Comparative Study of EMG based Joint Torque Estimation ANN Models for Arm Rehabilitation Device

Transcription

1 Interntionl Journl of Applied Engineering Reserch ISSN Volume 9, Numer 10 (2014) pp Reserch Indi Pulictions Comprtive Study of EMG sed Joint Torque Estimtion ANN Models for Arm Rehilittion Device M. H. Jli 1,, M. F. Sulim 1,, T. A. Izzuddin 1, c, W. M. Bukhri 1, d nd M. F. Bhrom 1, e 1 Fculty of Electricl Engineering, Universiti Teknikl Mlysi Melk Hng Tuh Jy, Mlcc, Mlysi mohd.hfiz@utem.edu.my, fni@utem.edu.my, c trmizi@utem.edu.my, d ukhri@utem.edu.my, e mohmd.fizl@utem.edu.my Astrct Rehilittion device is used s n exoskeleton for people who hd filure of their lim. Arm rehilittion device my help the reh progrm whom suffered with rm disility. The device used to fcilitte the tsks of the progrm should improve the electricl ctivity in the motor unit nd minimize the mentl effort of the user. Electromyogrphy (EMG) is the techniques to nlyze the presence of electricl ctivity in musculoskeletl systems. The electricl ctivity in muscles of disle person is filed to contrct the muscle for movements. In order to minimize the used of mentl forced for disle ptients, the rehilittion device cn e utilize y nlyzing the surfce EMG signl of norml people tht cn e implemented to the device. The ojective of this work is to compre the performnce of the joint torque estimtion model from the muscle EMG signl to torque for motor control of the rm rehilittion device using Artificil Neurl Network (ANN) technique. The EMG signl is collected from Biceps Brchii muscles to estimte the elow joint torque. A two lyer feed-forwrd network is trined using Bck Propgtion Neurl Network (BPNN) to model the EMG signl to torque vlue. The comprison etween two ANN models is mde to oserve the performnce difference etween these models. The experimentl results show tht ANN model with doule input nodes hs etter performnce result in term of Men Squred Error (MSE) nd Regression (R) which is crucilly importnt to represent EMG-torque reltionship for rm rehilittion device control.

2 1290 M. H. Jli et l Keywords - Electromyogrphy, Artificil Neurl Network, Arm Rehilittion Device, Joint Torque Estimtion, Exoskeleton 1. Introduction Humn support system is endoskeleton. Endoskeleton plys role s frmework of the ody which is one. Our dily movements re fully depends on the functionlity of our complex systems in the ody. The disility one or more of the systems in our ody will reduce our physicl movements. The ssistive device is need for reh s n exoskeleton. The functionlity of the rehilittion device hs to smooth s the physicl movement of norml humn. The rehilittion progrms provide the suitle progrm for conducting the nerve nd stimulte the muscles. People who hve temporry physicl disility hve the chnces to recover. Nowdys, rehilittion progrm re using exoskeleton device in their tsks. The functionlity of exoskeleton depends on muscle contrction. Electromyogrm studies help to fcilitte the effectiveness of the rehilittion device y nlysing the signl trnsmitted from the muscle. The technique of mesuring electricl ctivity tht produced from the muscles during rest or contrctions known s electromyogrphy (EMG). The electric signl genertes from the rin nd sends to the muscles vi motor neuron. The EMG my detect the dysfunctionl of the muscles or filure in signl trnsmission from nerve to muscle. The filure of sending the electricl signl from the rin requires electricl stimultion from the externl source to muscles. Electrodes re used for signl detection of electricl ctivity in muscles. The study of this electricl ctivity is importnt for comintion of electromyogrm nd rehilittion device. The rehilittion device is tool tht used to help the movements for dily life ctivities of the ptients who suffer from the filure of muscle contrctions, due to the filure of the muscles contrctions the movements is limited. The ility of the ptients to do the tsks in the rehilittion progrms need to e mesured. The rehilittion progrms hve to ssure whether the tsks will cuse effective or ring hrm to the ptients [1]. Historiclly, the rehilittion tsks hve een voided due to elief tht it would increse spsticity [2]. In this reserch, the nlysis of the dt will e focusing on upper lim muscles contrction consisting of iceps muscles only. The experiment is limited to the certin of upper lim movements tht use in trining. EMG is division of io signl; the io signl nlysis is the most complex nlysis. Thus, the signl nlysis is complicted process tht hs to e through mny phses of nlysis [3]. EMG is used s control signl for the rm rehilittion device. A system needs model to estimte reltionship etween EMG nd torque [4]. EMG signl sed control could increse the socil cceptnce of the disled nd ged people y

