Hand movement recognition from sEMG signals using Fourier decomposition method

• Autorzy: Fatimah Binish , Singh Pushpendra , Singhal Amit , Pachori Ram Bilas

Identyfikatory

DOI

10.1016/j.bbe.2021.03.004

• Języki publikacji: EN

Abstrakty

EN

Surface electromyogram (sEMG) provides a non-invasive way to collect EMG signals. The sEMG signals acquired from the muscles of the forearm can be used to recognize the hand grasps and gestures. In this work, an automatic recognition algorithm to identify hand movements using sEMG signals has been proposed. The signals are decomposed into Fourier intrinsic band functions (FIBFs) using the Fourier decomposition method (FDM). The features like entropy, kurtosis, and L1 norm are computed for each FIBF. Statistically relevant features are determined using the Kruskal Wallis test and used to train machine learning-based classifiers like support vector machine, k-nearest neighbor, ensemble bagged trees, and ensemble subspace discriminant. Two publicly available datasets are used to test the efficacy of the proposed algorithm. With an average accuracy of 99:49% on the UCI dataset and 93:53% on NinaPro DB5, the proposed method performs superior than the state-of-the-art algorithms. The performance of the proposed algorithm has also been analyzed in the presence of noise. The proposed method is based on Fourier theory, which makes it suitable for real-time implementation due to low computational complexity. It would help in the design of efficient and easy-to-use prosthetic hands.

Słowa kluczowe

EN

discrete cosine transform; DCT; Fourier decomposition method; FDM; hand movement; surface electromyogram; sEMG; machine learning

PL

dyskretna transformata cosinusowa; DTC; FDM; ruch dłoni; sEMG; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 690--703
• Opis fizyczny: Bibliogr. 70 poz., rys., tab.

