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Bio-mimetic Behaviour of IPMC Artificial
Muscle Using EMG Signal
R.K. Jain*, S. Datta*, S. Majumder*, S. Mukherjee**, D. Sadhu**, S. Samanta** and K. Banerjee***Scientists, **Students
DMS/Micro Robotics Laboratory
Central Mechanical Engineering Research Institute, Durgapur-713209, (CSIR), West Bengal, India.Corresponding author e-mail: rkjain@cmeri.res.in
AbstractAssistive devices such as prosthetic and
orthotic devices as well as micro robotic arm can perform
similar operation of a human arm when these are actuated
by muscle power. The generated force is transferred
through an electro-mechanical system. Most of electro
mechanical devices are heavy and are not compatible with
muscular system. However, an ionic polymer metalcomposite (IPMC) has tremendous potential as an
artificial muscle and is driven by a low voltage range
between -3 to +3 V. In this paper, bio-mimetic actuation of
IPMC is studied. The electric voltage is detected by
electromyographic (EMG) signal through human muscle
and is transferred to IPMC. It is observed that an IPMC
shows similar behaviour and controls of a human fore
arm.
Keywordsbio-mimetic, artificial muscle, IPMC,
actuator and EMG.
I. INTRODUCTIONThe assistive devices such as prosthetic and orthoticdevices need soft actuating systems which arecontrolled through human muscle power. The
emergence of effective electroactive polymer (EAP),also known as artificial muscles can potentially address
this need [1,2]. EAP responds to an electricalstimulation with a significant change of shape or size
and this characteristic behavior can be implemented fordesigning actuation-driving mechanism and improvethe quality of life. Especially, ionic polymer metal
composite can be widely applied to the artificial muscle
This work is supported in part by the Council of Scientific andIndustrial Research (CSIR), New Delhi, India (Grant No. NWP-30,2007-2012) under eleventh five year plan on Modular Re-
configurable Micro Manufacturing Systems (MRMMS) for MultiMaterial Desktop Manufacturing Capabilities.
S. Mukherjee, D. Sadhu, S. Samanta and K. Banerjee are students ofAsansol Engineering College, Asansol, India who are carrying out thefinal year B. Tech Project at CMERI, Durgapur, India. Rests of theauthors are with the Design of Mechanical System Group and MicroRobotics Laboratory, Central Mechanical Engineering Research
Institute (CMERI), Durgapur-713209, West Bengal, India.(Corresponding author email: rkjain@cmeri.res.in; Tel No +91-343-6452137)
because it is easily manufactured and is driven by
relatively low input voltage (3V) [3]. IPMC bends
towards the anode when subjected to a voltage acrossits thickness due to cation migration towards cathode in
the polymer network. When the polarity is changed, it
bends in the reverse direction. In this aspect, theactuating voltage for IPMC is given through our humanmuscle in place of battery source. For this purpose, anelectromyographic (EMG) signal approach is carried
out to transfer the signal from our muscle power. Inpast, Jou et al. (2006) has studied an articulatory feature
classification of face muscles using surfaceelectromyography [4]. Natarajan et al. (2002),
Karakostas et al. (2003)and Kunju et al. (2009) havemodeled the human arm interface for muscle activity,the quantification index of co-contraction at the knee
during walking gait and walking pattern respectively[5-7]. Daley et al. (1990), Hudgins et al. (2000) and
Light et al. (2002) have focused towards the
microprocessor-based multifunctional myoelectriccontrol system. Abdallah et al. (2009) and Ahsan et al.
(2009) have concentrated on step signal of EMG [6-13].Two-dimensional myoelectric control system of arobotic arm for an upper limb amputee and real-time
upper limb motion from non invasive biosignals with
physical human-machine interactions have been studiedby Celani et al. (2007) and Kwon et al. (2009)
respectively [14, 15]. Naik et al. (2010) has also studied
the pattern classification of myo-electrical signal duringdifferent maximum voluntary contractions using BSStechniques for a blind person [16]. Murphy et al. (2009)
has explored the micro electro-mechanical systemsbased sensor for mechanomyography [17]. Lee et al.s
(2004) and Al-Faiz et al.s (2009) research work arerelated to the control of IPMC system for handprosthesis and arm movement recognition based on
EMG signal respectively [18,19]. In furthercontinuation, we are exploring the bio-mimetic
behavior of an IPMC using EMG for micro robotic arm.
