ࡱ> [ Sbjbj 'ΐΐK { { { "#{ F0#J%%%%(?)<{) IFKFKFKFKFKFKFHxKKF)](^())KF%%p`F---)R%%?R-)IF--;<e"=% r濾${ #*=?vF0F=K*rK,=K= ))-)))))KFKFY,r)))F))))K))))))))) : Artificial Neural Network based Body Posture Classification from EMG signal analysis 1Rajesh Kumar Tripathy, 2Ashutosh Acharya, 2Sumit Kumar Choudhary, 3Santosh Kumar Sahoo 1Department of Biomedical Engineering, NIT Rourkela, India 2Department of Electronics Engineering, SIT, Bhubaneswar, India 3Department of Electronics Engineering, GITA, Bhubaneswar, India Abstract- This paper deals with the body posture Classification from EMG signal analysis using artificial neural network (ANN). The various statistical features extracted from each EMG signal corresponding to different muscles associated with the different body postures are framed using LABVIEW software. Further-more, these features are taken as the input towards the ANN classifier and thus the corresponding output for the respective classifier predicts the postures like Bowing, Handshaking, and Hugging. The performance of the classifier is determined by the classification rate (CR). The outcome of result indicates that the CR of Multilayer Feed Forward Neural Network (MFNN) type of ANN is rounded up to a percentage of 71.02%. Keywords- EMG, statistical features, LABVIEW,ANN, MFNN, CR 1. Introduction Electromyography(EMG) is a technique for recording the electrical activity of muscles in our body  ADDIN EN.CITE Ahsan20118[1]8810Ahsan, M. R.Ibrahimy, M. I.Khalifa, O. O.Hand motion detection from EMG signals by using ANN based classifier for human computer interactionModeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference onModeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on1-6Artificial neural networksElectromyographyFeature extractionNeuronsNoiseSupport vector machine classificationTrainingbackpropagationgesture recognitionhuman computer interactionmedical signal processingneural netssignal classificationANN based classifierBP networkEMG signalHCI systemLevenberg-Marquardt training algorithmartificial neural networkback-propagation networkelectrical potentialhand motion detectionmuscle cellstime-frequency based feature setsBack-PropagationDiscrete Wavelet Transform201119-21 April 201110.1109/icmsao.2011.5775536[ HYPERLINK \l "_ENREF_1" \o "Ahsan, 2011 #8" 1].This operation is performed using aninstrument known as electromyograph. The process of recording the signals by electromyograph instrument is called as anelectromyogram. An electromyograph detects the electrical activity generated by musclecells when these cells are mechanically activated  ADDIN EN.CITE  ADDIN EN.CITE.DATA [ HYPERLINK \l "_ENREF_2" \o "Yu, 2007 #9" 2]. The signals can further be analyzed to detect medical abnormalities, activation level in order to analyze certainvarious mechanical activities of human movement. In this present study the EMG signals are taken from different muscles with respect to different body postures at different time. The muscles are Right bicep, Right Triceps, Left Bicep and Left Triceps. For handling various complexities and non-linear problems Artificial Neural Networks (ANNs) has gained lot of interest towards this aspect ADDIN EN.CITE Ayrulu-Erdem20116[3]6617Ayrulu-Erdem, B.Barshan, B.Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, 06800 Ankara, Turkey. ebirsel@gmail.comLeg motion classification with artificial neural networks using wavelet-based features of gyroscope signalsSensors (Basel)Sensors (Basel)1721-431122012/02/10AlgorithmsHumansLeg/*physiology*Motion*Neural Networks (Computer)Signal Processing, Computer-Assisted/*instrumentation*Wavelet Analysis20111424-8220 (Electronic) 1424-8220 (Linking)22319378Research Support, Non-U.S. Gov'thttp://www.ncbi.nlm.nih.gov/pubmed/22319378327401510.3390/s110201721eng[ HYPERLINK \l "_ENREF_3" \o "Ayrulu-Erdem, 2011 #6" 3]. These are massively parallel-interconnected networks of simple elements intended to interact with the real world as the same way as