Journal of Robotics, Networking and Artificial Life, Vol. 2, No. 1 (June 2015), 60-63

An Authentication Method
Independent of Tap Operation on the Touchscreen of a Mobile Device
1Hisaaki Yamaba, 1So Nagatomo, 2Kentaro Aburada, 1Shinichiro Kubota,
1Tetsuro Katayama, 3Mirang Park, 1Naonobu Okazaki

1University of Miyazaki, 1-1, Gakuen-kibanadai nishi, Miyazaki, 889-2192, Japan
2

Oita National College of Technology, 1666, Maki, Oita, 870-015, Japan

3Kanagawa Institute of Technology, 1030, Shimo-ogino, Atsugi, Kanagawa, 243-0292, Japanyamaba@cs.miyazaki-u.ac.jp, hf11036@student.cs.miyazaki-u.ac.jp,
aburada@oita-ct.ac.jp, kubota@cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp,
mirang@nw.kanagawa-it.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily
lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user
authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching.
The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected
over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a series ofexperiments was carried out to investigate the prospect of an authentication method using s-EMGs. Specifically,several gestures of the wrist were introduced, and the s-EMGs generated for each motion pattern were measured.
We compared the s-EMG patterns generated by each subject with the patterns generated by other subjects. As aresult, it was found that each subject has similar patterns that are different from those of other subjects. Thus, sEMGs can be used to confirm one’s identification for authenticating passwords on touchscreen devices.
Keywords: mobile device, user authentication, shoulder surfing, electromyogram.

1. Introduction
This paper proposes a new user authentication method
for mobile devices by using surface electromyogram (sEMG) signals, not screen touching.
At the present time, mobile devices such as tablet
type PCs and smart phones have widely penetrated into
our daily lives. Therefore, an authentication method thatprevents shoulder surfing is needed. Shoulder surfing isthe direct observation of a user’s personal information,such as passwords. Authentication operations on mobiledevices are performed in many public places, so wehave to ensure that no one can view our passwords. But
it is not an easy task. Many mobile devices have nokeyboards, so the authentication method must use atouchscreen. When using a touchscreen, the owner ofthe mobile device inputs his or her authentication
information through simple or multi-touch gestures.
These gestures include, for example, designating his/herpasscode from displayed numbers, selecting registeredpictures or icons from a set, or tracing a registered one-Published by Atlantis Press
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stroke sketch on the screen. People positioned close to
the mobile device owner can easily grasp these actions
and obtain the user’s authentication information.
The s-EMG signals, which are generated by theelectrical activity of muscle fibers during contraction,
are detected over the skin surface. These s-EMGs havebeen used to control various devices, including artificial
limbs and electrical wheelchairs. Muscle movement canbe differentiated by analyzing the s-EMG1. For
example, fast Fourier transform (FFT) can be adopted
for the analysis. Feature extraction is carried out
through the analysis of the s-EMGs. The extracted
features are used to differentiate the muscle movement,including hand gestures.
In this paper, a series of experiments was carriedout to investigate the prospect of realizing anauthentication method using s-EMGs. Specifically,
several gestures of the wrist were introduced, and the sEMG signals generated for each motion pattern weremeasured. We compared the s-EMG signal patterns
generated by each subject with the patterns generated byother subjects. As a result, it was found that the patternsof each individual subject are similar but they differ
from those of other subjects. From these results, it isexpected that s-EMGs is used to confirm one’sidentification for authenticating passwords ontouchscreen devices.
2. Characteristics of authentication method formobile devices
User authentication of mobile devices has twocharacteristics.
One is that an authentication often takes placearound strangers. An authentication operation isperformed whenever a user wants to start using theirmobile devices. Therefore, the strangers around the user
can possibly see the user’s unlock actions. Some ofthese strangers may scheme to steal information such as
passwords for authentication.
The other characteristic is that much user
authentication of mobile devices is now performed on atouchscreen. Many current mobile devices do not have
hardware keyboards, and so it is not easy to input long
strings into such mobile devices. When users unlock
mobile touchscreen devices, they input passwords or

