2015 International Conference on Pervasive Computing (ICPC)
Review on Hand Gesture Based Mobile Control
Application
Swapnil M.Mankar
Department of Computer Science and Engineering
G. H. Raisoni College of Engineering Nagpur, India
smankar03@gmail.com
Abstract- Inexisting interfaces such as Digital pen which capture
only one kind of gesture and in proposed system interfaces it
brings a new interaction process. It can recognize both small
scale and large scale gestures. An algorithmic framework is for
information
about
Acceleration
and
Surface
Electromyographical (SEMG) signals for gesture identification.
In addition, the system consist of a wearable gesture sensing
device and an application program along with the algorithmic
framework for a mobile phone, will be designed to realize
gesture-based real-time interaction. The device which is worn on
the forearm, using that device user will be able to manipulate a
mobile phone using predefined gestures or even personalized
ones. In this system Accelerometer Sensor measure large scale
gesture and SEMG sensors measure the small scale gesture. Both
this sensors are used for the gesture recognition.
Keywords- Accelerometer; gesture based real time interaction;
hand gesture; large scale gesture; small scale gesture; surface
electromyography.
I.
INTRODUCTION
Sensing and identifying gestures are two important issues
to recognize gestural user interaction. In the proposed system
an algorithmic framework will be designed to realize
acceleration and Surface Electromyographical (SEMG)
signals for gesture recognition as given in paper [1].In
addition, the system having a wearable gesture sensing device.
This system also includes an application program with the
proposed algorithmic framework for Smartphone will be
designed to recognize gesture-based real-time interaction.
Accelerometer sensor and Surface Electromyography (SEMG)
sensor supports another two potential technologies for gesture
sensing. Accelerometer is used to measure Accelerations
(ACC) from vibrations and the gravity; therefore, they are
good at capturing noticeable actions with more accuracy, and
easily recognize a large-scale gesture which is given in paper
[1]. Surface Electromyographical signals, which recognize the
motion of related muscles during a gesture execution as given
in paper [5]. Surface Electromyographical signal have
advantages in capturing fine motions such as wrist and finger
movements and can be utilized to realize human–computer
interfaces. For example, a commercial gesture input device
named MYO is a wireless armband with several SEMG
sensors designed for interactions. Various kinds of interaction
solutions can be developed using its programming interface.
Since both Accelerometers and SEMG sensors have their own
advantages in capturing hand gestures, the combination of
both sensing approaches will improve the performance and
accuracy of hand gesture recognition .The proposed system
which is to be designed i.e. a wearable gesture-capturing
device consist of a gesture-based interface for a mobile phone
to demonstrate the feasibility of gesture-based interaction in
the mobile application which is given in paper [6] in different
way. In the proposed system consist of a wearable gesture-
978-1-4799-6272-3/15/$31.00(c)2015 IEEE
Sharda A. Chhabria
Department of Information Technology
G. H. Raisoni College of Engineering, Nagpur,
Indiasharda.chhabria@raisoni.net
based real-time interaction prototype for mobile devices using
the fusion of ACC and SEMG signals are presented.
II.
RELATED WORK
In Hand Gesture Recognition much more work has been
carried out. In hand gesture recognition different techniques
are used to recognize the gestures. Gesture recognition
includes different type of gestures such as facial gesture, hand
gesture, eye movement. Following are the techniques which
are used to identify the hand gestures.
Paper [1] designed a wearable gesture-based interaction
prototype which demonstrates the feasibility of hand gesture
interaction in mobile application. The system is based on the
fusion of Surface Electromyographical and Accelerometer
signals. A wireless wearable gesture capture device is
designed to acquire ACC and SEMG signals, and an algorithm
framework is proposed to realize gesture classification on
mobile devices.
Paper [2]presents a framework which is based on
algorithm i.e. systematic trajectory recognition algorithm.
This algorithm can be able to design effective classifiers for
acceleration based handwriting recognition and gesture based
recognition. The[2] paper based on a trajectory recognition
algorithm which includes different procedures such as
procedure of feature selection, signal pre-processing,
acceleration acquisition, feature generation, and feature
extraction with the reduced features. In this system digital pen
is used to make the hand gesture and writing digit. The hand
motionsare recognized through the accelerometer and
wirelessly transformed to system for online trajectory
recognition.
Paper[3] presents three different models which are based
on gesture recognition. The models which are designed in this
system are capable of recognizing different hand gestures. The
result which is obtained it is based on the basis of input
signals received from MEMS 3-axes accelerometer. It
describes a system by using MEMS accelerometer for
nonspecific person gesture recognition. The three channel
accelerometer which is presented in paper [1] recognizes the
accelerations of a hand in three different perpendicular
directions respectively. This signal is then pass on to the PC
via Bluetooth wireless protocol. Sign sequence of gesture
acceleration is the basic feature of the system which is
presented in paper [3]. This method reduces data values of the
gesture. This algorithm can be used for nonspecific user handgesture recognition using this system. In this system output is
obtained in less time with accuracy.