that of biological nervous system of human body. ANNs offers an unusual scheme based on the programming and exhibit higher computing speeds compared to other methods like fuzzy rule based approach ADDIN EN.CITE  ADDIN EN.CITE.DATA [ HYPERLINK \l "_ENREF_4" \o "Yang, 2012 #7" 4]. ANN is characterized by their topology, that is, the number of interconnections, the node characteristics which are classified by the type of nonlinear elements used and the kind of learning rules employed. ADDIN EN.CITE Zhou201010[5]101017Zhou, MuXu, YubinTang, LiMultilayer ANN indoor location system with area division in WLAN environmentSystems Engineering and Electronics, Journal ofSystems Engineering and Electronics, Journal of914-926215AccuracyArtificial neural networksEstimationNonhomogeneous mediaSignal to noise ratioTrainingWireless LANartificial neural networkindoor locationmulti-modemultilayer structurerelativity201010.3969/j.issn.1004-4132.2010.05.028[ HYPERLINK \l "_ENREF_5" \o "Zhou, 2010 #10" 5] The ANN is composed of Processing Elements called neurons, which are arranged in layers namely input, hidden and output layers. In this research we use LABVIEW based statistical feature extraction technique to extract the features from each EMG signal corresponding to different body postures. Then, we implement the MFNN as classifier to classify these postures as bowing, clapping and handshaking. 2. Materials and Methods In this present study the EMG signals are obtained during different body postures from the EMG physical action datasheet  ADDIN EN.CITE Frank201018[6]181859Frank, A.Asuncion, A.http://archive.ics.uci.edu/mlUCI Machine Learning Repository2010University of California, Irvine, School of Information and Computer Sciences[ HYPERLINK \l "_ENREF_6" \o "Frank, 2010 #18" 6].These data sheet contains the pre-defined EMG signals which are collected from different muscles like right bicep, right triceps, left bicep, left triceps, right thigh, right hamstring, left thigh and left hamstring during different body postures with the help of Delsys wireless EMG apparatus. Here we are implementing the gesture classification by considering the signals for only four different muscles which are right bicep, right triceps, left bicep and left triceps for classifying the body postures such as bowing, clapping and handshaking of a normal person. The bicep is the muscle which lies on the upper arm between shoulder and elbow. The triceps are the large muscles on the back of the upper limb of vertebrates. This muscle is mainly responsible for extension of the elbow joints while strengthening the arm. Here we only classify the different body postures such as Bowing, Clapping and Handshaking which are assigned as class-1, class-2 and class-3 respectively. The detail flow chart for this classification is focused in figure-1. The EMG signals (in figure-2) in the physical action data sheet contains each of 10000 samples for which we have to segment each signal into 1000 samples for better classification and effective analysis purpose. After segmentation we have extracted various statistical features like the arithmetic mean, root mean square value(RMS), standard deviation, variance, kurtosis, median, mode, summation and Skewness from each EMG signals using LABVIEW corresponding to the different body postures class of a normal person, which is shown in figure-2.These statistical features extracted from each signal as:  Figure-1.Flowchart for the body posture classification Arithmetic Mean-The mean of raw signals given by:  EMBED Equation.DSMT4  (1) Root Mean Square Value-The root mean square value of the signals given as:  EMBED Equation.DSMT4  (2) Standard Deviation-The standard deviation of the raw signals given as:  EMBED Equation.DSMT4  (3) (d) Variance-The variance of the signal is given as:  EMBED Equation.DSMT4  (4) (e) Kurtosis- The kurtosis of the signal given as:  EMBED Equation.DSMT4  (5) (f) Skewness-The Skewness of the signal given as:  EMBED Equation.DSMT4  (6) Among various number of neural network structures, the MFNN is widely used for both classification and regression tasks. The general architecture of MFNN is given in figure-3; the theory behind the working of the MFNN is given by the back propagation algorithm which is used to adjust the weights during training phase.  