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ELECTROMYOGRAPHY BASED FINGER MOVEMENT
IDENTIFICATION FOR HUMAN COMPUTER INTERFACE
Pah Nemuel D, Kumar Dinesh K
School of Electrical and Computer Engineering RMIT University GPO Box 2476 Melbourne,VIC 3001,Australia
Keywords: Surface Electromyogram, Wavelet Network, Neural Networks, and Rehabilitation.
Abstract: This paper reports experiments conducted to classify single channel Surface Electromyogram recorded from
the forearm with the flexion and extension of the different fingers. Controlled experiments were conducted
where single channel SEMF was recorded from the flexor digitorum superficialis muscle for various finger
positions from the volunteers. A modified wavelet network called Thresholding Wavelet Networks that has
been developed by the authors (D Kumar, 2003) has been applied for this classification. The purpose of this
research was towards developing a reliable man machine interface that could have applications for
rehabilitation, robotics and industry. The network is promising with accuracy better than 85%.
1 INTRODUCTION
With greatly improved computational power, and
use of computers having exploded into every walk
of life, there is a greater need for flexible, natural
and reliable human computer interface. Hand
movement gestures play a very important role in the
interactions between people. But most of the
interaction with computers is based static events
such as a key press, and the information contained in
the dynamic gesture is lost, greatly reducing the
scope of machine interaction. There is thus need for
simple and reliable methods for human hand action
identification by machines. This paper reports a new
technique for automatic recognition of human hand
movements.
Skeletal movement is caused by or prevented by
muscle contraction. Muscle contraction is a result of
electrical stimulation received from the nerves to
individual muscle fibres. The resultant electrical
activity can be recorded by electrodes kept in the
close proximity of the muscles. Surface
electromyography (SEMG) (J Cram, 1998) is the
recording of the electrical activity of skeletal muscle
from the skin surface. It is a result of the
superposition of a large number of transients
(muscle action potentials) that have temporal and
spatial separation that is semi-random.
SEMG signal is the electrical recording from the
surface and represents the summation of the
electrical activity from all the muscle fibres and thus
the summation of all Motor Unit Action Potentials
(MUAP) in the region of the electrodes. The origin
of each of the MUAP is inherently random, non-
stationary, and the electrical characteristics of the
surrounding tissues are non-linear. Distribution of
the magnitude of SEMG can be approximated by a
Gausian function (J Cram, 1998).
SEMG is used for a number of applications
including control of Human Computer Interface
(HCI), prosthesis control (Hudgins, 1993, D Graupe,
1975,F Chan, 2000), muscle diagnostic and
biofeedback. Amplitude and spectral information of
EMG have also been exploited to estimate muscle
fatigue and force of muscle contraction and torque
(K Englehart, 1999). These applications require
automated analysis and classification of SEMG. The
complexity of the signal makes this a challenging
task. The authors have reported using combination
of three channels SEMG from the forearm to
identify the hand action. The difficulty of using
multiple channels is the need for precise positioning
of the electrodes by an expert.
For automated classification of SEMG related to
movement, it is essential to develop the system that
can extract appropriate features of SEMG with
respect to the movement and have a mechanism for
relating these features to the movement generating
the signal without the need for multiple channels.
The earlier SEMG classification techniques were
based on the statistical analysis of the signal
properties (Hudgins, 1993). Auto Regressive (AR)
221
Nemuel D. P. and Dinesh K K. (2004).
ELECTROMYOGRAPHY BASED FINGER MOVEMENT IDENTIFICATION FOR HUMAN COMPUTER INTERFACE.
In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 221-226
DOI: 10.5220/0001146902210226
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