3 Comprtive Study of EMG sed Joint Torque Estimtion ANN Models 1291 improving their qulity of life [5]. The joint torque is estimted from EMG signls using Artificil Neurl Network [6]. The BPNN is used to find solution for EMGjoint torque mpping. The EMG signl of the iceps rchii muscle ct s the input of the ANN model whiles the desired torque ct s the idel output of the model. Hence the EMG signls considered the intent of the system while the joint torque is the controlled vrile for the rm rehilittion device [7]. There re severl work tht hs pplied BPNN for modeling the muscle ctivity of nkle to joint torque reltionship. It is done y pproximting the force from the EMG signl under sttic conditions [7]. The network is evluted sed on the est liner regression etween the ctul joint torque nd the estimted joint torque [4]. 2. Methods Figure 1 shows lock digrm of our reserch tht consists of two mjor phses. First phse is EMG dt processing nd desired torque determintion. Second phse is the ANN construction nd testing. The dt collection from the first phse is used to vlidte nd tech the ANN lgorithm in second phse. Figure 1. Reserch methods 2.1 Experimentl Setup Implementtion of rm rehilittion device sed on movement is recorded from the EMG signl of helthy sujects. From the humn ntomy studies, different ngle movements of upper lim with elow s the reference is depends on reltion of gonist nd ntgonist. In this study is focusing on the ehviour of iceps muscle s gonist nd the triceps s the ntgonist respectively. Muscle tht involved in this movement is iceps nd triceps, however in this study to understnd the electricl ctivity during muscle contrction, the iceps is the only muscle tht tking into ccount. The movements rnges in etween position of rm flexion until rm fully extend. The environment is in room with low lighting especilly the fluorescent light, ny electromgnetic devices is wy from the experiment equipment nd the

4 1292 M. H. Jli et l environment is in silent room. Then, the experimentl is set up with the suject sit on the chir while the hnd is on the tle. The suject hs to complete the tsk of lift up the dumell with kg of weight in Figure 2() for 5 times. Normlly, the ppernce of EMG signl is chos nd noisy depends on the type of electrodes lso the noise fctor. To simplify the difference of mplitude response for the motion, the dumell is functioned to mplify the mplitude in nlysing the electricl ctivity during rest nd contrct. The rehilittion devices (white in color on Figure 2()) helps to keep the position of the elow joint nd the wrist joint in line. Mostly, the EMG signl is otined fter severl trils of the movements. These movements re specified from ngle of 0 (rm in rest position), up to 120 (rm is fully flexion). Dt ws collected from two sujects y 5 repetitions of ech flexion movements [8]. Figure 2. Suject is set-up with rm rehilittion ssistive device for experiment (), Simultion of suject s to lift up the dumell kg of weight (). Prior of dt collection process, the skin needs preprtion. The preprtion of skin is ruled y the Surfce Electromyogrphy for the Non-Invsive Assessment of Muscles (SENIAM) procedure for non-invsive methods. The suject s skin hs to e shved y using smll electricl shver nd clened with sterile lcohol sws sturted with 70% Isopropyl Alcohol. This step is to e tken for minimizing the noise nd to hve good contct with the electrodes of the skin y decresing the impednce of the skin. The skin hs to e clen from ny contmintion of ody oil, ody slt, hir nd the ded cells. The preprtion of skin cn e done y wiping the lcohol sw into the re of skin tht electrode plcement to e pplied. The plcements of the electrode hve to e t the elly of the muscles not in the tendon or

