design of emg acquisition circuit to control an...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:17 No:05 37

I J E N S IJENS © October 2017 IJENS -IJMME-3993-172505

Design of EMG Acquisition Circuit to Control an

Antagonistic Mechanism Actuated by Pneumatic

Artificial Muscles PAMs

3Alkhayat M. Mouath*and2Nibras Abo Alzahab*,1Hassan AlImam*

1. Teaching Assistant, Mechanical Design Engineering Department, Faculty of Mechanical and Electrical Engineering,

Damascus University,[email protected]

2. Undergraduate Student, Biomedical Engineering Department, Faculty of Mechanical and Electrical Engineering, Damascus

University,[email protected]

3. Assistant professor, Mechanical Design Engineering Department, Faculty of Mechanical and Electrical Engineering, Damascus

University,[email protected]

*. The experimental work was divided equally, and the names’ order does not matter.

Abstract--A pneumatically actuated antagonistic pair of muscles

with joint mechanism (APMM) is supported and developed to be

essential for bionic and biomimetic applications to emulate the

biological muscles by realizing various kinds of locomotion based

on normal electrical activity of biological muscles. This Paper

aims to compare the response of antagonistic pairs of muscles

mechanism (APMM) based on the pneumatic artificial muscles

(PAMs) to an EMG signal that was acquired throw a designed

circuit and an EMG Laboratory acquisition kit. The response is

represented as a joint rotary displacement generated by the

contraction and extension of the pneumatic artificial muscles. A

statistical study was done to prove the efficiency of the designed

circuit the response of antagonistic pairs of muscles mechanism.

The statistical result showed that there is no significant

difference of voltage data in both EMG acquired signal between

reference kit and designed circuit. An excellent correlation

behavior between the EMG control signal and the response of

APMM as an angular displacement has been discussed and

statistically analyzed.

Index Term-- Pneumatic Artificial Muscles, Biomechatronics,

Electromyogram EMG, Pneumatic Proportional Directional

Control Valve, Signal Processing, Bionic.

I. INTRODUCTION

The innovation in bio robotic systems is involving various

numbers of Mechatronics and bionic applications which

require actuators to drive the motion of this system. Pneumatic

artificial muscles (PAMs) actuators are commonly used in

various Mechatronics applications such as artificial lower and

upper limb rehabilitation devices, biomechanical systems and

industrial applications [1] [2] [3] [4].

The importance of using pneumatic artificial muscles (PAMs)

is to reach a smoother and more natural movement pattern

according to its favorable properties similar to the biological

muscles. To achieve this goal, applying Electromyography

(EMG) signal to control pneumatic artificial muscles (PAMs)

plays a key role. Electromyography (EMG) has been widely

used to control upper limb assistive rehabilitation devices,

such as orthosis devices, which can supply external power to

the limb or used for limb rehabilitation [5] [6]. These devices

are called exoskeletons which refer to any electromechanical

system that is able to be worn as an armor covering or

wrapping around the said limb to enhance its capacity beyond

its natural powers or to restore the function of weak limbs.

Exoskeleton robots are developed because of the importance

of enhancing human capacity which is limited to certain

weights that a human can carry [6][7][8][9].

Michał A. Mikulski in [10] proposed control algorithms for

EMG-based powered exoskeleton used for physiotherapy and

rehabilitation. The upper limp single Degree-Of-Freedom

(DOF) exoskeleton was built using linear electric actuators.

M.N.Shah et. al. in [11] introduced an automated device to

perform the repetitive tasks on patients that physiotherapy

treatment requires.

H.He and K.Kiguchi in [12] proposed EMG-based control for

the robotic exoskeleton to assist lower-limp motion for the

physically weak patient. The changing EMG levels which

accommodate the different user’s psychological and physical

conditions were taken into account.

Pieter Beyl et. al. in [13] considered pneumatic muscles a

reasonable choice to be utilized as a part of powered

exoskeletons because of its intrinsic compliance and high

force-to-weight proportions.

Dongjun Shin et. al. used in their research the antagonistic

actuation system based on PAMs to discuss and analyze the

effect of Varity in radius of circular pulley joint in improving

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torque capacity while maintaining a work space range based

on the static mechanical properties of this mechanism [14].

Yusuke Hashimoto et. al. in [15] developed a gait-assistive

device constructed with pneumatic artificial muscles (PAMs)

and uses EMG as an input signal.

Based on signals acquired by human muscle activities and

PAMs actuators, a new rehabilitation system was developed

by Jun-ichiro Furukawa et.al. to compare the performance of

this support system by means of normal activity acquired by

leg muscles in for experimental condition [16].