The objective of this paper is to examine the bio-
mimetic behavior of IPMC through EMG signal. Whena forearm moves in different angles, generated signal is
2010 International Conference on Advances in Recent Technologies in Communication and Computing
978-0-7695-4201-0/10 $26.00 2010 IEEE
DOI 10.1109/ARTCom.2010.49
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transferred to IPMC which allows bending. This
bending behavior is utilized in IPMC based microrobotic arm movements for micro manipulation system.
II. FORE ARM BEHAVIORA. EMG signal behavior for fore armDuring actuation of an IPMC, an EMG signal is the
measured electric potential produced by voluntarycontraction of muscle fiber. The frequency range of theEMG signal is within 4 to 900 Hz. The dominant
energy is concentrated in the range of 100 Hz and
amplitude of voltage range is 5.5 mV according to
muscle contraction. Using these parameters, the circuitfor filter signal is designed using MATLAB
SIMULINK software as shown in Figure 1. In blockdiagram, the active EMG signal is taken from muscle
and uniform noise is considered. The electric potentialis first amplified with gain 32 dB and then band passfilter (BPF) is used with frequency range 4 to 900 Hz.
Using band stop filter (BSF), noise signal that arisesdue to AC coupler power is eliminated. The signal is
then passed through an amplifier with gain 60 dB.Subsequently, integrators are used to acquire better
damping signal. The filtered signal is then passed toIPMC through ADC/DAC along with control system.The output signal after filtering is shown in Figure 2. It
shows that the EMG signal is taken from muscle in the
range of 5.5 mV. After filtering the noise, steady statesignal is obtained.
Figure 1 Block diagram of EMG signal behavior
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-8
-6
-4
-2
0
2
4
6
8x 10
-3
Time (s)
Vo
ltage
(V)
EMG behaviour Voltage Vs. Time
Figure 2 Voltage corresponding to EMG signal behavior
B. Configuration of fore arm motionThe different configurations ranging from 00to 1800are
considered for measurement of displacement in actualenvironment as shown in Figure 3. The initial
articulation of the arm is formed by having 00betweenthe upper arm and forearm. IPMC actuates in a similarmanner according to the fore arm movement. The
magnified displacement of IPMC strip with fore armposition is obtained as shown in Figure 4. It follows the
exponential curve with maximum displacement 14 mm.This displacement would be utilized as an IPMC basedmicro robotic arm for lifting operation.
(a) (b) (c)
(d) (e)
Figure 3 Schematic layout for different positions of fore arm
Figure 4 IPMC displacement response with fore arm position
III. EXPERIMENTAL SETUPThe testing setup for bio-mimetic behavior of IPMC is
shown in Figure 5. For actuation of IPMC throughhuman muscles, EMG electrodes are positioned at
forearm. EMG electrodes generate the voltage from the
muscles which is then fed into the input port of an ADC
Fore arm position= 00 Fore arm position= 450 Fore arm position= 900
Fore arm position= 1350 Fore arm position= 1800
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Figure 5 Basic testing layout of IPMC actuation through muscles
(Analogto-Digital Converter). Electric field andcurrent are imposed on the two faces of IPMC which is
entered through the IPMC driver circuit. This data isamplified through PXI system along with DAQ
Assistant Express VI in LabVIEW 8.5 software. Thecurrent and voltage analysis of the human flexor carpi
ulnaris and extensor carpi ulnaris muscles is also donethrough oscilloscope.
IV. RESULT AND DISCUSSIONDuring experimentation, the input parameter from
muscle ranging from 5.5 mV is taken throughreferenced single-ended (RSE) signal along with
continuous sampled pulses as shown in Figure 6. Thepulse is amplified with the help of a PXI system. The
amplification factor is 550. The desired output voltagerange is generated through a DAC output port with the
same frequency range. The output signal is connectedto IPMC strip. Due to amplified output voltage from theDAC, an IPMC strip bends in one direction. By
changing the polarity of the signal, the bendingbehavior can be reversed. The input and output
configurations of the IPMC are shown in Figures 7 and8.
Figure 6 EMG placements on fore arm
Figure 7 IPMC configuration before fore arm movement
Figure 8 Bending configuration after fore arm movement
The input flexor and extensor pulses are extracted fromEMG signal which is fed to IPMC and various caseshave been studied as shown in Table-I. It has beenanalyzed that output signal (fed to IPMC) is directlyproportional to flexor and extensor muscles signal whenit is working in either on or off mode.
