A NEW REAL-TIME FUZZY INFERENCE SYSTEM FOR EMG PATTERN RECOGNITION SUITABLE FOR HAND PROSTHESIS CONTROL. M. Khezri 1, M. Jahed 1

Transcription

1 A NEW REAL-TIME FUZZY INFERENCE SYSTEM FOR EMG PATTERN RECOGNITION SUITABLE FOR HAND PROSTHESIS CONTROL M. Khezr 1, M. Jahed 1 1 Sharf Unversty of Technology, Electrcal Engneerng Department, Bomedcal Engneerng Group, Tehran, Iran Introducton Electromyography (EMG) s the study of muscle functon through the nqury of the electrcal sgnals that the muscles emanate. EMG sgnal collected form surface of the skn can be used n dfferent applcatons such as recognzng patterns of hand prosthess movements. We used ntellgent approach based on artfcal neural network (ANN) and Fuzzy nference system (FIS) wth a real-tme learnng method to dentfy moton commands. For ths purpose and to consder the effect of user evaluaton on recognzng hand movements, vson feedback has been appled to ncrease the capablty of ths system. The myoelectrc sgnals consdered to classfy were sx movements of the hand. Features chosen for EMG sgnal were n tme-frequency doman. These features were local maxma and zero crossng of the wavelet transform (WT) coeffcents. In ths work we evaluated the capablty of an EMG pattern recognton system usng ANN and FIS as classfer wth a real-tme learnng method. Our results demonstrated that the utlzed real-tme FIS approach wth a 95% average accuracy was superor to the prevously ntroduced ANN real-tme scheme [4]. The myoelectrc sgnal, recorded at the skn surface (SEMG), has been used for many dverse applcatons. The EMG sgnal gves us nformaton about the neuromuscular actvty from whch t orgnates, and ths has been fundamental to ts use n clncal dagnoss, and as a source for control of assstve devces. It has been proposed that the EMG sgnals from upper lmb musculature can be used to dentfy moton commands for the control of an externally powered prosthess hand. EMG s a complcated sgnal nfluenced by varous factors such as physologcal and anatomcal propertes, characterstcs of nstrumentaton and dffers from one person to another. In earler studes, the system learned the characterstcs of EMG sgnal n an offlne manner whch could not adust ts nner states correspond to operator s varaton n real-tme [1]-[3]. To decrease these effects on EMG pattern recognton system and elmnatng dependence on ndvdual subect, real-tme methods to tran ths system were ntroduced [4]. Accurate feature extracton from EMG sgnals s the man kernel of classfcaton systems n both real-tme and offlne systems and s essental to the moton command dentfcaton. Prevous works have shown that tme-frequency features present better results n EMG pattern recognton applcaton [3]. Ths s due to the effect of combnng tme doman and frequency analyses whch yelds a potentally more revealng pcture of the temporal localzaton of a sgnal s spectral characterstcs. In ths study, we propose a new real-tme method sutable for hand prosthess control. In ths non-nvasve system, two channel surface mounted electrodes are utlzed. We use tme-frequency representaton (TFR) specfcally local extrema and zero crossng of the wavelet transform (WT). After extractng the sutable features of sgnal, we employ a classfer to dentfy each movement wth a hgh degree of correctness. In ths work we use two ntellgent schemes, namely ANN and FIS as classfer to compare the ablty of our proposed approach to the prevously ntroduced real-tme learnng method [4].

2 1. Real-tme scheme of EMG pattern recognton system The real-tme scheme of EMG pattern recognton system used n ths work s shown n Fgure 1. The fve maor components are, EMG preprocessng and condtonng, feature extracton, Dmensonalty reducton, classfer (pattern recognzer) and traner unts. Fgure 1 - Conventonal Scheme for hand prosthess control The goal of preprocessng step s to prepare and amplfy the sgnal for the subsequent steps and to reduce nose artfacts. Sx movements of hands have been consdered and EMG sgnal for each movement has been extracted, namely hand openng and closng, pnch, thumb flexon and wrst radal flexon and extenson. These movements are shown n Fgure 2. Durng the feature extracton step, EMG sgnal s processed n order to emphass relevant structure. In ths phase we use tme-frequency features. These features are local extrema and zero crossng of wavelet transform coeffcents. For dscrete wavelet transform (DWT) defned by, Where ψ () t 1 t n.2 DW T x (, n) = x ( t ) ψ ( ) dt 2 2, s the mother wavelet. The analyss determnes the correlaton of the sgnal wth shfted (by n. 2 ) and scaled (by 2 ) versons of the mother wavelet, where n,. Therefore, wavelet analyss s the breakng up of a sgnal nto shfted and scaled versons of the orgnal (or mother) wavelet and the wavelet coeffcents may consttute an effectve feature set. However there s a fundamental drawback for the DWT whch s lack of shft nvarance. If the sgnal to be analyzed s shfted, the coeffcents of wavelet transform vary n a complcated manner. Ths matter may present a sgnfcant problem n the pattern recognton system. To overcome ths problem, we utlzed shft nvarant features of DWT, namely local extrema and zero crossng (ZC) [3]. Next, we used dmensonalty reducton to smplfy the task of classfer. For ths part of the system, prncple component analyss (PCA) was ntroduced. The role of dmensonalty reducton s to retan nformaton that s mportant for class dscrmnaton and dscard rrelevant nformaton. In the next step, we determned the type of moton commands sutable to control prosthess hand by utlzng two dfferent real-tme ntellgent classfer approaches, namely ANN and FIS. Fnally, n the last secton of ths system called traner unt, system learned the relaton between actual EMG patterns and generated control commands and furthermore adapted to the operator s characterstcs (Ths part does not exst n off-lne learnng method). (1)