Yong Zhu in [17] developed a prototype Ankle Foot Orthosis

(AFO) applying the sensors’ measurements of smart phones,

such as gyroscopes and accelerometers. The developed AFO

prototype is actuated with a pneumatic artificial muscle

(PAM).

The most important bio mechanical mechanism, which are

still frequently used to simulate and modeling the one link

bionic arm “biceps-triceps –elbow joint system”, is

Antagonistic pairs of muscles which have been actuated by

Pneumatic artificial muscles (PAM). In previous studies,

many researchers from different university have been studying

the behavior, mechanical and mathematical modeling and

control method of Antagonistic mechanism to innovate an

orthosis, prosthesis and bio Mechatronic systems actuated by

this pneumatic flexible actuator [18] [19]. As cited in many

scientific papers, PAMs were used as an adaptive actuator in

rehabilitation and bionic systems according to their similarity

to biological muscles [20][21][22][23].

Pneumatic artificial muscles (PAMs) are defined as a single

acting actuator like a tube including a flexible and contractile

membrane contracts axially and expands radially when the air

pressure inside the muscle is increased. Moreover, the

contraction of this actuator can be controlled by decreasing

and increasing the gas pressure inside it

[24][25][26][27][28][29].

Pneumatic artificial muscles have been considered as

substitute actuator to hydraulic and electric actuators after

being used as an unprecedented actuator according to their

sophisticated technical properties [30],and special mechanical

properties similar to biological muscle such as light simplicity,

inexpensive power source , low cost , cleanliness ,inherent

safety, elasticity, flexibility, lightweight, spring

characteristics, high strength, high power-to-weight ratio and a

high power/volume ratio[31]. According to this properties

PAMs has been widely used in bionics and biomechanical

applications and in the field of rehabilitation and prostheses

devices [32][33][34][35][36].

Typically, Antagonistic pairs of pneumatic muscles

mechanisms (APMM) were controlled by electro pneumatic

proportional valve using the closed loop control methods

based on the feedback from force and position sensors

[37][38]. Furthermore, scarcely any researches were

conducted using open loop control system depending on an

acquired EMG signal. In this paper, we compare the response

of pneumatic artificial muscle, used in antagonistic

mechanism and driven by proportional directional valve, to

the EMG signal which was acquired by a reference EMG

device and the one acquired by a designed circuit by the

research team.

II. METHODOLOGY A. Experimental setup and Characteristic of Antagonistic

Pairs of Muscles Mechanism (APMM)

The pneumatically actuated antagonistic pairs of muscles

mechanism (APMM)is based on the pneumatic artificial

muscle experimental setup and used as a controlled

mechanism to imitate the interact in biological movement

generated by biceps and triceps muscle as shown in Figure 1

[39].

Fig. 1. Working principle of human skeletal muscle systems [39].

The antagonistic mechanism is designed to imitate the elbow

joint, biceps and triceps biological upper limp. The elbow

joint rotary motion is imitated by chain and sprocket

mechanism. The rotary motion of the mechanism was

converted from opposite linear contractions which are

generated by the pneumatically actuated artificial muscles

(biceps and triceps muscles). To achieve work space rang as a

rotating displacement; two PAMs actuators are used to push

and pull the chain and then rotate the sprocket by means of

contraction forces generated by pneumatic actuators [40].With

this angular displacement a working angle range of 0 to +37

degrees will be achieved. The actuators (FESTO DMSP-10-

180N-RM-CM)are characterized by contraction length 30 mm

at maximum air operating pressure 7 bar, whereas the muscle

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0ahUKEwiqvYz3lO7WAhUBvRQKHdSuDlcQFggyMAE&url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FProsthesis&usg=AOvVaw3zPUQxchTTtF6o0F9r1boJ



internal diameter is 10 mm. Only one data acquisition

component is employed to collect the feedback from angular

position generated by this APMM, therefore a rotary encoder

sensor (OPKON PRID 50 ARS LTP 1000 Z V2) is assembled

with sprocket spindle. Opposite contractions of the actuators

are controlled by a5-ports – 3-ways proportional servo valve

(FESTO MPYE-5-1/8LF-010-B) which is connected to both

artificial muscles. In order to measure air pressure in the two

PAM, two pressure sensors were connected to the working

ports between the servo valve and the pneumatics artificial

muscles. Air pressure is injected to the servo valve from

pressure supply compressor through pressure regulator from

service unite under absolute pressure value 7 bar. Pneumatic

diagram design of the Antagonistic mechanism based on

FESTO FluidSIM Pneumatics software version 4.2 with two

artificial muscles is shown in figure 2.