5 Comprtive Study of EMG sed Joint Torque Estimtion ANN Models 1293 motor unit. This ensured the detecting surfce intersects most of the sme muscle on suject s in Figure 3() t the iceps rchii, nd s result, n improved superimposed signl is oserved. Reference electrode hs to e t the one s the ground, for this experiment it plced t elow joint s shown in Figure 3(). These electrodes re connected to the comintion of hrdwre Olimex EKG-EMG-PA nd Arduino Meg for dt collection. Figure 3. The iceps rchii muscles for electrode positions (), The electrode plcements on suject skin () 2.2 EMG dt processing Figure 4 shows the EMG dt processing lock digrm. After otined stisfctory EMG signl s shown in Figure 5(), Fst Fourier Trnsform (FFT) is performed to the signl to nlyses the frequency content of the signl. The EMG signl is rek into its frequency component nd it is presented s function of proility of their occurrence. In order to oserve the vrition of signl in different frequency components, the FFT signl is represented y Power Spectrl Density (PSD). From the PSD we cn descries how the signl energy or power is distriuted cross frequency. Figure 5() shows tht most of the power is in the rnge of elow 10Hz, therefore the EMG signl should e filtered in the rnge of ove 10Hz s cut off frequency for low pss filtered. After decide the cut off frequency for filtering, the DC offset of the EMG signl is removed nd is rectified to otin its solute vlue s shown in Figure 5(c). Finlly, the signl ws smoothed nd normlized pssing it through 5 th order Butterworth type low-pss filter with cut off frequency 10Hz nd the smooth signl is illustrte in Figure 5(d) [8][9].

6 1294 M. H. Jli et l Figure 4. EMG dt processing c d Figure 5. Rw EMG signl (), Power Spectrl Density (), Rectified signl (c), Smooth signl (d) 2.3 Desired Torque Desired torque of the elow joint is used s trget dt for our ANN techniques s well s ct s output signl for muscle. The dt is collected throughout the ngle from 0 0 to ngle with increment of ech step to lign with the smple numer of EMG signl. Figure 6() shows the rm rehilittion device position for torque clcultion. The desired torque for elow joint is determined y pplying stndrd torque eqution: -

7 Comprtive Study of EMG sed Joint Torque Estimtion ANN Models 1295 τ = (r lod F lo od + r rm F r rm) cos θ (1) Where r lod is distnce from the elow joint to the lod, F lod is force due to lod, F rm is the force due to the mss of the lever rm nd r rm is evenly distriuted distnce of mss of rm distnce which is hlf of r lod. The ngle θ etween r nd F is drwn from the sme origin. A pplied lod is 5 pounds (2.268kg) dumell nd the distnce from the elow joint is 0..25m while the mss of the lever rm is 0.1kg. Figure 6() shows the desired torque chrcteristic for the elow joint. Figure 6. Arm rehilittion device position (), Desired torque chrcteristic () 2.4 Artificil Neurl Network (ANN) Model ANNN is computing prdigm tht is loosely modeled fter corticl structures of the rin. It consists of interconnected processing elements clled nodes or neuron tht work together to produce n output function. It cple to mp dt set of numeric inputs with set of numeric outputs. It is lso the most widely pplied trining network whichh hs input lyer, hidden lyer nd output lyer. The neurons on ech lyer need to e considered crefully to produce high ccurcy network. The numer of hidden neurons could ffect the performnce of the network. The network performnce not lwys een improved if the hidden lyer nd its neurons is incresed [4]. Therefore the numer of hidden neurons is tested to chievee the optimized network. However theree is constrint in determining the numer of neurons. If the numers of hidden neurons is too lrge, the network requires more memory nd the network ecome more complicted while if the numer of hidden neurons is too smll, the network would fce difficulty to djust the weigh properly nd could cuse over fitting which is prolem wheree the network cnnot e generlized with slightly different inputs [10]

