Advertisement

Gaussian Smoothing Filter for Improved EMG Signal Modeling

  • Ibrahim F. J. GhalyanEmail author
  • Ziyad M. Abouelenin
  • Gnanapoongkothai Annamalai
  • Vikram Kapila
Chapter
  • 40 Downloads

Abstract

The goal of the research presented in this chapter is to improve the classification process of electromyography (EMG) signals that are contaminated with noise. If the existence of noise in EMG signals is not accounted for, it can degrade the performance of the classification task. Therefore, it is necessary to utilize an efficient filtering process to improve the classification of EMG signals. Guided by the need to filter the noise out of EMG signals, this chapter proposes to employ a Gaussian smoothing filter (GSF) that is simple in its implementation with an efficient filtering performance. The GSF, which is a Gaussian function, offers equal support in both frequency and time domains, allowing it to yield a performance compromise in removing the noise while preserving high frequency components of EMG signals. It is additionally shown that the use of GSF not only enhances the classification accuracy, but it also reduces the computational time needed in the training and testing of the classification process. To evaluate the performance of the GSF in EMG signals classification problem, two experiments are considered. The first experiment consists of classification of multiple hand gestures using EMG signals and the second experiment considers classifying phases of hand motion for a grasping task. An array of standard classification techniques are considered in both experiments and the use of GSF in filtering out noise is shown to enhance classification accuracy with remarkably reduced computational time for the considered classification techniques. This illustrates the feasibility of GSF in filtering EMG signals for classification tasks. To gain further insights into the GSF, its performance is compared with that of a median filter (MF), one of the well-known filtering techniques. By using the overall classification accuracy as an index of comparison, the GSF is shown to result in a superior classification accuracy, demonstrating its efficacy for EMG signals filtering process. Thus, employing GSF proves to provide enhancement in the classification accuracy and required computational efforts.

Keywords

Electromyography EMG Classification Gaussian filter Signals smoothing 

Notes

Acknowledgements

This work is supported in part by the National Science Foundation grants DRK-12 DRL: 1417769, ITEST DRL: 1614085, and RET Site EEC: 1542286, and NY Space Grant Consortium grant 76156-10488.