Fig. 2. Pneumatic diagram of Experimental system

B. Experimental platform of Antagonistic Pairs of Muscles

Mechanism

Two electronic circuits and an open source micro controller

circuit were established for experimental platform of

antagonistic mechanism, the following circuits include: EMG

Laboratory acquisition kit, EMG acquisition circuit designed

by research team, Arduino Mega2560 microcontroller for

open loop control and position sensor feedback acquisition

and PWM signal converter to analogue signal and low pass

filter circuit. A photograph of the experiment system is shown

in Figure 3.

Fig. 3. Photo of antagonistic pairs of muscles mechanism experimental system

A. Experimental platform:1. Arduino Mega2560 board, 2.

10V power supply, 3. Conditioning circuit, 4. Designed EMG

acquisition circuit. B. Experimental setup: 5. Pneumatic

service unit, 6. Manifold, 7. Proportional directional control

valve, 8. Rotary encoder, 9. Chain & Sprocket, 10. Pneumatic

artificial muscles, 11. Pressure sensors, 12. Air Compressor.

C. Experimental system: 13. Oscilloscope, 14. 24 V Power

supply. 15. Personal computer.

C. Acquired reference signal

The Reference EMG signal was acquired using KL-72001

BIOSIGNALS MESURMENT SYSTEM and KL-75002

ELECTROMYOGRAM EMG MODULE, the EMG

Laboratory acquisition kit, which consist of signal acquisition

part and enveloping part. The signal acquisition part has a

pre-amplifier, isolator, band reject filter, low pass filter, gain

amplifier and high pass filter. Enveloping part has a full-wave

rectifier and integration and buffer block. Figure 4

demonstrates a real photo of the kit.



Fig. 4. Photo of EMG Laboratory acquisition kit

D. EMG Signal Acquisition

EMG signal acquisition is an increasing aspect in the field of

bionics research. The concept of EMG signal acquisition is the

use of electrodes attached to the desired muscle to collect the

signal which is a small signal that contains noise. The noise in

the collected signal could be removed by using instrumental

amplifier and filtering. The acquired signal could be used as a

control signal after being rectified and enveloped. Rectifying

EMG signal plays a significant role to obtain the shape or

“envelop” of the signal. The EMG signal in our project was

acquired throw INA118 instrumental amplifier, digital

rectifier and digital filtering [41][42].

E. Electrodes

Electrodes area key issue that directly affects the quality of the

acquired signal. Two types of electrodes are mainly used to

obtain EMG signal, invasive inserted electrodes and non-

invasive surface electrodes. The advantage of using needle

invasive electrodes is to obtain a clear signal with high signal-

to-noise ratio which makes it very accurate in diagnosing

muscle illness. Surface electrodes are easy to use with

acceptable signal-to-noise ratio which makes it suitable for the

application of bionics. Surface electrodes were used in this

research as they are non-invasive, therefore easy to be used

[41][43]. Electrodes placement with EMG signal acquisition

designed circuit is illustrated in figure 5.

F. Pre-amplifier

Pre-amplifier stage is used for two main purposes. Firstly, to

reject the common mode that causes added noise to the signal.

Secondly, amplifying the single in order to transfer it from the

range of mV to the range of volt, so it could be sampled via

Arduino. The pre-amplifier has an instrumental amplifier

INA118 and two operational amplifiers LM741. By using

INA118 instrumental amplifier, an excellent amplifying

properties will be achieved which include: low power and

high common-mode rejection (110dB at G = 1000) with

excellent accuracy [44].The gain G could be determined by

connecting external resistors that set any gain from 1 to

10,000 according to the equation (1) [45]:

G = 1 +50 kΩ

RG (1)

Where RG is the external resistor that sets the gain value.

The designed pre-amplifier circuit schematic diagram is

shown in figure 6. The circuit was designed and simulated by

Proteus 8 professional software by Lab center Electronics Ltd.

R1, R2, R3 and R4 comprise RG. From the mid-point of R1,

R2, R3 and R4, a signal is fed into LM741 to generate a

reference potential. Each R1, R2, R3 and R4 is 10Ω, therefore

the gain value G is equal to 1251 according to the equation

(1).