3 Fgure 2 - Representaton of sx dstnct hand movements 2. EMG pattern classfcaton usng ntellgent classfers There are several technques for classfer but n ths work we focused on two ntellgent technques that have generalzaton property, namely ANN and FIS [5,6, and 7]. One of the smplest and wdely used ANN for EMG pattern classfcaton s multlayer perceptron (MLP). Ths network conssts of a set of nputs, number of hdden layer and an output layer. Each nput connected through a set of weghts to hdden layer. Also each neuron n hdden layer connected to output layer. Hdden and output neurons have a transfer functon. In ths network hardlmtter, logstc sgmod and hyperbolc tangent sgmod are used as a transfer functon. The connecton weghts between and neuron threshold are vared by back propagaton (BP) algorthm. Let a tranng example denoted by[ x ( n), d( n )], wth the nput vector x ( n ) and the desred response vector d( n). Wth the use of logstc for the sgmod non-lnearty, the output of neuron n the layer l s gven by, ( l ) 1 y ( n) = (2) p ( l 1) ( l 2) 1 exp( w ( n) y ( n)) + = 0 ( where w l ) ( n) s the weght of neuron n layer l that s connected to to neuron n layer (l 1). For neuron n the frst and last layer (.e. nput layer l = 1and output layer, l = L ) We have: () 1 ( L y ( n) = x ( n), y ) ( n) = o ( n). Where x ( n ) and o ( n) are the th elements of nput vector and output layer respectvely. FIS s another classfer that we consder n ths study. FIS can emulate human decson makng by usng fuzzy rules n the form of IF-THEN [5]. The fuzzy systems at frst fuzzfy nputs nto membershp degrees of fuzzy sets then nfer by fuzzy logc through rules whch usually come from experence. The output of FIS system defned by, n MF ( x ). ( y ) L = 1 y = L n = 1 MF ( x ) = 1 = 1 Where MF s the th membershp functon n th rule also x and y are th nput and the output of th rule. The fuzzy system used n the present study s Sugeno type. Ths system wth weght average defuzffer, product nference rule, non-sngleton and Gaussan membershp functon can be descrbed by equaton Role of traner unt n real-tme pattern recognton In real-tme learnng method for the applcaton of pattern recognton there s a mechansm for evaluatng the system s performance by the operator. In the case of offlne methods prevously mentoned, there s no need for consderng the tme as a cost functon. (3)

4 The real-tme method must be able to evaluate the accuracy of system n recognzng hand movement that performed by operator. The traner unt makes tranng data whch contans a teacher sgnal from the operator and reduced features from dmensonalty reducton unt. Next, ths tranng data s fed to the pattern recognton unt. When the traner unt receves the teacher sgnal ({ T = 1, 2,..., n} where n s the number of movement or EMG sgnal) from operator, t creates the teachng vector as, t = ( t1, t2,..., t n ), t 1, f = T = (4) 0, otherwse The traner unt creates the reduced feature set (p) wth teachng data (t) n the form of Γ( pt, ) and sends ths teachng vector to pattern recognton unt. The traner unt updates the state of pattern recognton unt n the nterval that control command from EMG sgnal s generated. Ths process s contnued untl the control command (o) of output n compare wth (t) s not correct or RMS value that defned by formula (5) exceeds a constant value 0.1: 4. Expermental results: 1 n n = 1 RMS = o d 2 ( ) (5) To mplement the real-tme EMG recognton system, we used a PC wth Pentum4 processor. The proposed controller was mplemented by MATLAB software. A key on the keyboard was pressed as an nterface to send the teacher sgnal to the traner unt. A computer graphc smulated the workng of a prosthess hand. The operator watched the montor and sent the moton command by pressng the correspondng keys. He sent new moton command when he udged that the controller has learned the prevous command moton completely. Therefore the subect watched the montor and also re-sent a teacher sgnal for the past moton f t was not performed correctly. Fgure 3 shows the scheme of ths process. Constructng of the feature set by DWT, called for the verfcaton of the parameters whch affected t. For ths reason, the canddate parameters were consdered and the accuracy of the EMG pattern recognton system based on these parameters was calculated. As a result best representatve parameters were selected. Specfcally, to acqure these results we consdered ampltude of DWT as a feature and compared the results by mplementng ANN and FIS schemes. Fnally to choose most sutable DWT, a collecton of mother wavelets were compared and an approprate depth of decomposton was chosen. The selecton of the canddate mother wavelet for EMG pattern recognton was determned emprcally where the DWT decomposton was termnated pror to a full decomposton when necessary. Fgure 3 - An expermental setup for a real-tme EMG pattern recognton system