Paper [4] present system which identifies the different sign
language gesture which is based on technology also known as
Sign Language Recognition (SLR), mainly for large
vocabulary sign language recognition system. The systems
consist
of
portable
accelerometer
and
surface
electromyographical sensor which is given in paper [1]. The
complete framework has been designed at the component
level for automatic Chinese SLR.
Paper [5] presents framework which includes the
information fusion of three axis accelerometer as given in
paper [1] [2] [3]. It also includes multichannel
electromyographical sensors for hand gesture recognition. In
paper [5] gives the information about the gesture segment in
terms of start point and end points of gesture segment. The
recognization of meaningful gesture segments using the
startpoint and end points of gesture segments are recognized
automatically based on the intensity of the EMG signal.
III. PROPOSED WORK
The proposed system includes the controlling of mobile
operation using multiple hand gestures with the help of
interfaced wearable computing module. This wearable
computing module is responsible for taking all decision
according to the hand gestures. This wearable Computing is
responsible for the complete execution of the operation of
mobile in which hand gesture is an input signal. The proposed
system will be user-friendly for user so that it becomes easy to
control the system through hand gesture. The proposed system
can be implemented in the daily life use.
IV. CONCLUSION
The techniques which are used to recognize the hand
gesture reviewed through this paper. This is a review paper so
that conclusion is not possible in terms of result or numerical
values. After reviewing the different techniques for
recognition of hand gesture, recognition of hand gesture
through SEMG sensor and Accelerometer is the best
technique on the basis of the techniques which are reviewed
through this paper.
References
[1]
[2]
[3]
[4]
Zhiyuan Lu, Xiang Chen, Member, IEEE, Qiang Li. Xu Zhang,
Member, IEEE and Ping Zhou, Member, IEEE, “A Hand Gesture
Recognition Framework and Wearable Gesture-Based Interaction
Prototype for Mobile Device” inIEEE transactions on human-machine
systems,vol.44,no.2,April 2014.
J. Wang and F. Chuang,“An Accelerometer-based Digital Pen with a
trajectory recognition algorithm for handwritten digit and gesture
recognition”, IEEE Trans. Ind. Electron, vol. 59,no. 7, pp. 2998-3007,
July 2012.
R. Xu, S. Zhou and W.J. Li.,“MEMS accelerometer based nonspecific
user hand gesture recognition”, IEEE Sensors J., vol. 12, no. 5, pp.
1166-1173,May 2012.
Yun Li, Student Member, IEEE, Xiang Chen ,Member, IEEE, Kongqiao
Wang, and Z. Jane Wang, Member, IEEE,“A Sign –Component-Based
Framework For Chinese Sign Language Recognition Using
Accelerometer and SEMG Data”, IEEE transactions on biomedical
engineering, vol. 59, no. 10, October 2012.
[5]
Xu Zhang , Xiang Chen, Associate Member, IEEE, Yun Li,
Vuokko Lantz, Kongqiao Wang and Jihai Yang,“A Framework for
Hand Gesture Recognition Based on Accelerometer and EMG Sensor”,
IEEE transactions on systems, man and cybernetics - part a: systems and
humans, vol. 41, no. 6,November 2011.
[6] C. Zhu and W. Sheng,“Wearable sensor based hand gesture and daily
activity recognition for robot- assisted living”, IEEE Trans. Syst., Man,
Cybern. A, Syst. Humans, vol. 41, no. 3, pp. 569-573, May 2011.
[7] S. Vernon and S. S. Joshi,“Brain-muscle-computer interface: Mobile
phone prototype development and testing”,IEEE Trans. Inform.
Technol. Biomed., vol. 15, no. 4, pp. 531-538, Jul. 2011.
[8] T. S. Saponas, D. S. Tan, D. Morris, J. Turner and J. A. Landay,
“Making muscle-computer interface more practical”, in proc. SIGCHI
Conf. Human Factors Computer. Syst., 2010, pp. 851-854.
[9] A. Hatano, K. Araki and M. Matsuhara,“A Japanese input method for
mobile terminals using surface EMG signals”, in Proc. 22ndAnnu. Conf.
Jpn.-Soc.,Artif. Intell, 2009, pp. 5-14.
[10] Xiang Chen, Xu Zhang, Zhang-Yan Zhao, Ji-Hai Yang, Vuokko Lantz,
Kong-QiaoWang.,“Multiple Hand Gesture Recognition based on surface
EMG Signal”, 1-4244-1120-3/07/$25.00©2007 IEEE.
[11] Kevin R. Wheeler, Senior Member, IEEE, Mindy H. Chang and Kevin
H. Knuth,“Gesture—Based Control and EMG Decomposition”, IEEE
transactions on Systems, man and cybematics- part c: applications and
reviews, vol. 36, no. 4, July 2006.