References

  1. 1.
    Kamen, G., & Gabriel, D. (2010). Essentials of Electromyography. Champagn, IL: Human Kinetics.Google Scholar
  2. 2.
    Botelho, S. Y. (1955). Comparison of simultaneously recorded electrical and mechanical activity in myasthenia gravis patients and in partially curarized normal humans. The American Journal of Medicine, 19(5), 693–696.CrossRefGoogle Scholar
  3. 3.
    Choi, C., & Kim, J. (2007). A real-time EMG-based assistive computer interface for the upper limb disabled. In IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk, The Netherlands (pp. 459–462).Google Scholar
  4. 4.
    Sandoval, A. E. (2010). Electrodiagnostics for low back pain. Physical Medicine and Rehabilitiation Clinics of North America, 21(4), 767–776.CrossRefGoogle Scholar
  5. 5.
    Sharma, S., & Dubey, A. K. (2012). Movement control of robot in real time using EMG signal. In 2nd International Conference on Power, Control and Embedded Systems, Allahabad, India (pp. 1–4).Google Scholar
  6. 6.
    Di Nardo, F., et al. (2015). Assessment of the ankle muscle co-contraction during normal gait: A surface electromyography study. Journal of Electromyography and Kinesiology, 25(2), 347–354.CrossRefGoogle Scholar
  7. 7.
    Nazmi, N., et al. (2016). A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors, 16(8).CrossRefGoogle Scholar
  8. 8.
    Graupe, D., & Cline, W. K. (1975). Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Transactions on Systems, Man, and Cybernetics, SMC-5(2), 252–259.CrossRefGoogle Scholar
  9. 9.
    Englehart, K., Hudgins, B., Parker, P. A., & Stevenson, M. (1999). Classification of the myoelectric signal using time-frequency based representations. Medical Engineering & Physics, 21(6–7), 431–438.CrossRefGoogle Scholar
  10. 10.
    Nishikawa, D., Yu, W., Yokoi, H., & Kakazu, Y. (1999). EMG prosthetic hand controller using real-time learning method. In IEEE International Conference on Systems, Man, and Cybernetics (pp. 153–158).Google Scholar
  11. 11.
    Ju, P., Kaelbling, L. P., & Singer, Y. (2000). State-based classification of finger gestures from electromyographic signals. In Proceedings of the 7th International Conference on Machine Learning, Stanford, CA, USA (pp. 439–446).Google Scholar
  12. 12.
    Yoshikawa, M., Mikawa, M., & Tanaka, K. (2006). Real-time hand motion estimation using EMG signals with support vector machines. In SICE-ICASE International Joint Conference, Busan, South Korea (pp. 593–598).Google Scholar
  13. 13.
    Murugappan, M. (2011). Electromyogram signal based human emotion classification using KNN and LDA. In IEEE International Conference on System Engineering and Technology (ICSET), Sham Alam, Malaysia (pp. 106–110).Google Scholar
  14. 14.
    Negi, S., Kumar, Y., & Mishra, V. M. (2016). Feature extraction and classification for EMG signals using linear discriminant analysis. In 2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA), Bareilly, India (pp. 1–6).Google Scholar
  15. 15.
    Orjuela-Cañón, A. D., Ruíz-Olaya, A. F., & Forero, L. (2017). Deep neural network for EMG signal classification of wrist position: Preliminary results. In IEEE Latin American Conference on Computational Intelligence (LA-CCI), Arequipa, Peru (pp. 1–5).Google Scholar
  16. 16.
    Ghalyan, I. F., Abouelenin, Z. M., & Kapila, V. (2018). Gaussian filtering of EMG signals for improved hand gesture classification. In The IEEE Signal Processing in Medicine and Biology Symposium (SPMB 2018), Philadelphia, PA, USA (pp. 1–6).Google Scholar
  17. 17.
    Battye, C. K., Nightingale, A., & Willis, J. (1955). The use of myo-electric currents in the operation of prostheses. The Journal of Bone and Joint Surgery, British, 37–B(3), 506–510.CrossRefGoogle Scholar
  18. 18.
    Kobrinsky, A. (1960). Bioelectric control systems. Radio USSR (In Russian), 11, 37–39.Google Scholar
  19. 19.
    Bottomley, A. H. (1962). Working model of a Myo-electric control system. In Proceedings of the International Symposium on the Applications of Automatic Control Prosthetic Design, Belgrade, Yugoslavia (pp. 37–45).Google Scholar
  20. 20.
    Bottomley, A. H. (1963). Myo-electriccontrol of powered prostheses. The Journal of Bone and Joint Surgery, British Volume, 47(3), 411–415.CrossRefGoogle Scholar
  21. 21.