TABLE-I
THE POWER POLARITY ON BOTH SIDES OF IPMC
The voltage signal behavior is taken from EMG and fedto IPMC for actuation as shown in Figure 9. It is foundthat the trend of IPMC actuation voltage is similar toEMG voltage with amplification factor.
Cases Flexor
muscles
Extensor
muscles
State Polarity
Side A Side B
I Off Off None None None
II Off On Extensor Negative Positive
III On Off Flexor Positive Negative
IV On On None None None
IPMC
IPMC control circuitPXI System
DAQ assistant
ADC/DAC
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0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-6
-4
-2
0
2
4
6x 10
-3
Time (s)
EMG
Voltage(V)
EMG Voltage Vs Time
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-3
-2
-1
0
12
3
Time (s)IPMC
ActuationVolta
ge(V) IPMC Actuation Voltage Vs Time
Figure 9 Different voltage responses with time
The EMG voltage VEMG (t)and IPMC actuation voltageVIPMC actuation(t) equations are respectively given below;
0.1
01 01 01
0
( ) (2 )EMGt
V t V Sin f t =
= + (1)
and0.1
02 02 020( ) (2 )
IPMCactuationtV t V Sin f t
== + (2)
Where, V01 is mean value of EMG voltage (V); f01 is
EMG frequency range (Hz) ; tis time (s) ; 01is phasedifference when signal is taken through EMG (radian);V02is mean value of IPMC actuation voltage (V); f02is
IPMC actuation frequency range (Hz); 02 is phase
difference when signal is given to IPMC (radian). Forfinding the frequency range of each signal, the
experimental datas are taken and solved throughMATLAB curve fitting tool. The numerical values are
V01= 0.0047070.0000415, f01= 3.950.018, 01=
2.8840.175, V02= 2.6110.045, f02= 48.50.65 and
02= -0.040040.03. From these datas, it is found that
EMG frequency range (f01) is similar to simulated dataand IPMC actuation frequency range is 48.50.65 Hzwhich is nearly human muscle frequency range. Afterthese observations, it is understood that IPMC behaves
as an artificial muscle and the fore arm movement (interms of voltage) can be transferred to IPMC throughEMG. Thus, IPMC strip can be applicable as micro
robotic arm in micro manipulation application.
V. CONCLUSIONIn this paper, IPMC control system with a bio-mimeticfunction through EMG is discussed. EMG signals are
taken from forearm and actuation of IPMC is obtainedwhen fore arm moves at different angles. To obtain
larger displacement, the voltage signal is amplified atdifferent forearm positions. An EMG signal is
generated by an intended contraction of muscles in theforearm which provides the actuation to an IPMC. An
IPMC acts as both capacitive and resistive elementactuator that behaves like biological muscle. This
feature can be applied in the field of rehabilitation
technology and micro manipulation.
In future, we will focus on developing well equippedmicro robotic arm system. Human fore arm movementcould be transferred into IPMC based micro robotic arm
in a similar manner. It can be concluded that IPMC
behavior shows replacement of an electro-mechanicalsystem like electric motors in the application field ofmicro manipulation.
ACKNOWLEDGEMENT
The authors are grateful to the Director, CentralMechanical Engineering Research Institute (CMERI),
Durgapur, West Bengal, India for granting thepermission to publish this paper. The project is
financially supported by the Council of Scientific andIndustrial Research, New Delhi, India under eleventh
five year plan on Modular Re-configurable Micro
Manufacturing Systems (MRMMS) for Multi MaterialDesktop Manufacturing Capabilities (NWP-30) at
CMERI. Authors are also thankful to Mr. SouvickChowdhury (Project Assistant, CMERI) for assistance
in this project.
REFERENCES
[1] Y. Bar-Cohen, Focus issue on bio-mimetics using electroactivepolymers as artificial muscles, Bio-inspiration & bio-mimetics,vol. 2, 2007.
[2] Y. Bar-Cohen, Electroactive polymer (EAP) actuators asartificial muscles - reality, potential and challenges, 2 ndEdition, ISBN 0-8194-5297-1, SPIE Press, vol. PM136, March,2004.
[3] K. J. Kim and M. Shahinpoor, A novel method ofmanufacturing three dimensional ionic polymer metal
composites (IPMCs), bio-mimetic sensors, actuators andartificial muscles, Polymer, 43, 2002, pp. 797-802.