Twórcy

autor

Fatimah Binish

• CMR Institute of Technology, Bengaluru, India

autor

Singh Pushpendra

• National Institute of Technology, Hamirpur, India

autor

Singhal Amit

• Bennett University, Greater Noida, India

autor

Pachori Ram Bilas

• Indian Institute of Technology, Indore, India

Bibliografia

• [1] Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D. Improving EMG based classification of basic hand movements using EMD. In: 35th Annual International Conference of the IEEE EMBS. p. 5754–7.
• [2] Nishad A, Upadhyay A, Pachori RB, Acharya UR. Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals. Future Generation Computer Systems 2019;93:96–110.
• [3] Daley H, Englehart K, Hargrove L, Kuruganti U. High density electromyography data of normally limbed and transradial biocybernetics and biomedical engineering 41 (2021) 690–703 701 amputee subjects for multifunction prosthetic control. J Electromyogr Kinesiol 2012;22(3):478–84.
• [4] Kiguchi K, Hayashi Y. A study of EMG and EEG during perceptionassist with an upper-limb power-assist robot. In: IEEE International Conference on Robotics and Automation. p. 2711–6.
• [5] R. Merletti, G. Cerone, Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices, Journal of Electromyography and Kinesiology 54 (2020) 102440.
• [6] Wang Y, Wu Q, Dey N, Fong S, Ashour AS. Deep back propagation-long short-term memory network based upperlimb sEMG signal classification for automated rehabilitation. Biocybern Biomed Eng 2020;40(3):987–1001.
• [7] Amsüss S, Goebel PM, Jiang N, Graimann B, Paredes L, Farina D. Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Trans Biomed Eng 2014;61(4):1167–76.
• [8] Wang J, Hao Q, Xi X, Cao J, Xue A, Wang H. Estimation of continuous joint angles of upper limb based on sEMG by using GA-Elman neural network. Math Problems Eng 2020;4065351.
• [9] Finneran A, O’Sullivan L. Effects of grip type and wrist posture on forearm EMG activity, endurance time and movement accuracy. Int J Ind Ergon 2013;43:91–9.
• [10] Oskoei MA, Hu H. Myoelectric control systems-A survey. Biomed Signal Process Control 2007;2:275–94.
• [11] Xi X, Ma C, Yuan C, Miran SM, Hua X, Zhao Y-B, Luo Z. Enhanced EEG-EMG coherence analysis based on hand movements. Biomed Signal Process Control 2020;56 101727.
• [12] Su H, Ovur SE, Zhou X, Qi W, Ferrigno G, Momi ED. Depth vision guided hand gesture recognition using electromyographic signals. Adv Robotics 2020;34(15):985–97.
• [13] Zhou X, Qi W, Ovur S, et al. A novel muscle-computer interface for hand gesture recognition using depth vision. J Ambient Intell Human Comput 2020;11:5569–80.
• [14] Karabulut D, Ortes F, Arslan YZ, Adli MA. Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics. Biocybern Biomed Eng 2017;37 (2):326–35.
• [15] Simo M, Neto P, Gibaru O. EMG-based online classification of gestures with recurrent neural networks. Pattern Recogn. Lett. 2019;128:45–51.
• [16] Chen C, Yu Y, Ma S, Sheng X, Lin C, Farina D, Zhu X. Hand gesture recognition based on motor unit spike trains decoded from high-density electromyography. Biomed Signal Process Control 2020;55 101637.
• [17] Too J, Abdullah AR, Saad NM. Classification of hand movements based on discrete wavelet transform and enhanced feature extraction. Int J Adv Computer Sci Appl 2019;10(6):83–9.
• [18] Wang C, Guo W, Zhang H, Guo L, Huang C, Lin C. sEMG-based continuous estimation of grasp movements by long-short term memory network. Biomed Signal Process Control 2020;59 101774.
• [19] Narayan Y. SEMG signal classification using KNN classifier with FD and TFD features. Mater Today: Proc 2020.
• [20] Liu X, Xi X, Hua X, Wang H, Zhang W. Feature extraction of surface electromyography using wavelet weighted permutation entropy for hand movement recognition. J Healthcare Eng 2020:8824194.
• [21] Shao J, Niu Y, Xue C, Wu Q, Zhou X, Xie Y, Zhao X. Singlechannel sEMG using wavelet deep belief networks for upper limb motion recognition. Int J Ind Ergon 2020;76 102905.
• [22] She H, Zhu J, Tian Y, Wang Y, Yokoi H, Huang Q. SEMG feature extraction based on stockwell transform improves hand movement recognition accuracy. Sensors 2019;19(20):4457.
• [23] Cote-Allard U, Fall CL, Drouin A, Campeau-Lecours A, Gosselin C, Glette K, Laviolette F, Gosselin B. Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Trans Neural Syst Rehabil Eng 2019;27(4):760–71.
• [24] Leone F, Gentile C, Ciancio AL, Gruppioni E, Davalli A, Sacchetti R, Guglielmelli E, Zollo L. Simultaneous sEMG classification of hand/wrist gestures and forces. Front Neurorobotics 2019;13:42.
• [25] Bai D, Chen S, Yang J. Upper arm motion high-density sEMG recognition optimization based on spatial and timefrequency domain features. Int J Adv Computer Sci Appl 2019;3958029.
• [26] Amanpreet K. Machine learning-based novel approach to classify the shoulder motion of upper limb amputees. Biocybern Biomed Eng 2019;39(3):857–67.
• [27] Cai S, Chen Y, Huang S, Wu Y, Zheng H, Li X, Xie L. SVM-based classification of sEMG signals for upper-limb selfrehabilitation training. Front. Neurorobotics 2019;13:31.
• [28] Xue Y, Ju Z. SEMG based intention identification of complex hand motion using nonlinear time series analysis. In: 2019 9th International Conference on Information Science and Technology (ICIST). p. 357–61.
• [29] Shi W-T, Lyu Z-J, Tang S-T, Chia T-L, Yang C-Y. A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study. Biocybern Biomed Eng 2018;38 (1):126–35.
• [30] M.S. Isakovi, N. Miljkovi, M.B. Popovi, Classifying sEMG-based hand movements by means of principal component analysis, in: 2014 22nd Telecommunications Forum Telfor (TELFOR), 2014, pp. 545–548.
• [31] Shen S, Gu K, Chen X, Yang M, Wang R. Movements classification of multi-channel sEMG based on CNN and stacking ensemble learning. IEEE Access 2019;7:137489–500.
• [32] Wei W, Dai Q, Wong Y, Hu Y, Kankanhalli M, Geng W. Surfaceelectromyography-based gesture recognition by multi-view deep learning. IEEE Trans Biomed Eng 2019;66(10):2964–73.
• [33] Qi W, Su H, Aliverti A. A smartphone-based adaptive recognition and real-time monitoring system for human activities. IEEE Trans. Human-Mach. Syst. 2020;50(5):414–23.