    Mann, R. W. (1968). Design criteria, development and pre-and post-fitting amputee evaluation of an EMG controlled, force sensing, proportional-rate, elbow prosthesis with cutaneous kinesthetic feedback. IFAC Proceedings Volumes, 2(4), 579–586.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Rothchild, R. D. (1965). Design of an externally powered artificial elbow forelectromyographic control. Cambridge, MA: MIT.Google Scholar
  23. 23.
    Rothchild, R. D., & Mann, R. W. (1966). An EMG controlled, force sensing,proportional rate, elbow prosthesis. In Proceedings of the Symposium on Biomedical Engineering (pp. 106–109). Milwaukee, WI: Marquette University.Google Scholar
  24. 24.
    Herberts, P. (1969). Myoelectric signals in control of prostheses: Studies on arm amputees and normal individuals. Acta Orthopaedica Scandinavica, 40(Suppl 124), 1–83.CrossRefGoogle Scholar
  25. 25.
    Scott, R. N. (1967). Myoelectric energy spectra. Medical and Biological Engineering, 3, 303–305.CrossRefGoogle Scholar
  26. 26.
    Dorcas, D. S., Dunfield, V. A., & Scott, R. M. (1970). Improved myoelectric control system. Medical and Biological Engineering, 8, 333–341.CrossRefGoogle Scholar
  27. 27.
    Kwatny, E., Thomas, D. H., & Kwatny, H. G. (1970). An application of signal processing techniques to the study of myoelectric signals. IEEE Transactions on Biomedical Engineering, BME-17(4), 303–313.CrossRefGoogle Scholar
  28. 28.
    Lawrence, P. D., & Lin, W. (1972). Statistical decision making in the real-time control of an arm aid for the disabled. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2(1), 35–42.  https://doi.org/10.1109/TSMC.1972.5408554.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Parker, P. A., Stuller, J. A., & Scott, R. N. (1977). Signal processing for the multistate myoelectric channel. Proceedings of the IEEE, 65(5), 662–674.CrossRefGoogle Scholar
  30. 30.
    De Luca, C. J. (1979). Physiological and mathematical basis of myoelectric signals. IEEE Transactions on Biomedical Engineering, BME-26(6), 313–325.CrossRefGoogle Scholar
  31. 31.
    Hogan, N., & Mann, R. W. (1980). Myoelectric signal processing: Optimal estimation applied to electromyography-part I: Derivation of the optimal myoprocessor. IEEE Transactions on Biomedical Engineering, BME-27(7), 382–395.CrossRefGoogle Scholar
  32. 32.
    Hudgins, B., Parker, P., & Scott, R. N. (1993). A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 40(1), 82–94.CrossRefGoogle Scholar
  33. 33.
    Chaiyaratana, N., Zalzala, A. M. S., & Datta, D. (1996). Myoelectric signals pattern recognition for intelligent functional operation of upper-limb prosthesis (ACSE Research Report 621). Department of Automatic Control and Systems Engineering.Google Scholar
  34. 34.
    Merletti, R., & Conte, L. R. L. (1997). Surface EMG signal processing during isometric contractions. Journal of Electromyography and Kinesiology, 7(4), 241–250.CrossRefGoogle Scholar
  35. 35.
    Bilodeau, M., Cincera, M., Arsenault, A. B., & Gravel, D. (1997). Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. Journal of Electromyography and Kinesiology, 7(2), 87–96.CrossRefGoogle Scholar
  36. 36.
    Clancy, E. A., & Hogan, N. (1999). Probability density of the surface electromyogram and its relation to amplitude detectors. IEEE Transactions on Biomedical Engineering, 46(6), 730–739.CrossRefGoogle Scholar
  37. 37.
    Farina, D., & Merletti, R. (2000). Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions. Journal of Electromyography and Kinesiology, 10(5), 337–349.CrossRefGoogle Scholar
  38. 38.
    Englehart, K., Hudgin, B., & Parker, P. A. (2001). A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 48(3), 302–311.CrossRefGoogle Scholar
  39. 39.
    Rosen, J., Brand, M., Fuchs, M. B., & Arcan, M. (2001). A myosignal-based powered exoskeleton system. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31(3), 210–222.CrossRefGoogle Scholar
  40. 40.
    Hussein, S. E., & Granat, M. H. (2002). Intention detection using a neuro-fuzzy EMG classifier. IEEE Engineering in Medicine and Biology Magazine, 21(6), 123–129.CrossRefGoogle Scholar
  41. 41.
    Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848–854.CrossRefGoogle Scholar
  42. 42.
    Ajiboye, A. B., & Weir, R. F. (2005). A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(3), 280–291.CrossRefGoogle Scholar