Fig. 5. Electrodes placement with pre-amplifier circuit

Fig. 6. The designed pre-amplifier circuit schematic diagram



G. Signal Sampling

Sampling analog signal into Arduino Mega2560 board is

achieved by the embedded analog-to-digital converter (ADC)

using 10 bits resolution. Sampling rate should commit to

Nyquist frequency “a sinusoid can only be correctly recreated

if it is sampled at no less than twice its frequency” i.e.

sampling rate frequency should be at least twice the highest

frequency component in the signal[46], whereas the highest

frequency component in biceps muscle EMG signal is 450 Hz

[43][47]. To sample EMG signal, 9615.38 Hz sampling rate

frequency is used. This could be calculated by dividing 16Mz,

which is the ADC clock, by a prescale factor that is set to a

default value equals to 128 (16MHz/128 = 125 KHz). The

conversion takes 13 ADC clocks; hence the sample rate is

about 125KHz/13 or 9615.38 Hz [48]. The signal was sampled

into Arduino Mega2560 board and transferred to MATLAB

Software version r2016 where it was saved as .txt file to be

filtered later [49].

H. Signal Processing

In order to obtain a useful signal which could be used as a

control signal, it should be denoised, rectified and filtered

[50]. The first step in EMG signal processing is to delete the

DC component which is caused by the environment and the

circuit noise. Deleting DC component was done by MATLAB

function “detrend”. The second step is to take the absolute

value of the signal which is called full wave rectification in

order to obtain the shape or “envelop” of the signal. Signal

rectification was done by MATLAB function “abs”. If the

EMG signal is filtered and smoothed without being rectified a

zero value is the result, which shows the importance of

rectification. Thirdly, filtering and smoothing the signal was

achieved by the usage of 5th order Butterworth digital low pass

filter with 450Hz cut-off frequency. The filter was built by

MATLAB function “butter(5,450/9600,'low')” and

applied by MATLAB function “filtfilt”. In order to

make the signal easy to be used, it was digitally amplified [47]

[49] [51].

I. Conditioning Circuit

The aim of this step is to transform the output signal from

Arduino Mega2560 board, 5V PWM signal with frequency of

490Hz, to adapt the proportional directional control valve’s

analog set point voltage, which is in the range of 0 to 10V. To

achieve the required input characteristics of the proportional

valve, a conditioning circuit was designed and used. The

circuit is consisted of three main sections. Firstly, PC817

isolator was used to protect the Arduino Mega2560 board.

Secondly, C2328 transistor-based amplifier is used in

saturation mode with 10V supplement. Thirdly, a low pass

filter with 450Hz cut-off frequency smooths the PWM signal

which results an analog-like signal with 10V maximum value.

Figure 7 represents the schematic diagram of designed

conditioning circuit.

Fig. 7. Schematic diagram of designed conditioning circuit with conditioning

steps

J. Data analysis

Statistical analysis was used to study the EMG acquired signal

data and antagonistic pairs of muscles mechanism response to

the control signal [52] [53]. Data were analyzed using

software Statistical Package version 17.0. Paired Samples T

Test was employed to examine if there is any significance

difference between means for two EMG voltage data

(Acquired by designed circuit & Reference kit). The

significant level was set at statistical significance level 0.05.

To describe and analyze the relationship between EMG

control signal (Acquired by designed circuit & Reference kit)

data and angular position values and to determine the degree

of correlation between studied variables, Bivariate Correlate

test was employed in Statistical analysis with correlation

coefficient (Pearson’s Correlation r) at statistical significance

level 0.01 [54]. EMG signal data and statistical results were

compared and further discussed.

III. RESULT AND DISCUSSION

Digital signal processing (DSP) was carried out using

MATLAB Software version r2016. For acquired EMG signal

the DSP procedure is consisted of three steps: Removing DC

offset, EMG signal rectifier and filtering with amplifying final

signal to maximum 5 V amplitude voltage (signal envelop). In

the otherwise, EMG reference signal was only rectified and

amplified. The response of EMG signals with time through

DSP steps are shown in figure 8 and figure 9.



Then, final voltage data which is obtained from EMG signal

after DSP were further tested statistically using SPSS software

version 17.0.

Fig. 8. The DSP of EMG signal

Paired samples T test was employed at 0.05 significance level

to determine whether the significance difference between

means for two sets of EMG voltage data (Acquired by

designed circuit & Reference kit) is the same or different.

Since the size of EMG voltage data samples through the

response sampling time is larger than (30) and equal to (98)

samples, the difference scores of EMG voltage data are

normally distributed. The data were continued being analyzed

by Paired-Sample T Test using SPSS as shown in Table (1).

