Therefore, HumTouch does not need any surface activationand can be effortlessly applied by installing electrodes on asurface.
In our previous study, we conducted localization of touchon a semi-conductive paper [5]. This method is conductedby attaching four electrodes to each corner of the paper.
Each electrode is represented as an orthant of the Cartesian
coordinate system so that the touch location can be computed
as a linear combination of the voltages recorded by the fourelectrodes. However, the locations thus measured were foundto be distorted; this issue was resolved by implementing thekernel regression analysis. The mean error for the estimatedlocation was 0.88 cm on a 19 × 16 cm2 paper. On the other
hand, unlike papers, the surface of a three-dimensional object
is continuous. Therefore, the allocation of electrodes andsubsequent calculations adopted in [5] could not be appliedfor 3D objects.
In this study, we attached six electrodes to a wood cylinder.
Unlike the two-phased localization in [5], the method used
This study was in part supported by the Mazda foundation.

Fig. 1. A semi-conductive wood cylinder of 3 cm in diameter and 30 cm inlength. Five points with 5 cm interval were marked at φ = 0◦ , 90◦ , 180◦
and 270◦ on the surface. 20 points were marked in total.

Fig. 2. Three electrodes (screws) were nailed at φ = 0◦ , 120◦ and 240◦ oneach side of the cylinder.

in the present study did not rely on the preprocessing oflocation of touch; instead, it directly computed the locationby using the differential outputs obtained from the electrodepairs and applying the kernel regression method. This method
is expected to serve as a general localization method for threedimensional objects.
II. M ATERIALS AND APPARATUSES
We painted a wood bar (diameter = 3 cm, length = 30 cm)
with a semi-conductive ink, which contains polyvinyl alcohol,polyethylene glycol and glutaraldehyde [4]. Three screws (5.0mm × 10 mm) were attached on either of the ends, i.e., total
six screws were used as electrodes. The interval between eachscrew was 120◦ as shown in Fig. 2. An oscilloscope withsix channels (HS6 DIFF, TiePie Engineering, Netherlands;

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2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)

sampling frequency: 500 kHz) was used to record the signals
obtained from the electrodes.
III. E XPERIMENTAL P ROCEDURE
Five points with 5 cm intervals were marked on every 90◦of the cylinder as shown in Fig. 1. Furthermore, these twenty
marked points were touched one at a time by a participant
with his index ﬁnger for approximately 1 s in a normal ofﬁceroom. The contact force of the touch was not restricted andthe procedure was repeated seven times to collect seven datasets. In this way, a total of 20 × 7 samples were recorded.
IV. DATA P ROCESSING
We applied a moving average ﬁlter with a span of 5 tothe collected signals and the resultant maximum voltage ineach channel was recorded. For a sample i, the recorded
maximum voltage of the jth channel is denoted by vij wherei = 1, ..., 140 and j = 1, ..., 6. Typical vij values ranged 0.2–0.7 V.
To prepare for the input vector for kernel regression, therecorded voltage data was further processed. For the maximum
voltages recorded at the ath and bth channel of sample i, di,abwas computed as:
di,ab =via − vib, (a, b = 1, 2, ..., 6; a ̸= b).
via + vib

(1)

We applied the kernel regression analysis to ﬁnd the relationship between the actual locations of the marked points andthe input vector. The relationship was used to build a modelfor the localization.
The actual location of the marked points is expressed as(φ, z) where φ = (φ1 , φ2 , ..., φn )T is the angular coordinate and z = (z1 , z2 , ..., zn )T is the z coordinate of thelocation. The input vector for the ith sample is xi , wherexi = (di,ab ; a, b = 1, ..., 6, a ̸= b)T ∈ R15×1 . In this study, weapplied six data sets (n = 120 samples) to build the regression
model and the estimated location (φ̃, z̃) for x was computedas:φ̃ =z̃ =n∑

αφi k(xi , x ),

i=1n
∑

(2)

αφi k(xi , x ).

The Matrix K that comprises of the kernel functions isexpressed as:k(x1 , x1 ) . . . k(x1 , xn )

..
..
K=
(7)
.
.
.
k(xn , x1 ) . . .

The kernel function k(xi , x) used in this study was theGaussian kernel, which is expressed as follows:
2k(xi , x) = exp(−|xi − x| ),(4)

where, |.| is the L2 norm. The coefﬁcients αφi and αzi are theith components of vectors αφ and αz , which can be given as
follows:(5)(6)

k(xn , xn )The regularization term λ was the value that carried out thesmallest estimation error. In this study, λ was set to 4.3×10−5
and I was a n × n identity matrix.
V. R ESULTS
The leave-one-out cross validation was applied to investigate
the generalization of the localization method. The points inFig. 3 are the mean estimated locations obtained from theseven trials for the twenty marked points and their error bars
indicate their corresponding standard deviations. The estimation error for the location was 0.21 cm in the z direction and26.4◦ (0.23 cm in circumference distance) in the φ direction.
These errors are comparable to those of other methods. For
example, a method based on electric tomography involves
mean localization errors of less than 10 mm [7].

(3)

i=1

αφ = (K + λI)−1 φ
αz = (K + λI)−1 z.

Fig. 3. Mean estimated coordinates of the twenty marked points after theapplication of the leave-one-out cross validation. The error bars indicate thestandard deviations for each point and the black crosses are the actual locationsof the marked points.

VI. C ONCLUSIONSWe proposed a hum-noise based touch sensor (named HumTouch) that can be applied on cylindrical objects. In this study,
twenty points were localized via the kernel regression methodwith a mean estimated error of 0.22 cm. The result shows thatHumTouch can be implemented not only on 2D surfaces but
also on 3D objects.
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