The Eighth International Conference on Electronic Measurement and Instruments ICEMI’2007

Study on Measurement and Processing Technology of

Electromyography Zhang Xiaodong Luan Haojie

Northwestern Polytechnical University, Xi’an 710072 China

Abstract: For electromyography applied in the control of the

robot hand and the prosthetic hand, the exciting principle and

basic features of electromyography are firstly analyzed in this

paper. And its measurement system is designed on the basis of

the analysis, which includes the whole design of the

measurement system, and the design of its amplification and

filter circuit, etc. Finally, the developed measurement system is

applied in the control of a virtual hand by programming in

computer, and a good control result is achieved.

Keywords: Electromyography; Measurement; Signal

Processing; Virtual Hand; Control.

1 Introduction

Electromyography signal (stated as EMG for simplicity) is an electrophysiological manifestation of forelimb extensor and flexor when individual performing certain operations and actions. If the signal could be well measured, analyzed and discriminated, it is possible to perform precise control of robot hand or prostheses hand utilizing EMG as an incoming signal. To realize this goal, much research work had been done [1] [2]. Most of the previous work focuses on the design of robot hand and the research of methodology of EMG signal discrimination, however, seldom refers to design measurement circuit. Based on this point, this paper presents an EMG signal measurement circuit constructed in International Cooperation laboratory and an integrated EMG classification, recognitions system. Finally, a visual robot hand is created as a real-time controlled object to test the validity of the proposed system.

2 Electrophysiological Analysis of EMG Signal

EMG signal generates in motoneurone of spinal marrow, which is part of the nervous centralis. The signal transmits through nerve fiber, from axone to muscle fiber and couples in muscle fiber at the endplate of nerve fiber. Under the control of nerves centralis, the motoneurone generates electric impulse, which conducts from axone to muscle fiber. The impulse activates the contraction of muscle fiber and arouses muscle tension. Meanwhile, the transmission of electric impulse generates electric field in human parenchyma causing the electric potential differences in measurement electrodes. SEMG is a synthetic electrophysiological reflection of skin when muscle action occurs. It is also closely related to the activity of many deep muscles and combines with noise signal. To extract information about the action patterns of the body from SEMG, the first problem is to determine the proper positions of electrodes. In general, a pair of skin of the muscles that mainly account for the same movement of human body is considered ideal measure positions, for example, flexor or extensor. The electrode is circular; the distance between the electrodes is about 30-40mm and is placed along the longitudinal midline of the muscle on the area with more muscular mass. The positions of the electrodes could also be adjusted according to the experiment in order to acquire the strongest SEMG signal. To discriminate more action patterns, additional electrodes are needed.

Fig 1 is a simplified SEMG model. The electric impulse that generates in central nerve and transmits

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The Eighth International Conference on Electronic Measurement and Instruments ICEMI’2007

through axone provides a signal source. The expression of the signal is presented as follow.

1iik )tt()t(u

(1)

Fig 1 Model of EMG Signal Generation

The signal transmitting in axone is equivalent to

delay linkage as )t( i .

The surface voltage of active muscle fiber is identical to impulse response as.

)t(p)t(h kk (2)

The impact of muscle depth could be described by

another impulse response as . )t(gk

Consequently, the Motor unit action potential train

(MUAPT) is the joint effort of links

as follow [3].

)t(mk

)t(h,),t(h)t(h M21

(3)

N

1ikkikk )t(g)t(p)t()t(u)t(m

Finally, the EMG signal x (t) is the summation of MUAPT as follow.

(4)

M

1kk )t(m)t(x

3 Analysis for Basic Features of EMG

SEMG is a kind of weak electrophysiological signal. Fig 2 and 3 respectively show its features in time domain and its power spectral distribution. It is

clear to see from Fig 2 that the magnitude of the signal ranges from 0.1 to 5mV. Similarly, from Fig 3, we could appreciate that most energy concentrate in a range of 50~150Hz. For example, Ronager J, a Researcher in nerve and muscle research centre, Boston University using double electrodes model, discovered that the majority of relevant information concerning movement distribute among 20-500Hz. Furthermore, most EMG signals are found in a range of 50-150Hz [4] [5].