8 1296 M. H. Jli et l The input signl is propgted forwrd through network lyer using ck propgtion lgorithm. An rry of predetermined input is compred with the desired output response to compute the vlue of error function. This error is propgted ck through the network in opposite direction of synptic connections. This will djust the synptic weight so tht the ctul response vlue of the network moved closer to the desired response [11]. BPNN hs two-lyer feed-forwrd network with hidden neurons nd liner output neurons. The function used in the hidden lyer of network is sigmoid function tht genertes vlues in rnge of -1 to 1 [8]. There re lyers of hidden processing units in etween the input nd output neurons. For ech epoch of dt presented to the neurl network, the weights (connections etween the neurons) nd ises re updted in the connections to the output, nd the lerned error etween the predicted nd expected output, the delts, is propgted ck through the network [12]. A Lvenerg-Mrqurdt trining ck propgtion lgorithm is implemented for this work to model the EMG to torque signl. Two ANN models is considered to compre the performnce of ech model. ANN model 1 consist of input lyer with single node which is denoted s tht represent EMG signl from iceps while ANN model 2 hs dditionl node t the input lyer which denoted s t tht represent the movement time which ct s trining dt. The output lyer hs only one node which is denoted s τ tht represents the desired torque ct s trget dt. Tle I descrie the difference etween the models nd the network structure of ech model is shown in Figure 7[4]. The network ws trined using 1839 sets of EMG dt for rm flexion motion from 0 0 ngles to ngle. It lso hs output dt which is torque of correspondent rm motion. The trining process ws itertively djusted to minimize the error nd incresed the rte of network performnce [10]. MATLAB softwre is used to construct the BPNN network. The network requires dt for lerning nd testing in order to determine the weights ech node uses. The trining hs een done y dividing the input dt of 70% for trining, 15% for vlidtion nd 15% for testing [9]. The network will e trined until the following condition fulfilled efore it stop [10]: - rech mximum numer of epochs grdient performnce ecme less thn the minimum grdient vlidtion performnce incresed more thn the mximum fil times since the lst decresed one

9 Comprtive Study of EMG sed Joint Torque Estimtion ANN Models 1297 Tle 1. ANN model description ANN model Model 1 Model 2 No. of input node 1 2 Node 1 EMG dt, EMG dt, Node 2 Movement time, t - Hidden neurons Output node 10 Desired torque, τ 10 Desired torque, τ The performnce evlution of the network is sed on the Men Squred Error (MSE) of the trining dt nd Regression (R) etween the trget outputs nd the network outputs s well s the chrcteristics of the trining, vlidtion, nd testing errors. The network is considered hs the est performnce if it hs lowest MSE nd highest R while exhiit similr error chrcteristics mong the trining, vlidtion nd testing. However even if the MSE shows very good result ut the vlidtion nd testing vry gretly during the trining process, the network structure is still considered unstisfctory ecuse the network is not generlized. Therefore further tuning nd trining need to e conducted in order to improve the network performnce [10]. Figure 7. () Model 1, () Model 2 3. Experimentl Results & Discussion In order to optimize the network performnce, different numer of hidden neurons is simulted for severl times until chieved the stisfctory results [10].The network is trined using Lvemerg-Mrqurdt lgorithm nd the performnce of the network is mesured using MSE nd R. The est vlidtion performnce of Model 1 is t epoch 70 s shown in Figure 8() while e-10 t epoch 1000 for Model 2 in Figure 8(). Thus it is cler tht Model 2 provide etter result in term of MSE compre to Model 1. However the stop epoch of Model 2 is higher thn Model 1.

10 1298 M. H. Jli et l Both results show tht the MSE hs decresed rpidly long the epochs during trining. The regression for Model 1 is while for Model 2. Figure 9() shows tht Model 2 produces etter curve fitness for trining, test nd vlidtion dt round compre to Model 1 s shown in Figure 9() [9][13]. Figure 8. () Best vlidtion performnce Model 1, () Best vlidtion performnce Model 2 Error sizes re well distriuted if most error pproching zero vlues tht mke the trined model performs etter. Figure 10() shows tht Model 1 hs mximum instnce round 450 of MSE distriuted round the zero line of the error histogrm while Figure 10() shows tht Model 2 hs mximum instnce round 700 of MSE distriuted round zero line [13]. This histogrm could e interpreted with error fluctution digrm long the zero vlues s shown in Figure 11(). It descried tht Model 1 exhiit lrge vlue of fluctution from the zero vlues compred to Model 2. Fig. 11() shows comprison etween prediction output from the trined network nd trget torque for Model 1 nd Model 2. The prediction output for Model 2 hs solutely spectculr greement with the trget output compre to Model 1tht hs only firly good greement with the chrcteristics with the trget dt [4]. Tle 2 summrize the performnce result of oth model.

