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Classification of sEMG Signal-Based Arm Action Using Convolutional Neural Network

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Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems

Abstract

Prosthetic arms are rapidly gaining pace because of their use in the development of robotic prosthesis. The surface electromyography (sEMG) signal acquired from the residual limb of amputee helps to control the movement of prosthesis. In this work, the sEMG signal is acquired using a dual-channel amplifier (Olimex EMG shield) from the below-elbow muscles of one amputee for six actions. The acquired signal is pre-processed using band-pass and band-stop filter to eliminate the noise in the signal. The classification is accomplished using machine learning and deep learning approach. The testing of machine learning algorithm and deep learning are implemented in Raspberry Pi 3 embedded in a Python script. In the machine learning approach, 11 relevant time domain features are extracted from pre-processed signal that are fed as input to linear support vector machines for classification. In the second approach, the signals are converted into images for deep learning analysis. Convolutional neural network (CNN) is used for classification of six hand actions. The model is trained and tested by varying a number of steps per epoch and number of epochs, and accuracy is compared with linear support vector machine (SVM). Results demonstrate that the mean accuracy of linear support vector machine is observed to be 76.66%, whereas for CNN model with 1000 steps per epoch with 10 epochs, it is found to be 91.66%. The classification accuracy of CNN is higher than machine learning model. Thus the usage of proposed methodology with CNN model could act as an indigenous system for sEMG signal acquisition and to activate the signal-controlled prosthetic arm which aids in rehabilitation of the amputees.

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References

  1. Scheme E, Englehart K (2011) Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehab Res Dev 48(6)

    Google Scholar 

  2. Sapsanis C, Georgoulas G, Tzes A (2013, June) EMG based classification of basic hand movements based on time-frequency features. In 21st Mediterranean conference on control and automation (pp. 716–722). IEEE

    Google Scholar 

  3. Subasi A, Yilmaz M, Ozcalik HR (2006) Classification of EMG signals using wavelet neural network. J Neurosci Methods 156(1–2):360–367

    Article  Google Scholar 

  4. Ismail R, Wijaya GD, Ariyanto M, Suriyanto A, Caesarendra W (2018, October) Development of myoelectric prosthetic hand based on Arduino IDE and visual C# for trans-radial amputee in Indonesia. In 2018 international conference on applied engineering (ICAE) (pp. 1–5). IEEE

    Google Scholar 

  5. Chowdhury RH, Reaz MB, Ali MABM, Bakar AA, Chellappan K, Chang TG (2013) Surface electromyography signal processing and classification techniques. Sensors 13(9):12431–12466

    Article  Google Scholar 

  6. Reaz MBI, Hussain MS, Mohd-Yasin F (2006) Techniques of EMG signal analysis: detection, processing, classification and applications (correction). Biol Proced Online 8(1):163–163

    Article  CAS  Google Scholar 

  7. Fraser GD, Chan AD, Green JR, MacIsaac DT (2014) Automated biosignal quality analysis for electromyography using a one-class support vector machine. IEEE Trans Instrum Meas 63(12):2919–2930

    Article  Google Scholar 

  8. McCool P, Fraser GD, Chan AD, Petropoulakis L, Soraghan JJ (2014) Identification of contaminant type in surface electromyography (EMG) signals. IEEE Trans Neural Syst Rehabil Eng 22(4):774–783

    Article  Google Scholar 

  9. Thongpanja S, Phinyomark A, Quaine F, Laurillau Y, Limsakul C, Phukpattaranont P (2016) Probability density functions of stationary surface EMG signals in noisy environments. IEEE Trans Instrum Meas 65(7):1547–1557

    Article  Google Scholar 

  10. Hamedi M, Salleh SH, Ting CM, Astaraki M, Noor AM (2016) Robust facial expression recognition for MuCI: a comprehensive neuromuscular signal analysis. IEEE Trans Affect Comput 9(1):102–115