Fig 2 EMG Signal in Time Domain

Fig 3 EMG Signal in Frequency Domain

4 Construction of Measurement System for EMG

Based on the foregoing analysis of basic characters and electrophysiological origin of the EMG signal, we construct an EMG measurement system in lab shown as Fig 4. We employ two circular electrodes to acquire EMG signal from both flexor and extensor and one electrode for reference. The purpose of the

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The Eighth International Conference on Electronic Measurement and Instruments ICEMI’2007

reference electrode is to ensure that the flexor and extensor EMG signal could be a suitable input for a differential amplifier and a filter circuit. The amplified and filtered EMG signal is sampled, converted into digital signal and final processed by a computer; a neutral network is employed to perform pattern recognition (not referred in the paper). The recognition result is used to control the visual robot hand to realize certain tasks.

Fig 4 Frame of the Proposed Measurement System

(a) Frame

(b) Circuit diagram

Fig 5 The Proposed Amplification and Filter Circuits

Fig 5 shows the proposed amplification and filter circuits. The amplification circuit consists of two stages. In the first stage, an amplifier with a 20dB gain is implemented, amplifying the millvolt magnitude signal 10 times. A low pass filter is used to eliminate high aliasing frequencies over 300Hz. Then, the signal passes the second stage amplifier and gains a 40dB.

As a result, the signal is transformed from the magnitude of millvolt to that of volt, which could be sampled by an A/D convert. Finally, to avoid electromagnetic interference, we utilize a 60Hz stopband filter.

5 Application of EMG to the Control of a Virtual Robot Hand

To validate the EMG signal analysis and measurement system presented in this paper, we apply it to the control of a visual robot hand in the lab as shown in Fig 6. We develop the visual robot hand with visual c++ 6.0, which could realize a simulation of three fingers movement on the computer. When the subjects open or close his/her hands, the EMG signal acquired from both flexor and extensor is inputted to the proposed system. The signal that is measured, analyzed and recognized by the system is used to drives the visual robot hand to perform open and close movement. Simulation result shows that the proposed system correctly classifies 100% of the EMG signal and successfully applies to the control of visual robot hand.

Fig 6 Real-time Control of a Robot Hand

6 Conclusion

SEMG is a kind of weak electrophysiological signal, whose magnitude in time domain ranges from 0.1 to 5mV, and its most energy concentrate in a range

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The Eighth International Conference on Electronic Measurement and Instruments ICEMI’2007

of 50~150Hz. In order to sample the weak EMG signal, the

amplification circuit of EMG measurement system should consist of two stages, namely the first stage with a 20dB gain and the second with a gain of 40dB. In the meantime, a low pass filter should be used to eliminate high aliasing frequencies over 300Hz, and a 60Hz stopband filter must also be adopted to avoid electromagnetic interference.

Simulation result shows that the proposed system correctly classifies 100% of the EMG signal and successfully applies to the control of visual robot hand.

Acknowledgement

The authors are very grateful for the great helps and supports

provided by Professor Hyouk Ryeol Choi, School of

Mechanical Engineering, Sungkyunkwan University, Korea.

References

[1] Lei Min and Wang Zhizhong. The Study Advances and

Prospects of Processing Surface EMG Signal in Prosthesis

Control, Chinese Journal of Medical Instrumentation.

Vol.25, No 3, 2001

[2] Luo Zhizeng and Wang Rencheng. Study of Myoelectric

Bionic Artificial Hand with Tactile Sense, Chinese Journal

of Sensors and Actuators. Vol.18, No 1, 2005

[3] Luo Zuneng. Practical electromyography, Beijing: People’s

Medical Publishing House, 2000

[4] Ronager J. and Christensen H. Power spectrum analysis of

the EMG pattern in normal and diseased muscles, J Neuro.

Sci, 1989

[5] George N. and Thomas P. EMG pattern analysis and

classification for a prosthetic arm, IEEE Trans. on

Biomedical Engineering. Vol.29, No 6, 1982

Author Biography

Zhang Xiaodong: received the B.E. degree in energy and

power engineering from Xi’an Jiaotong University, Xi’an,

China, in 1989, and the M.E. and Ph.D. degree in mechanics

from Xi’an Jiaotong University, Xi’an, China, in 1992 and

1996, respectively.

Since 1996 he has become a teacher, and in 2006 promoted as a

professor in school of Engine and Energy, Northwestern

Polytechnical University, Xi’an 710072, China. From 2001 to

2002, he was a visiting scholar about three months in

department of Mechanical and Medical Engineering, University

of Bradford, Bradford, UK. From 2003 to 2005, he was a

researcher about two years in school of Mechanical

Engineering, Sungkyunkwan University, Korea. His recent

research interests are EEG & EMG signal processing and its

application on robotics, intelligent measurement and control

technology, and instrumentation.

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