11 Comprtive Study of EMG sed Joint Torque Estimtion ANN Models 1299 Figure 9. () Regression of the trined Model 1, () Regression of the trined Model 2 Figure 10. () Error histogrm of the trined Model 1, () Error histogrm of the trined Model

12 1300 M. H. Jli et l Figure.11 () Error fluctution long zero line, () Trget vs trined network output Tle 2. Summry of the result BPNN Structure Model 1 Model 2 Stop Epochs Regression MSE e-10 Time Elpsed (Seconds) Conclusions Bsed on the result, it cn e concluded tht the ANN Model 2 hs etter performnce in term of MSE nd R compre to ANN Model 1. Model 2 hs super low MSE vlue s well s perfect result for R. However the stop epochs nd time elpsed for Model 2 is worse thn Model 1. The computtionl time for Model 2 consume longer time which is 1.00 seconds compre to Model 1 tht required only 0.04 seconds. Therefore there is trdeoff etween these models. In order to improve the Model 2 stop epochs nd time elpsed, evolution neurl network trining lgorithm such s genetic lgorithm nd prticle swrm optimiztion cn e implemented to produce good men squred error nd regression performnce result while reducing the computtionl time. Model 2 is considering good performnce s it shows tht this ANN model cn well represent the reltionship etween EMG signls nd elow joint torque. Hence this model cn e used for motor torque control of the rm rehilittion devices. Acknowledgements The uthors would like to thnks Universiti Teknikl Mlysi Melk (UTeM) nd Ministry of Eduction, Mlysi for the finncil supports given through Reserch Grnt.

13 Comprtive Study of EMG sed Joint Torque Estimtion ANN Models 1301 References [1] S. D. C. G. C. Louise Ad.: Strengthening Interventions Increse Strength nd Improve Activity After Stroke: A Systemtic Review. Austrlin Journl of Physiotherpy, vol. 52, pp , [2] B. B.: Adult Hemiplegi: Evlution nd tretment..oxford, Butterworth- Heinemnn., [3] J. Muthuswmy.: Biomedicl Signl Anlysis in Stndrd Hndook Of Biomedicl Engineering And Design. Mc-Grw Hill, 2004, pp [4] Li Dpeng, Zhng Yxiong.: Artificil Neurl Network Prediction of Angle Bsed on Surfce Electromyogrphy. Interntionl Conference on Control, Automtion nd Systems Engineering (CASE).pp. 1-3, [5] M. B. I. Rez, M. S. Hussin, nd F. Mohd-Ysin.: Techniques of EMG Signl Anlysis: Detection, Processing, Clssifiction nd Applictions. Biologicl Procedures Online, vol. 8, pp , [6] Morit, S. Kondo, T. Ito, K.: Estimtion of Forerm Movement from EMG Signl nd Appliction to Prosthetic Hnd Control, IEEE Interntionl Conference on Rootics nd Automtion(ICRA).vol.4.pp [7] Kent, L.M. Siegler, S. Guez, A. ; Freedmn, W..: Modelling Of Muscle EMG To Torque By The Neurl Network Model Of Bckpropgtion. IEEE Interntionl Conference of the Engineering in Medicine nd Biology Society, Proceedings of the Twelfth Annul. Pp [8] Fvieiro, G.W. Blinot, A. Brreto, M.M.G.: Decoding Arm Movements y Myoeletric Signls nd Artificil Neurl Networks. Conference of Biosignls nd Biorootics(BRC). pp [9] Neem, U.J. Adullh, A.A. ; Cihu Xiong.: Estimting humn rm's muscle force using Artificil Neurl Network. IEEE Interntionl Symposium on Medicl Mesurements nd Applictions Proceedings (MeMeA), pp [10] Ahsn, M.R. ; Irhimy, M.I. ; Khlif, O.O. EMG Motion Pttern Clssifiction through Design nd Optimiztion of Neurl Network. Interntionl Conference on Biomedicl Engineering (ICoBE), pp [11] B. Hudgins, P. Prker, nd R. Scott.: A new strtegy for multifunction myoelectric control. IEEE Trns. Biomed. Eng., vol. 40, no. 1, pp , Jn [12] Mrs, P, Chen JR, Nmir, R Lerning Algorithms.: Theory nd pplictions in signl processing, control nd communictions. CRC [13] Supeni, E. E. nd Eprchchi, J. A. nd Islm, M. M. nd Lu, K. T. (2013) Development of rtificil neurl network model in predicting performnce of the smrt wind turine lde. In: 3rd Mlysin Postgrdute Conference (MPC 2013), 4-5 Jul 2013, Sydney, Austrli.

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