• [34] Su H, Qi W, Yang C, Sandoval J, Ferrigno G, Momi ED. Deep neural network approach in robot tool dynamics identification for bilateral teleoperation. IEEE Robotics Autom. Lett. 2020;5(2):2943–9.
• [35] Ouyang G, Zhu X, Ju Z. Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot. IEEE J. Biomed. Health Inform. 2014;18:257–65.
• [36] Ju Z, Liu H. Human hand motion analysis with multisensory information. IEEE/ASME Trans Mechatron 2014;19:456–66.
• [37] Nazemi A, Maleki A. Artificial neural network classifier in comparison with LDA and LS-SVM classifiers to recognize 52 hand postures and movements. In: 4th International Conference on Computer and Knowledge Engineering. p. 18–22.
• [38] Tello RMG, Bastos-Filho T, Costa RM, Frizera-Neto A, Arjunan S, Kumar D. Towards sEMG classification based on Bayesian and k-NN to control a prosthetic hand. In: ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC). p. 1–6.
• [39] N.M. Kakoty, S.M. Hazarika, Recognition of grasp types through principal components of DWT based EMG features, in: IEEE International Conference on Rehabilitation Robotics, 2011, pp. 1–6.
• [40] Gaur P, Pachori RB, Wang H, Prasad G. A multi-class EEGbased BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry. Expert Syst Appl 2018;95:201–11.
• [41] Khezri M, Jahed M. Surface electromyogram signal estimation based on wavelet thresholding technique. In: 702 biocybernetics and biomedical engineering 41 (2021) 690 – 703 Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. p. 4752–5.
• [42] Ruangpaisarn Y, Jaiyen S. sEMG signal classification using SMO algorithm and singular value decomposition. In: Proceedings of 7th International Conference on Information Technology and Electrical Engineering. p. 46–50.
• [43] Iqbal O, Fattah SA, Zahin S. Hand movement recognition based on singular value decomposition of surface EMG signal. Proceedings of IEEE Region 10 Humanitarian Technology Conference 2017:837–42.
• [44] Akben S. Low-cost and easy-to-use grasp classification, using a simple 2-channel surface electromyography. Biomed Res 2017;28:577–82.
• [45] Singh P, Joshi SD, Patney RK, Saha K. The Fourier decomposition method for nonlinear and non-stationary time series analysis. Proc R Soc London A: Math, Phys Eng Sci, 473, 2017. p. 1–27.
• [46] Singh P, Singhal A, Joshi SD. Time-frequency analysis of gravitational waves. In: 2018 International Conference on Signal Processing and Communications (SPCOM). p. 197–201.
• [47] Singh P, Joshi SD. Some studies on multidimensional Fourier theory for Hilbert transform, analytic signal and AM–FM representation. Circuits, Systems, Signal Processing 2019;38:5623–50.
• [48] Singh P, Pachori RB. Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms. J Mech Med Biol 2017;17(7):1–16.
• [49] Singhal A, Singh P, Fatimah B, Pachori RB. An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomed Signal Process Control 2020;57 101741.
• [50] Fatimah B, Singh P, Singhal A, Pachori RB. Detection of apnea events from ECG segments using Fourier decomposition method. Biomed Signal Process Control 2020;61 102005.
• [51] Singhal A, Singh P, Lall B, Joshi SD. Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons Fractals 2020;138 110023.
• [52] Pizzolato S, Tagliapietra L, Cognolato M, Reggiani M, Mu¨ ller H, Atzori M. Comparison of six electromyography acquisition setups on hand movement classification tasks. PLOS ONE 2017;12(10):1–17.
• [53] Duncan SF, Saracevic CE, Kakinoki R. Biomechanics of the hand. Hand Clinics 2013;29(4):483–92.
• [54] Singh P. Novel Fourier quadrature transforms and analytic signal representations for nonlinear and non-stationary time series analysis. Royal Soc. Open Sci.nce 2018;5(181131):1–26.
• [55] Ahmed N, Natarajan T, Rao KR. Discrete cosine transform. IEEE Trans Computers 1974;C-23(1):90–3.
• [56] Britanak V, Yip PC, Rao KR. Discrete Cosine and Sine Transforms: General properties, Fast algorithms and Integer Approximations. Oxford: Academic Press; 2006.
• [57] Di C, Li S, Li F, Qi K. A novel framework for learning automata: A statistical hypothesis testing approach. IEEE Access 2019;7:27911–22.
• [58] Ahsen ME, Singh NK, Boren T, Vidyasagar M, White MA. A new feature selection algorithm for two-class classification problems and application to endometrial cancer. In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). p. 2976–82.
• [59] McKight PE, Najab J. Kruskal-Wallis Test, The Corsini Encyclopedia of Psychology. American Cancer Society; 2010.
• [60] C. Cortes, V. Vapnik, Support-vector networks, in: Machine Learning, Vol. 20, 1995, pp. 273–297.
• [61] Mehla VK, Singhal A, Singh P. A novel approach for automated alcoholism detection using Fourier decomposition method. J Neurosci Methods 2020;346 108945.
• [62] Xie B, Minn H. Real-time sleep apnea detection by classifier combination. IEEE Trans Inf Technol Biomed 2012;16:469–77.
• [63] Qurraie SS, Afkhami RG. ECG arrhythmia classification using time frequency distribution techniques. Biomed Eng Lett 2017;7:325–32.
• [64] A.S. Ashour, Y. Guo, A.R. Hawas, G. Xu, Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images, Health Information Science and Systems 6 (21).
• [65] Mikoajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW). p. 117–22.
• [66] Q. Wen, L. Sun, X. Song, J. Gao, X. Wang, H. Xu, Time series data augmentation for deep learning: A survey, arXiv: 2002.12478 (2020).
• [67] Wang F, hua Zhong S, Peng J, Jiang J, Liu Y. Data augmentation for EEG-based emotion recognition with deep convolutional neural networks, MultiMedia Modeling. Lect Notes Comput Sci 2018;10705:82–93.
• [68] Yavuz E, Eyupoglu C. A cepstrum analysis-based classification method for hand movement surface EMG signals. Med Biolog Eng Computing 2019;57:2179–201.
• [69] Sikder N, Mohammad Arif AS, Nahid A. Heterogeneous hand guise classification based on surface electromyographic signals using multichannel convolutional neural network. In: 2019 22nd International Conference on Computer and Information Technology (ICCIT). p. 1–6.
• [70] Bergil E, Oral C, Ergul E. Efficient hand movement detection using k-means clustering and k-nearest neighbor algorithms. J Med Biolog Eng 2020:1–14.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).