TABLE I

Paired-Sample T Test Factors

Paired Variables T test factors

Mean Mean

Differences Error % Correlation T value Sig. (2-tailed)

Pair

EMG

signals

Reference

Kit 2.1271

-0.1063 4.997 0.884 -0.986 0.326 Designed

circuit 2.2334

Although all previous figures (8, 9) show some variance in

EMG signals envelop. However Paired T test factors

demonstrate different results. As shown in table (1) voltage

data in both EMG acquired signal between reference kit and

designed circuit has no significant difference and have

excellent correlation during Paired T test:

(r = 0.884, P < 0.05 &t (97) = −0.986, P(2 tail) > 0.05)

By looking at the Md value (𝑀𝑑 = −0.1603) in table (1), it

indicates that there is a very nominal difference between

means value related to both acquired and reference EMG

signals. And the minus sign indicates that the reference EMG

signal data is less than EMG acquired data with 4.997%

relative error between two studied signals. The reason of this

error is the usage of 1000 times signal amplification in

acquisition circuit which led to this result. To minimize that

error by increasing the accuracy of the acquired circuit,

the amplification stage in this circuit can be divided into two

stages: X10 times amplification through INA118 and X100

through additional LM741 operational amplifier as a non-

inverting amplifier [47].



Fig. 9. The DSP of EMG reference signal

After proving that there is no significant difference between

two EMG acquired signals, the signals were used as a control

signal to control opposite linear contractions related to PAM

by means of Proportional control valve. Whereas the EMG

signal is conditioned to achieve the required input

characteristics of the proportional valve with voltage range (0-

10 V). EMG control signals are depicted in figure 10. The

response of Antagonistic Pairs of Muscles Mechanism to

control EMG control signals which are represented in sprocket

rotary displacement is shown in figure (11, 12). Figure (11)

illustrates that the rotary displacement reaches a maximum

angular position (37°) with maximum EMG control signals

amplitude and it is decreased by reveres linear contraction of

PAM which is generated by decreasing the EMG signal value.

To examine and analyze the strength of the relationship

between EMG control signal and angular position, Bivariate

correlation test was employed at 0.01 significance level. Data

illustrated in table 1 confirms that there is a significant

positive relationship exist between EMG control signal

(Acquired by designed circuit & Reference kit) and angular

position, test scours were strongly correlate

(rEMG Reference (98) = +0.812, P <

0.01) &(rEMG circuit (98) = +0.819, P < 0.01).

Thus higher angular position values are related to higher EMG

voltage data.



TABLE II Bivariate Correlation Test Factors

Correlation variables Correlation test factors

Mean Pearson Correlation coefficient

EMG (Reference) control signal

&

angular position

4.25428 +0.812

19.03631

EMG (Designed circuit) control signal &

angular position

4.46674 +0.819

19.05214

Fig. 10. EMG control signals (A. Acquired by Designed Circuit, B. Acquired by Reference kit)

Fig. 11. Response of APMM to EMG control signals (acquired by designed circuit)



Fig. 12. Response of APMM to EMG control signals (acquired by Reference kit)

IV. CONCLUSIONS

The paper discusses the concerns related to EMG signal

aquatinting and filtering and the response of antagonistic pairs

of PAMs mechanism as the rotation of the sprocket. The

experimental system show high reliability in performance,

which was divided into two main parts: Experimental platform

and Experimental setup. Two statistical tests were carried out

to the extracted features. Based on the results have been

obtained from this study, the comparison between reference

kit and designed circuit shows that there are no significant

difference between EMG acquired signals voltage data. A

strong relationship between the EMG control signal and the

response of APMM as an angular displacement can be

concluded. Depending on the results of the statistical study of

the experiment results, the designed circuit is as effective as

the EMG Laboratory acquisition kit and it is able to

successfully acquire EMG signal and convert it into a control

signal. This reliability in performance is commensurate with

the low value of the cost of manufacturing this circuit

compared to the laboratory kit and other commercial circuits.

The total cost of manufacturing this circuit is 9 USD, which is

only in the range of (4% to 10%) of the price of the laboratory

kit according to its originality and manufacturing sources.

Unfortunately, the angle of rotation is only 37° which is not

effective to be applied in an exoskeleton device.

V. FUTURE STUDIES

In order to make this system able to be worn as an exoskeleton

device, many features could be added. Increasing the range of

rotation requires increasing of the contraction length. In order

to achieve absolute angle of rotation, a closed loop control

system must be applied e.g. Proportional, Integral, Differential

(PID) control system. The most important enhancement of the

system is that it could be used in Brain-Computer Interface

(BCI) based exoskeletons as a stimulation signal trigger

response.

ACKNOWLEDGMENT

This study was not funded by any grant. The authors would

like to thank the staff of Automatic Control and Industrial

Automation ACIA Laboratory, and Biomedical Engineering

Department in the Faculty of Mechanical and Electrical

Engineering; Thanks go also to Prof. Mahmoud Bani Al-

Marjeh Head of Mechatronics Department in Damascus

University for his valuable comments and discussions on this

research.

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