Feature Extraction of MES (MyoElectricSignal) using Temporal approach for prosthetic arm control

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1 International Journal of Electronics and Computer Science Engineering 678 Available Online at ISSN Feature Extraction of MES (MyoElectricSignal) using Temporal approach for prosthetic arm control Mr. Santosh S. Patil 1, Prof. Mahesh S. Kumbhar Rajarambapu Institute of Technology Department of Electronics and Communication Engineering 1 Student M.Tech(Digital System) 2 Associate Professor & Head of Department 1 - san19786@gmail.com 2 - mahesh.kumbhar@ritindia.edu Abstract- Extracting MES signal is the very basic need before movement classification for driving prosthetic arm. Analysing accurate and optimum features form acquiesced MES signal is very useful for meaningful classification of movement. In this paper we have describes the temporal approach in time domain to extract the features form EMG signals which is referred as MUAP. We have discussed MAV method from temporal approach. In this paper we present classification of arm open, close and hold movement. This paper will describe method of analysing MES signal in MATLAB tool & also analysing the linear motion of the actuating device with the help of microcontroller using PROTEUS tool. Result and Conclusion section describes the simulation and practical result of proposed work. Keywords MyoElectricSignal (MES), Electromyography (EMG), Motor Activation Unit Potential (MAUP), Mean absolute value (MAV). I. INTRODUCTION This research work describes the methodology of collecting EMG signal and hardware software approach to drive the prosthetic arm. In first method I have designed the simulation system which acquires the EMG signals from the surface these signal then denoised and amplified to required potential value. In second method I have designed the method which is calculating the linear relationship between the movements and the response rotation of the motor. Following fig 1.1 shows the block diagram of my proposed work. Body BIO Signal Data Acquisition and Data Segmentation Raw Signal Feature Extraction Raw Signal Dimensionality Reduction Prosthetic Device Controller Classification Fig. 1.1 Block Diagram for proposed work. I have worked on the two approaches First approach drives the hand through entire electronic hardware. In second approach I have recorded the EMG signals in to the database and analyzed the signal to find out the extracted pulses

2 679 Feature Extraction of MES (MyoElectricSignal) using Temporal approach for prosthetic arm control by applying temporal approach with the help of MATLAB tool. The extracted MUAP pulses p then given to the microcontroller and based on the switching network I have classified the open, Close and holding movements. Ton-Tai Pan et.al [7] have discussed on Design of myoelectric controlled partial Hand, Hand EMG Signal processing circuit, System design includes, Electronic circuit Sensors, Prosthesis Mechanism.,, Design Methodology and System Implementation by considering Lifting and Gripping Force improvement. Sijiang Du and Vuskovic, M [6] have discussed on the spectoral and temporal approach for f extracting features from the EMG signal and concluded that temporal method, has offered much better time efficiency and lower dimension of the feature vector than the expensive method based on the short time Thompson transform, with just a slightly smaller classification hit rate II. PROPOSED METHODOLOGY & ALGORITHM A. Hardware Approach Fig 1.2 Photographs of designed work As shown in the Fig 1.2 I have designed EMG instrumentation amplifier and acquiring patient data for further analysis. I am using NI DAQ 6009 data acquisition card. EMG Amplifier and Arm driving kit consist of signal out ou port for Filter, Averaging circuit,, Comparator, Com Timer and relay driving circuit. In this Approach I have selected the Disk Electrode sensors to sense EMG sign signals. als. Which then amplified by the Instrumentation amplifier and then signals are filtered by the High pass butterworth 4th order filter. I have used Mean absolute value (MAV) is an estimate of the mean absolute value of the signal xi in a segment i that is N samples in length for or averaging the signal. 1, 1, 1 Then the average value of the signal is classified to the DC level with the help of comparator. Output of the comparator triggers the delay circuit which then drives the relay circuit to trigger prosthetic arm in open and close position. I have taken EMG signals from the database and perform the analysis of the same using following flow. Algorithm is divided into the following category. 1. Acqusition of EMG signal. 2. Processing. Dc offset. Removal of noise using filter Amplifying signal. 3. Analysis using temporal approach. 4. Sending data to the parallel port. 684

3 IJECSE, Volume2, Number 2 Mr. Santosh S. Patil et al. 680 Fig 1.3 Flowchart for MES Feature extraction As shown in Fig 1.3 first step is to acquire EMG data from the surface of the amputee patient. Second step removes the straight lines available in the signal which is called as removal of DC offset values. Third step removes the noise from the signal. Fourth step determines the threshold value required for shaping signal. Fifth step calculates the MAV for the defined vector and extract the feature from the filtered and rectified signal. Sixth step performs the wave shaping on the Raw extracted pulses. Last step sent those extracted pulses to the parallel port to transmit pulses for further classification. Virtual Simulation To find out precise control characteristics I have designed virtual simulation in proteus which takes the extracted pulses from the MATLAB tool. In this approach I have used microcontroller 8051 to find out the movements of the