5 For a sgnal wth length of N samples, the maxmum depth of decomposton s J = log N 2. The EMG sgnal n ths work has 500 samples, thus the maxmum depth of decomposton wll be 9. Fgure 4 shows the effect of mother wavelet and depth of decomposton on EMG pattern recognton system. To obtan these results, we used dfferent types of mother wavelet such as haar, daubeches, symlet, coflet and borthogonal. The results ndcated that Haar was the most sutable mother wavelet wth 9 level of decomposton provdng the best performance to recognze EMG patterns. Fgure 4 - Selecton of best WT and performance After extractng the features, the accuracy of ANN and FIS based systems were evaluated and compared. Also the effectveness of the real-tme method was compared to the offlne approach. Table-1 depcts the results of mplemented EMG pattern recognton system usng sx hand movements prevously noted. The results ndcated FIS superor performance as compared to ANN. Also the realtme method for learnng EMG pattern recognton system (because of consderng the user evaluaton) was shown to produce better results. Hence by usng FIS real-tme learnng method, we were able to successfully dscrmnate among sx descrptve and dstnct hand movements wth an average accuracy of 95%. Table 1 - Comparng acqured results by ANN and FIS Usng real-tme and offlne learnng method (%) Classfers Hand open Hand close Pnch Thu-flex Wr-exten Wr-flex Average Off lne ANN Learnng FIS Real-tme ANN Learnng FIS Concluson In ths work we desgned an EMG pattern recognton system to dscrmnate among sx movements of hand. For desgnng ths system we used two tme features of WT, namely local extrema and zero crossng. To create the feature set we consdered the best parameters of WT, for whch Haar was chosen as the best mother wavelet and nne level of decomposton were exerted for best performance. Also n order to consder the effect of real-tme learnng on recognzng sx dstnct hand movements, operator vson feedback was utlzed. Ths study showed that FIS real-tme learnng method can provde acceptable results for a surface EMG pattern recognton system sutable for hand prosthess control.

6 References 1. Hudgns.B, Parker.P and Scott.R.N, A new strategy for multfuncton myoelectrc control, IEEE Trans. Bomed. Eng., vol. 40, no. 1, pp , Jan Englehart.K and Hudgns.B, A robust, real-tme control scheme for multfuncton myoelectrc control, IEEE Trans. Bomed. Eng., vol. 50, no. 7, pp , Jul Englehart.K, Hudgns.B and Parker.P.A, A wavelet-based contnuous classfcaton scheme for multfuncton myoelectrc control, IEEE Trans. Bomed. Eng. vol. 48, no. 3, pp , Mar Nshkawa.D, YU.W, Yoko.H, Kakazu.Y,EMG Prosthetc Hand Controller Dscrmnatng Ten Motons usng Real-Tme Learnng Method. Proceedng of the 1999 IEEE/ RS Internatonal conference on Intellgent Robots and Systems. 5. Zadeh.L.A, Outlne of a new approach to analyss of complex systems and decson processes, IEEE Trans. Syst. Man Cybern., vol. SMC3, pp , Chan.F.H.Y, Yang.Y.S, Lam.F.K, Zhang.Y.T, and Parker.P.A, Fuzzy EMG classfcaton for prosthess control, IEEE Trans. Rehabl. Eng.vol.8, no.3, pp , sep B.Karlk, M. O. Tokh, and M. Alc, A fuzzy clusterng neural network archtecture for multfuncton upper-lmb prosthess, IEEE Trans. Bomed. Eng., vol. 50, no. 11, pp , Nov