    Article  Google Scholar 

  11. Khezri M, Jahed M (2008, August) Surface electromyogram signal estimation based on wavelet thresholding technique. In 2008 30th annual international conference of the IEEE engineering in medicine and biology society (pp. 4752–4755). IEEE

    Google Scholar 

  12. Maier J, Naber A, Ortiz-Catalan M (2017) Improved prosthetic control based on myoelectric pattern recognition via wavelet-based de-noising. IEEE Trans Neural Syst Rehabil Eng 26(2):506–514

    Article  Google Scholar 

  13. Phinyomark A, Phukpattaranont P, Limsakul C (2011) Wavelet-based denoising algorithm for robust EMG pattern recognition. Fluct Noise Lett 10(02):157–167

    Article  Google Scholar 

  14. Phinyomark A, Limsakul C, Phukpattaranont P (2009, March) A comparative study of wavelet denoising for multifunction myoelectric control. In 2009 international conference on computer and automation engineering (pp. 21–25). IEEE

    Google Scholar 

  15. Phinyomark A, Limsakul C, Phukpattaranont P (2009, May) An optimal wavelet function based on wavelet denoising for multifunction myoelectric control. In 2009 6th international conference on electrical engineering/electronics, computer, telecommunications and information technology (Vol. 2, pp. 1098–1101). IEEE

    Google Scholar 

  16. Hargrove L, Scheme E, Englehart K, Hudgins B (2008) Filtering strategies for robust myoelectric pattern classification. CMBES Proceedings 31

    Google Scholar 

  17. De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH (2010) Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech 43(8):1573–1579

    Article  Google Scholar 

  18. Powar OS, Chemmangat K, Figarado S (2018) A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals. Biomed Sig Process Cont 42:277–286

    Article  Google Scholar 

  19. Fraser GD, Chan AD, Green JR, Abser N, MacIsaac D (2011) CleanEMG—power line interference estimation in sEMG using an adaptive least squares algorithm. In 2011 annual international conference of the IEEE engineering in medicine and biology society (pp. 7941–7944).IEEE

    Google Scholar 

  20. Ortolan RL, Mori RN, Pereira RR, Cabral CM, Pereira JC, Cliquet A (2003) Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment. IEEE Trans Neural Syst Rehabil Eng 11(1):60–69

    Article  Google Scholar 

  21. Zhou P, Lock B, Kuiken TA (2007) Real time ECG artifact removal for myoelectric prosthesis control. Physiol Meas 28(4):397

    Article  CAS  Google Scholar 

  22. Phinyomark A, Limsakul C, Phukpattaranont P (2011) Application of wavelet analysis in EMG feature extraction for pattern classification. Measure Sci Rev 11(2):45–52

    Google Scholar 

  23. Phinyomark A, Nuidod A, Phukpattaranont P, Limsakul C (2012) Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. ElektronikairElektrotechnika 122(6):27–32

    Google Scholar 

  24. Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39(8):7420–7431

    Article  Google Scholar 

  25. Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y (2013) EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst Appl 40(12):4832–4840

    Article  Google Scholar 

  26. Ahsan MR, Ibrahimy MI, Khalifa OO (2011, May) Electromyography (EMG) signal based hand gesture recognition using artificial neural network (ANN). In 2011 4th international conference on mechatronics (ICOM) (pp. 1–6).IEEE

    Google Scholar 

  27. Boostani R, Moradi MH (2003) Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol Meas 24(2):309

    Article  Google Scholar 

  28. Zardoshti-Kermani M, Wheeler BC, Badie K, Hashemi RM (1995) EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans Rehabil Eng 3(4):324–333

    Article  Google Scholar 

  29. Zhang YT, Herzog W, Liu MM (1995, September) A mathematical model of myoelectric signals obtained during locomotion. In proceedings of 17th international conference of the engineering in medicine and biology society (Vol. 2, pp. 1403–1404).IEEE