personal identification numbers (PINs) by tapping
numbers or characters displayed on the touchscreen. In
many cases, the user moves only one finger. Since users
have to look at their touchscreens while unlocking theirdevices, strangers around them can easily see the unlock
actions, and so it becomes very easy for such strangers
to steal passwords or PINs.
To prevent shoulder-surfing attacks, many studies
have been conducted. The secret tap method introducesa shift value to avoid revealing pass-icons2. The usermay tap other icons in the shift position on thetouchscreen, as indicated by a shift value, to unlock thedevice. By keeping the shift value secret, people aroundthe user cannot know the pass-icons, although they canstill watch the tapping operation. The rhythm
authentication method relieves the user from looking at
the touchscreen when unlocking the device3. In thismethod, the user taps the rhythm of his or her favorite
music on the touchscreen. The pattern of tapping is usedas the password. In this situation, the users can unlock
their devices while keeping them in their pockets orbags, and the people around them cannot see the tap
operations that contain the authentication information.
3. Surface electromyogram signals
The s-EMG signals are detected over the skin surface
and are generated by the electrical activity of muscle
fibers during contraction. Muscle movement can bedifferentiated by analyzing the s-EMG. Usually, FFT isadopted for the analysis, and feature extraction iscarried out through analysis of the s-EMG.
However, since measured s-EMG signals vary bysubject, the extracted features do not show enoughperformance to correctly differentiate the muscle
movement in multiple subjects. Therefore, researchers
have explored other methods to improve theperformance of feature extraction. Since some methods
demonstrate good performance for some subjects but
other methods show better performance for othersubjects, a feature that can be used to distinguishgestures for everyone is desired. For example, a methodthat uses the maximum value and the minimum value ofraw s-EMG signals was proposed4.

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Figure 2 Gestures used in the experiments
Figure 1 Experimental System

4.

5. Experiments

User authentication using s-EMG

In this section, the method of user authentication byusing s-EMGs, which do not require looking at atouchscreen, is presented.
The s-EMG signals are measured, and the featuresof the measured raw signals are extracted. We estimate
gestures of a user of a mobile device from the extracted
features. Next, combinations of the gestures areconverted into a code for authentication. These
combinations are inputted into the mobile device andused as a password for user authentication.
Adopting s-EMGs for authentication of mobiledevices has two advantages. First, the user does nothave to look at his/her device. Since the user can make agesture that is used as a password on a device inside apocket or in a bag, it is expected that the authentication
information can be concealed. No one can see what
gesture is made. In addition, it is expected that if
another person reproduces a sequence of gestures that auser has made, the authentication will not be successful,
because the extracted features from the s-EMG signals
are usually not the same between two people.

A series of experiments was carried out to investigate
the prospect of the authentication method using sEMGs. Specifically, we investigated whether themeasured s-EMG signals of one experimental subject
were similar and whether the signals of different
subjects were different from each other.
Figure 1 shows the experimental system used inthese experiments. An electromyograph measured the sEMG of each movement pattern with two electrodesensors. The measured data were stored and analyzed ona PC. Ten healthy persons whose ages were in thetwenties (students of University of Miyazaki)
participated as experimental subjects (Subjects A–J).
The six hand gestures (1–6) shown in Figure 2 wereintroduced in the experiments. First, each subject madeeach gesture ten times in succession and their s-EMG
signals were recorded (Exp. #1). Approximately one
week later, the same experiment was carried out again(Exp. #2).
Some of the results are shown in Figure 3. The
raw s-EMG signals of gesture 1 made by experimental
subjects A and B are displayed. The signals of Exp. #1
and #2 are similar for each subject. On the other hand,the signals of subjects A and B are not similar to those

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Figure 3 Obtained s-EMG signalsof the other subject. The same results were obtained forevery pair of subjects and all gestures.
These results show that the s-EMG is promising
as identification input for a new user authentication
method.
6. Conclusion
We investigated a new user authentication method thatcan prevent shoulder-surfing attacks in mobile devices.
We know that the unlocking operations of mobiledevices such as tablet-type PCs or smart phones canoften be viewed by other people. Users can hide suchoperations from others by not looking at the touchscreenof a mobile device while unlocking it. To realize suchan authentication method, we assigned a set of gestures
to obtain the s-EMG signals. A series of experiments
was carried out to confirm that the s-EMG signals ofeach person are similar for that person and the signals ofdifferent people are not similar to other people’s signals.
These results showed that s-EMG signals can be used as

input for a new user authentication method. We willconduct a quantitative evaluation of this approach infuture work.
Acknowledgements
Useful advice and the experiment system were offeredby Prof. K. Tanno, Prof. H. Tamura and Mr. K.
Nagatomo of University of Miyazaki.
References
1. H. Tamura, et al., A study on motion recognition without
FFT from surface-EMG, IEICE-part D, J90-D(9), (2007)
2652-2655.
2. Y. Kita, et al., Implementation and evaluation ofshoulder-surfing attack resistant users (In Japanese),
IEICE-part D, J97-D(12), (2014) 1770-1784.
3. Y. Kita, et al., A Study of Rhythm Authentication and its
Accuracy using the Self-Organizing Maps (In Japanese),
Proceedings of DICOMO 2014, (2014) 1011-1018.
4. H. Tamura, et al., A study on the s-EMG patternrecognition using neural network, IJICIC, 5(12) (2009)4877-4884.

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