Abstract
It is of great importance to effectively process and interpret surface electromyogram (sEMG) signals to actuate a robotic and prosthetic exoskeleton hand needed by hand amputees. In this paper, we have proposed a cepstrum analysis-based method for classification of basic hand movement sEMG signals. Cepstral analysis technique primarily used for analyzing acoustic and seismological signals is effectively exploited to extract features of time-domain sEMG signals by computing mel-frequency cepstral coefficients (MFCCs). The extracted feature vector consisting of MFCCs is then forwarded to feed a generalized regression neural network (GRNN) so as to classify basic hand movements. The proposed method has been tested on sEMG for Basic Hand movements Data Set and achieved an average accuracy rate of 99.34% for the five individual subjects and an overall mean accuracy rate of 99.23% for the collective (mixed) dataset. The experimental results demonstrate that the proposed method surpasses most of the previous studies in point of classification accuracy. Discrimination ability of the cepstral features exploited in this study is quantified using Kruskal-Wallis statistical test. Evidenced by the experimental results, this study explores and establishes applicability and efficacy of cepstrum-based features in classifying sEMG signals of hand movements. Owing to the non-iterative training nature of the artificial neural network type adopted in the study, the proposed method does not demand much time to build up the model in the training phase.
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References
Ruangpaisarn Y, Jaiyen S (2015) sEMG signal classification using SMO algorithm and singular value decomposition. In: 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE)
Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D (2013) Improving EMG based classification of basic hand movements using EMD. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Caton R (1875) The electric currents of the brain. Am J EEG Technol 10(1):12–14
Kiguchi K, Hayashi YA (2012) A study of EMG and EEG during perception-assist with an upper-limb power-assist robot. In: 2012 IEEE International Conference on Robotics and Automation
Sapsanis C, Georgoulas G, Tzes A (2013) EMG based classification of basic hand movements based on time-frequency features. In: 2013 21st Mediterranean Conference on Control & Automation (MED)
Nishad A, Upadhyay A, Pachori RB, Acharya UR (2019) Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals. Future Gener Comp Syst 93:96–10
Kurita Y, Tada M, Matsumoto Y, Ogasawara T (2002) Simultaneous measurement of the grip/load force and the finger EMG: effects of the grasping condition. In: 11th IEEE International Workshop on Robot and Human Interactive Communication
Finneran A, O'Sullivan L (2013) Effects of grip type and wrist posture on forearm EMG activity, endurance time and movement accuracy. Int J Ind Ergon 43(1):91–99
Liu J, Zhou P (2013) A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury. IEEE Trans Neural Syst Rehabil Eng 21(1):96–103
Kaniusas E (2012) Fundamentals of biosignals. In: Biomedical signals and sensors I. Springer, Berlin, pp 1–26
Carroll D, Subbiah A (2012) Recent advances in biosensors and biosensing protocols. J Biosens Bioelectron 3:3
Iqbal O, Fattah SA, Zahin S (2017) Hand movement recognition based on singular value decomposition of surface EMG signal. In Humanitarian Technology Conference (R10-HTC), 2017 IEEE Region 10, pp 837–842
Pons JL, Rocon E, Ruiz AF, Moreno JC (2007) Upper-limb robotic rehabilitation exoskeleton: tremor suppression. In: Rehabilitation robotics. InTech, London
Hayashi T, Kawamoto H, Sankai Y (2005) Control method of robot suit HAL working as operator's muscle using biological and dynamical information. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3063–3068
Kiguchi K, Tanaka T, Fukuda T (2004) Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Trans Fuzzy Syst 12(4):481–490
Wang W, Zhang G, Yang L, Balaji VS, Elamaran V, Arunkumar N (2019) Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals. Future Gener Comp Syst 98:227–232
Mewett DT, Reynolds KJ, Nazeran H (2004) Reducing power line interference in digitised electromyogram recordings by spectrum interpolation. Med Biol Eng Comput 42(4):524–531
Wang N, Lao K, Zhang X (2017) Design and myoelectric control of an anthropomorphic prosthetic hand. J Bionic Eng 14(1):47–59
Parker P, Englehart K, Hudgins B (2006) Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol 16(6):541–548
Yamanoi Y, Morishita S, Kato R, Yokoi H (2017) Development of myoelectric hand that determines hand posture and estimates grip force simultaneously. Biomed Signal Process Control 38:312–321
Khezri M, Jahed M (2008) Surface electromyogram signal estimation based on wavelet thresholding technique. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 4752–4755
Kakoty NM, Hazarika SM (2011) Recognition of grasp types through principal components of dwt based emg features. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp 1–6
Nazemi A, Maleki A (2014) Artificial neural network classifier in comparison with LDA and LS-SVM classifiers to recognize 52 hand postures and movements. In: 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE), pp 18–22