[4] S. C. Jou, L. M. Hein, T. Schultz and A. Waibel, Articulatoryfeature classification using surface electromyography, IEEEICASSP, Germany, 2006, pp. 605-608.
[5] S. Natarajan, M. Kazi and G. Muralidharan, Biosignal basedhuman-machine interface for robotic arm, IEEE conference,2002, Available:http://www.mechatroniks.net/index_files/5.pdf
[6] T. Karakostas, N. Berme, M. Parnianpour, W.S. Pease and P.M.Quesada, Muscle activity and the quantification of co-contraction at the knee during walking gait, Summer Bio-
engineering Conference, Florida, June 25-29, 2003, pp.1-2.[7] N. Kunju, N. Kumar, D. Pankaj, A. Dhawan and A. Kumar,
EMG signal analysis for identifying walking patterns ofnormal healthy individuals, Indian Journal of Biomechanics:Special Issue, NCBM, March 7-8, 2009.
[8] T. L. Daley, R. N. Scott, P. A. Parker and D. F. Lovely,Operator performance in myoelectric control of a multifunction
prosthesis stimulator, Journal of Rehabilitation Research andDevelopment, vol. 27, 1990, pp. 920.
[9] B. Hudgins, K. Englehart, P. Parker and R. N. Scott, Amicroprocessor-based multifunction myoelectric controlsystem, Institute of Biomedical Engineering, University of
New Brunswick Fredericton, NB Canada, 1997. Available:http://www.ee.unb.ca/kengleha/papers/ CMBES97
[10] C. M. Light, P. H. Chappell, B. Hudgins and K. Englehart,Intelligent multifunction myoelectric control of hand
189
8/13/2019 Bio-Mimetic Behaviour of IPMC Artificial EMG Signal
5/5
prostheses, Journal of Medical Engineering & Technology,vol. 26, no. 4, July/August, 2002, pp. 139 146.
[11] G. Cheron, J. P. Draye, M. Bourgeiosas and G. Libert, Adynamic neural network identification of electromyography andarm trajectory relationship during complex movements, IEEE
Transactions on Biomedical Engineering, vol. 43, no. 5, 1996,pp. 552-558.
[12] M. Abdallah and B. Zahran, Analysis: signal-step analysis ofsurface myoelectric signal, European Journal of ScientificResearch, ISSN 1450-216X, vol. 26, no. 2, 2009, pp. 298-304.Available: http://www.eurojournals.com/ejsr.htm.
[13] M. R. Ahsan, M. I. Ibrahimy and O. O. Khalifa, EMG signalclassification for human computer interaction: a review,European Journal of Scientific Research, ISSN 1450-216X, vol.33, no. 3, 2009, pp. 480-501, Available:http://www.eurojournals.com/ejsr.htm
[14]N. M. L. Celani, C. M. Soria, E. C. Orosco, F. A. D. Sciascioand M. E. Valentinuzzi, Two-dimensional myoelectric controlof a robotic arm for upper limb amputees, Journal of Physics:Conference Series, 90, 012086, IOP publishing, 2007, pp. 1-8.
[15] S. Kwon and J. Kim, Real-time upper limb motion predictionfrom non invasive biosignals for physical human-machineinteractions Proceedings of the 2009 IEEE InternationalConference on Systems, Man and Cybernetics, San Antonio,TX, USA, October, 2009, pp. 847-852.
[16] G. R. Naik, D. K. Kumar and S. P. Arjunan, Patternclassification of myo-electrical signal during differentmaximum voluntary contractions: a study using BSStechniques, Measurement Science Review, vol. 10, no. 1, 2010,
pp. 1-6.
[17] C. Murphy, N. Campbell, B. Caulfield, T. Ward and C. Deegan,Micro electro mechanical systems based sensor formechanomyography, Biosignal, 2008.
[18] M. J. Lee, S. H. Jung, S. Lee, M. S. Mun and I. Moon, Controlof IPMC-based artificial muscle for myoelectric hand
prosthesis, R&D Project, Ministry of Health & Welfare,Republic of Korea, 2004.
[19] M. Z. Al-Faiz and Y. I. Al-Mashhadany, Human armmovements recognition based on EMG signal, MASAUMJournal of Basic and Applied Sciences, vol. 1, no. 2, September,
2009, pp. 164-171.
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