    Google Scholar 

  30. Oskoei MA, Hu H (2006, December) GA-based feature subset selection for myoelectric classification. In 2006 IEEE international conference on robotics and biomimetics (pp. 1465–1470).IEEE

    Google Scholar 

  31. Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55(8):1956–1965

    Article  Google Scholar 

  32. Hudgins B, Parker P, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40(1):82–94

    Article  CAS  Google Scholar 

  33. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3–4):197–387

    Article  Google Scholar 

  34. Park KH, Lee SW (2016, February) Movement intention decoding based on deep learning for multiuser myoelectric interfaces. In 2016 4th international winter conference on brain-computer Interface (BCI) (pp. 1–2).IEEE

    Google Scholar 

  35. Atzori M, Cognolato M, Müller H (2016) Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorobot 10:9

    Article  Google Scholar 

  36. Geng W, Du Y, Jin W, Wei W, Hu Y, Li J (2016) Gesture recognition by instantaneous surface EMG images. Sci Rep 6:36571

    Article  CAS  Google Scholar 

  37. Xia P, Hu J, Peng Y (2018) EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks. Artif Organs 42(5):E67–E77

    Article  Google Scholar 

  38. Allard UC, Nougarou F, Fall CL, Giguère P, Gosselin C, Laviolette F, Gosselin B (2016, October). A convolutional neural network for robotic arm guidance using semg based frequency-features. In 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2464–2470).IEEE

    Google Scholar 

  39. Samarawickrama K, Ranasinghe S, Wickramasinghe Y, Mallehevidana W, Marasinghe V, Wijesinghe K (2018) Surface EMG signal acquisition analysis and classification for the operation of a prosthetic limb. Int J Biosci Biochem Bioinformatics 8:32–41

    Google Scholar 

  40. Sharma S, Farooq H, Chahal N (2016) Feature extraction and classification of surface EMG signals for robotic hand simulation. Commun Appl Electron 4:27

    Article  Google Scholar 

  41. Sathish S, Nithyakalyani K, Vinurajkumar S, Vijayalakshmi C, Sivaraman J (2016) Control of robotic wheel chair using EMG signals for paralysed persons. Indian J Sci Technol 9(1):1–3

    Article  CAS  Google Scholar 

  42. Savithri CN, Priya E (2019) Statistical analysis of EMG-based features for different hand movements. In: Smart intelligent computing and applications. Springer, Singapore, pp 71–79

    Chapter  Google Scholar 

  43. Savithri CN, Priya E (2017, January) Correlation coefficient based feature selection for actuating myoelectric prosthetic arm. In 2017 trends in industrial measurement and automation (TIMA) (pp. 1–6).IEEE

    Google Scholar 

  44. Vapnik V (2013) The nature of statistical learning theory. Springer

    Google Scholar 

  45. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  46. Zia urRehman M, Waris A, Gilani SO, Jochumsen M, Niazi IK, Jamil M et al (2018) Multiday EMG-based classification of hand motions with deep learning techniques. Sensors 18(8):2497

    Article  Google Scholar 

  47. Said AB, Mohamed A, Elfouly T, Harras K, Wang ZJ (2017, March) Multimodal deep learning approach for joint EEG-EMG data compression and classification. In 2017 IEEE wireless communications and networking conference (WCNC) (pp. 1-6). IEEE

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sri Sai Ram Engineering College, Chennai, Tamilnadu, India

    C. N. Savithri, E. Priya & J. Sudharsanan

Authors
  1. C. N. Savithri
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  2. E. Priya
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  3. J. Sudharsanan
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Correspondence to C. N. Savithri .

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Editors and Affiliations

  1. Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India

    E. Priya

  2. Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India

    V. Rajinikanth

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Savithri, C.N., Priya, E., Sudharsanan, J. (2021). Classification of sEMG Signal-Based Arm Action Using Convolutional Neural Network. In: Priya, E., Rajinikanth, V. (eds) Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-6141-2_13

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