Figure-2. Statistical feature extraction using LABVIEW  Figure-3 Architecture of MFNN for body posture classification 3. MFNN as classifier The MFNN shown in architecture diagram shown in Figure-3 has three layers namely input layer, hidden layer and output layer. The input layer mainly consists of m nodes in the m dimensional feature vector. The input layer sends the data to the next layer which is called as the hidden layer. Each input node is fully connected with hidden layer nodes by a set of weights Vji. Similarly the data propagation from hidden layer to output layer can be done by setting the weight Vkj. The main aim of MFNN is to minimize the cost function which can be defined as the mean square value of the error between actual output (Yk) and predicted output (Pk). The cost function given by:  EMBED Equation.DSMT4  (7) The back propagation algorithm is used to update the weights for the minimization of the cost function ADDIN EN.CITE POLIKAR2006139[7]13913953ROBI POLIKARPATTERN RECOGNITIONWiley Encyclopedia of Biomedical Engineering2006John Wiley & sons[ HYPERLINK \l "_ENREF_7" \o "POLIKAR, 2006 #139" 7]. This weight minimization corresponds to:  EMBED Equation.DSMT4  (8) This implies  EMBED Equation.DSMT4  (9) As the actual output and predicted output present in the output layer, thats why the change in weight is given as:  EMBED Equation.DSMT4  (10) Where  EMBED Equation.DSMT4  is the derivative of the activation function. Generally sigmoid activation function is used for both hidden and output layers of MFNN. Likewise the change in weights obtained in input to hidden layer is determined by the formulae:  EMBED Equation.DSMT4  (11) Hence  EMBED Equation.DSMT4  (12) Where  EMBED Equation.DSMT4  and  EMBED Equation.DSMT4 are sensitivity of hidden and output layer respectively. 4. Results and discussion Here the proposed classification model is carried out by taking 216 numbers of segmented EMG signal during different body postures. Using LABVIEW software, the various statistical features like Arithmetic Mean, Root Mean Square Value, Standard Deviation, Kurtosis, Median, Mode, Summation, Skewness and Variance are extracted from each EMG signal corresponding to different body postures. Out of this 216 number of examples 50 % are taken as training and 50% are taken as testing purpose for MFNN. During training of MFNN we assume the parameters given as in table-1. Table-1.Optimised neural network parameters during training phase Different parameters taken for training of MFNN classifierValuesNumber of hidden neurons8Learning rate0.9Momentum factor0.9Number of iterations3000 After training operation the optimized training performance is found to be 74% in terms of accuracy and 0.08 in terms of mean square error (MSE).After the training the testing data is evaluated. The measured accuracy of the testing data is given in terms of confusion matrix which is given in Table 2. From Table 2 we calculate the CR. Table-2 confusion matrix for testing features  Class-1 Class-2 Class-3Class-12868Class-25252Class-37323 Hence the (CR) = EMBED Equation.DSMT4 =71.02% Conclusion Here, the various statistical features were successfully extracted from the each class of EMG signal corresponding to different postures. After extracting these features the MFNN classifier was implemented to classify the postures as Bowing, Clapping and Handshaking. Our result confirms that the desired MFNN classifier able to classify the EMG signal features handsomely with a CR of 71.02%. Further this work can be extended by considering more number of body postures corresponding the signals from other muscles of the body. Also the