4 Feature Extraction of MES (MyoElectricSignal) using Temporal approach for prosthetic arm control 681 motor based on the extracted pulses from the EMG signals. To implement this methodology we required virtual port software to which will create virtual Com port on the work station. Need to extract the pulses in Matlab Tool and send it to the Proteus design on virtual com port. Fig 1.4 Virtual simulation to find linear MUAP movements In this method I have Configure Virtual Serial Port Driver tool to create virtual COM ports on the work station. Motor unit requires pulses to drive following fig shows the received pulses on COM port. Fig 1.5 Virtually transmitted MUAP pulses III. EXPERIMENT AND RESULT In this section I am describing all the results which I have received during the conduction of my research work. Below Fig 1.6 describes acquired EMG signal for different 3 persons. Fig 1.7 describes the MUAP obtained from acquiesced signal. Table 1.1 Describes the MAV values for opening, closing and holding movement. 684

5 IJECSE, Volume2, Number 2 Mr. Santosh S. Patil et al. 682 Fig 1.6 EMG signals for different 3 persons for Opening and movements. Person1 Person2 Person3 Fig 1.7 Extracted MUAP from MES signal Mean Absolute Value Open Close Hold Table 1.1 Observed MAV for open and close movement. Table 1.1 describes the calculated MAV for open and close movement for different three persons. From the results I observed that MAV(Close) > MAV(Open). Observation at Instrumentation Amplifier Observation at Filter 684

6 Feature Extraction of MES (MyoElectricSignal) using Temporal approach for prosthetic arm control Hand 400mv 25ms 500mv 25ms Observation at Averaging Circuit 1 Hand 400mV 25ms 800mv 25ms Observation at Comparator 1 Hand 800mV 25ms 2.0 V 25ms Observation at Comparator 1 Hand 800mV V Hand 800mV 3.7 S 12.0 V 3.7 S IV.CONCLUSION From the developed system I have concluded EMG data collection method should be in proper way to get noiseless amplifier. If system consists of noise in the signal it affects on the average value of the classification level. Filter design should have proper cutoff region to select required frequency signals. Developed System for driving prosthetic arm gives better results in classifying the threshold value of the averaged rectified raw pulses and defines the better strength of MUAP which are required for the precise control of the movements. From this work we have been developed the standby system of the switching network which will be helpful in case of the threshold failure.

7 IJECSE, Volume2, Number 2 Mr. Santosh S. Patil et al. 684 V. REFERENCE [1] Phinyomark, A.; Hirunviriya, S.; Limsakul, C.; Phukpattaranont, P, Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on, Publication Year: 2010, Page(s): [2] Xu Zhang; Yu Wang; Han, R.P.S., Wavelet transform theory and its application in EMG signal processing, Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on, Volume: 5,Publication Year: 2010, Page(s): [3] Mahaphonchaikul, K.; Sueaseenak, D.; Pintavirooj, C.; Sangworasil, M.; Tungjitkusolmun, S., EMG signal feature extraction based on wavelet transform,electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on,publication Year: 2010, Page(s): [4] Xinpu Chen; Xiangyang Zhu, A novel spectral analysis method of EMG feature extraction for wrist motions recognition Biomedical Robotics and Biomechatronics, BioRob nd IEEE RAS & EMBS International Conference on, Publication Year: 2008, Page(s): [5] Mulas, M.; Folgheraiter, M.; Gini, G., An EMG-controlled exoskeleton for hand rehabilitation,rehabilitation Robotics, ICORR th International Conference on,publication Year: 2005, Page(s): [6] Sijiang Du; Vuskovic, M., Temporal vs. spectral approach to feature extraction from prehensile EMG signals Information Reuse and Integration, IRI Proceedings of the 2004 IEEE International Conference on,publication Year: 2004, Page(s): [7] Ton-Tai Pan, Ping-Lin,Huihua Kenny Chiang, Mechatronics Experiments Course Design:A Myoelectric controlled partial hand prosthetic project, IEEE transaction on education,vol-47,no.3,august 2004 Prof. Mahesh S. Kumbhar doing his P.hd and working as Head of department in Rajaram bapu Institute of Technology. Having 16 Years of Teaching Experience. Mr. Santosh S. Patil doing M.Tech degree in Electonics (Digital System) from an autonomous college Rajarambapu Institute of Technology, from the Shivaji University. Having 6 Years of Industry experience.

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