Ju Z, Liu H (2014) Human hand motion analysis with multisensory information. IEEE-ASME Trans Mech 19(2):456–466
Ouyang G, Zhu X, Ju Z, Liu H (2014) Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot. IEEE J Biomed Health 18(1):257–265
Akben SB (2017) Low-cost and easy-to-use grasp classification, using a simple 2-channel surface electromyography (sEMG). Biomed Res India 28(2):577–582
Tabatabaei SM, Chalechale A (2019) Local binary patterns for noise-tolerant sEMG classification. SIViP 13(3):491–498
Sapsanis C, Georgoulas G, Tzes A (2013) sEMG for basic hand movements data set, UCI machine LearningRepository. https://archive.ics.uci.edu/ml/datasets/sEMG+for+Basic+Hand+movements . Accessed October 9, 2018
Merletti R, Di Torino P (1999) Standards for reporting EMG data. J Electromyogr Kinesiol 9(1):3–4
Perrott MH (2007) Lecture notes of basic communication course. MIT, Cambridge http://web.mit.edu/6.02/www/s2007/lec10.pdf. Accessed November 2, 2018
Østensvik T, Belbo H, Veiersted KB (2019) An automatic pre-processing method to detect and reject signal artifacts from full-shift field-work sEMG recordings of bilateral trapezius activity. J Electromyogr Kinesiol 46:49–54
Oppenheim AV, Schafer RW (2004) From frequency to quefrency: a history of the cepstrum. IEEE Signal Process Mag 21(5):95–106
Bogert BP, Healy MJR, Tukey JW (1963) The quefrency analysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum, and saphe cracking. In: Rosenblatt M (ed) Proc. of the Symp. On time series analysis. Wiley, Hoboken, pp 209–243
Randall RB (2017) A history of cepstrum analysis and its application to mechanical problems. Mech Syst Signal Process 97:3–19
Rabiner LR, Schafer RW (2007) Introduction to digital speech processing. Found Trends Signal Process 1:1–194
Davis SB, Mermelstein P (1990) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. In Readings in speech recognition, pp 65–74
Holmes J, Holmes W (2001) Speech synthesis and recognition. Taylor & Francis, London
Mogran N, Bourlard H, Hermansky H (2004) Automatic speech recognition: an auditory perspective. In: Speech processing in the auditory system. Springer, New York, pp 309–338
Yavuz E, Topuz V (2018) A phoneme-based approach for eliminating out-of-vocabulary problem of Turkish speech recognition using Hidden Markov Model. Comput Syst Sci Eng 33(6):429–445
Salo F, Nassif AB, Essex A (2019) Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Comput Netw 148:164–175
Fausett LV (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Englewood Cliffs
Yavuz E, Kasapbaşı MC, Eyüpoğlu C, Yazıcı R (2018) An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybern Biomed Eng 38(2):201–216
Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design. Pws Pub, Boston
Powell MJ (1987) Radial basis functions for multivariable interpolation: a review. Algorithms for approximation, pp 143–167
Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill, New York
Yavuz E, Eyupoglu C, Sanver U, Yazici R (2017) An ensemble of neural networks for breast cancer diagnosis. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp 538–543
Cochocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. Wiley, Hoboken
Hannan SA, Manza RR, Ramteke RJ (2010) Generalized regression neural network and radial basis function for heart disease diagnosis. Int J Comput Appl 7(13):7–13
Bauer MM (1995) General regression neural network for technical use, Master’s thesis. University of Wisconsin-Madison
Demuth H, Beale M, Hagan M (2006) Neural network toolbox user’s guide. The MathWorks Inc, Natick
Polat K, Güneş S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187(2):1017–1026
Zainuddin Z, Huong LK, Pauline O (2013) Reliable epileptic seizure detection using an improved wavelet neural network. Australas Med J 6(5):308–314
Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. San Francisco, Elsevier, Morgan Kaufmann Publishers
Eyupoglu C, Aydin MA, Zaim AH, Sertbas A (2018) An efficient big data anonymization algorithm based on chaos and perturbation techniques. Entropy 20(5):373
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437
MathWorks Statistics and Machine Learning Toolbox (2018) The MathWorks Inc
Subasi A, Alharbi L, Madani R, Qaisar SM (2018) Surface EMG based classification of basic hand movements using rotation forest. In 2018 advances in science and engineering technology international conferences (ASET), pp 1–5
Pachori RB (2008) Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process 14:1–6
Sharma R, Pachori RB (2015) Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42(3):1106–1117
Bhati D, Sharma M, Pachori RB, Gadre VM (2017) Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digit Signal Process 62:259–273
Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895–1923
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Yavuz, E., Eyupoglu, C. A cepstrum analysis-based classification method for hand movement surface EMG signals. Med Biol Eng Comput 57, 2179–2201 (2019). https://doi.org/10.1007/s11517-019-02024-8
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DOI: https://doi.org/10.1007/s11517-019-02024-8