SVM and Ensemble and fuzzy based classifiers can be used to improve the accuracy for the classification. References  ADDIN EN.REFLIST [1] M. R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, "Hand motion detection from EMG signals by using ANN based classifier for human computer interaction," in Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on, 2011, pp. 1-6. [2] S. Yu, M. H. Fisher, A. Wolczowski, G. D. Bell, D. J. Burn, and R. X. Gao, "Towards an EMG-Controlled Prosthetic Hand Using a 3-D Electromagnetic Positioning System," Instrumentation and Measurement, IEEE Transactions on, vol. 56, pp. 178-186, 2007. [3] B. Ayrulu-Erdem and B. Barshan, "Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals," Sensors (Basel), vol. 11, pp. 1721-43, 2011. [4] J. Yang, H. Singh, E. L. Hines, F. Schlaghecken, D. D. Iliescu, M. S. Leeson, and N. G. Stocks, "Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach," Artif Intell Med, vol. 55, pp. 117-26, Jun 2012. [5] M. Zhou, Y. Xu, and L. Tang, "Multilayer ANN indoor location system with area division in WLAN environment," Systems Engineering and Electronics, Journal of, vol. 21, pp. 914-926, 2010. [6] A. Frank and A. Asuncion, "UCI Machine Learning Repository," ed: University of California, Irvine, School of Information and Computer Sciences, 2010. [7] R. POLIKAR, "PATTERN RECOGNITION," in Wiley Encyclopedia of Biomedical Engineering, ed: John Wiley & sons, 2006.      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H.Wolczowski, A.Bell, G. D.Burn, D. J.Gao, R. X.Towards an EMG-Controlled Prosthetic Hand Using a 3-D Electromagnetic Positioning SystemInstrumentation and Measurement, IEEE Transactions onInstrumentation and Measurement, IEEE Transactions on178-186561Acoustic sensorsElectromyographyFingersHumansMusclesProsthetic handPrototypesRobot controlSensor systemsShape measurementdata gloveslearning (artificial intelligence)pattern recognitionprosthetics3D electromagnetic positioningPC sound cardelectromyographic potentialsfinger movementsforearm muscleshand movementshand posturehuman hand posemachine learningrobotic prosthetic handvisual reference3-D electromagnetic positioning systemElectroencephalographic (EEG)electromagnetic sensorelectromyographic (EMG)20070018-945610.1109/tim.2006.887669sDyK  _ENREF_2sDyK  _ENREF_3\ DYang20127[4]7717Yang, J.Singh, H.Hines, E. L.Schlaghecken, F.Iliescu, D. D.Leeson, M. S.Stocks, N. G.School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK. J-Yang@ieee.orgChannel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approachArtif Intell MedArtificial intelligence in medicineArtif Intell MedArtificial intelligence in medicineArtif Intell MedArtificial intelligence in medicine117-265522012/04/17*AlgorithmsAnalysis of VarianceBrain/*physiologyData Interpretation, StatisticalElectroencephalography/*methodsEvoked Potentials/physiologyFingersFourier AnalysisHandHumansLeast-Squares AnalysisMovement/physiology*Neural Networks (Computer)Signal Processing, Computer-Assisted/*instrumentationUser-Computer Interface2012Jun1873-2860 (Electronic) 0933-3657 (Linking)22503644Research Support, Non-U.S. Gov'thttp://www.ncbi.nlm.nih.gov/pubmed/2250364410.1016/j.artmed.2012.02.001engsDyK  _ENREF_4sDyK  _ENREF_5sDyK  _ENREF_6?Dd b  s >A?Picture 1be>JSvfA>!n9>JSvfPNG  IHDR. osRGB pHYs+=IDATx^};A:D`%LY@!5H?Yp8:$|Ngd2L~LO홭 _UuWL^c"jp|D + Y  D]mt,ԀL 젊ƍ"t8k Ì ?`i"@й0@]O@C]1; CztT 'w:;S 2$b`W}=YXZ)* g.\uuuØ@+-n[ORWzԺ!D?>~?𤍀?LܶPJlUiskP9˻E4$+芍ԔR HR4)R_t;j0i5yXJ%p1KA S}IludDUL5˰?"A GDv@WlG4D]!htvpD*@Cv@WlG4D+Պi/ADžCN+"z$}fظ+LMhAaZ4 Kj`e8Z0 Kt[W%pZo?NΆ3e!:  gH A.TytHg&Ӫޙ~&P[ qz`Q)vU,w ;VFymV*f c5L:S*T'xF].Vz:=ZrE(ܓTy-M}+|,F r9ub SLmhJstdW\B%/—}敼BݜꅑDBʖT,\Nf`+Uɥ +=ڣ7LA06jxI^K߳T˽ծUD炥@F2Ä-7zzaJK3+4XFۖNOs}ԝΚQޣJhJAm%/SƵ\6ksh\d7Zv]19@e^9\bд# `^b+ja2ljÔ%,U+ wErB+ p#R#@3ᇽ@Wl H$4C]~h tŖD2@3ET1 dV ׳jf jؽuq2#~]"iy dkZAv_2V7R[o~5bu3ҤV%XYϪ1:. du|W~"@Rn{lX+ǒZ`iu.o(.`ݑ"K]-{2xTbm2S@@~ԬUUjHY&{%P顽*k;eib|XҎA/*`Śl" dihl2J/`. 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