  43. 43.
    Huang, Y., Englehart, K. B., Hudgins, B., & Chan, A. (2005). A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Transactions on Biomedical Engineering, 52(11), 1801–1811.CrossRefGoogle Scholar
  44. 44.
    Chan, A., & Englehart, K. B. (2005). Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Transactions on Biomedical Engineering, 52(1), 121–124.CrossRefGoogle Scholar
  45. 45.
    Fleischer, C., Wege, A., Kondak, K., & Hommel, G. (2006). Application of EMG signals for controlling exoskeletonrobots. Biomedical Engineering, 51, 314–319.CrossRefGoogle Scholar
  46. 46.
    Reaz, M. B., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: Detection, processing, classification and applications. Biological Procedures Online, 8(1), 11–35.CrossRefGoogle Scholar
  47. 47.
    Oskoei, M. A., & Hu, H. (2006). GA-based feature subset selection for myoelectric classification. In 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China (pp. 1465–1470).Google Scholar
  48. 48.
    Oskoei, M. A., & Hu, H. (2008). Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Transactions on Biomedical Engineering, 55(8), 1956–1965.CrossRefGoogle Scholar
  49. 49.
    Hussain, M. S., Reaz, M. B. I., Mohd.-Yasin, F., & Ibrahimy, M. I. (2008). Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. The Journal of Knowledge Engineering, Expert Systems, 26(1), 35–48.CrossRefGoogle Scholar
  50. 50.
    Ahmad, S. A., & Chappell, P. H. (2009). Surface EMG pattern analysis of the wrist muscles at different speeds of contraction. Journal of Medical Engineering & Technology, 33(5), 376–385.CrossRefGoogle Scholar
  51. 51.
    Khezri, M., & Jahed, M. (2011). A neuro–fuzzy inference system for sEMG-based identification of hand motion commands. IEEE Transactions on Industrial Electronics, 58(5), 1952–1960.CrossRefGoogle Scholar
  52. 52.
    Lorrain, T., Jiang, N., & Farina, D. (2011). Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses. Journal of NeuroEngineering and Rehabilitation, 8(1), 25.CrossRefGoogle Scholar
  53. 53.
    Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420–7431.CrossRefGoogle Scholar
  54. 54.
    Matsubara, T., & Morimoto, J. (2013). Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface. IEEE Transactions on Biomedical Engineering, 60(8), 2205–2213.CrossRefGoogle Scholar
  55. 55.
    Subasi, A. (2013). Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Computers in Biology and Medicine, 43(5), 576–586.CrossRefGoogle Scholar
  56. 56.
    Phinyomark, A., et al. (2013). EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Systems with Applications, 40(12), 4832–4840.CrossRefGoogle Scholar
  57. 57.
    Rogers, D. R., & MacIsaac, D. T. (2013). A comparison of EMG-based muscle fatigue assessments during dynamic contractions. Journal of Electromyography and Kinesiology, 23(5), 1004–1011.CrossRefGoogle Scholar
  58. 58.
    Nazarpour, K., Al-Timemy, A. H., Bugmann, G., & Jackson, A. (2013). A note on the probability distribution function of the surface electromyogram signal. Brain Research Bulletin, 90, 88–91.CrossRefGoogle Scholar
  59. 59.
    Thongpanja, S., et al. (2015). Analysis of electromyography in dynamic hand motions using L-kurtosis. Applied Mechanics and Materials, 781, 604–607.CrossRefGoogle Scholar
  60. 60.
    Tsai, A.-C., et al. (2014). A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions. Biomedical Signal Processing and Control, 11, 17–26.CrossRefGoogle Scholar
  61. 61.
    Siddiqi, A. R., Sidek, S. N., & Khorshidtalab, A. (2015). Signal processing of EMG signal for continuous thumb-angle estimation. In 41st Annual Conference of the IEEE Industrial Electronics Society (IECON 2015), Yokohama, Japan (pp. 374–379).Google Scholar
  62. 62.
    Yu, Y., Fan, L., Kuang, S., Sun, L., & Zhang, F. (2015). The research of sEMG movement pattern classification based on multiple fused wavelet function. In IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China (pp. 487–491).Google Scholar
  63. 63.
    Kasuya, M., Yokoi, H., & Kato, R. (2015). Analysis and optimization of novel post-processing method for myoelectric pattern recognition. In 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, Singapore (pp. 985–990).Google Scholar
  64. 64.
    Peng, L., Hou, Z., Kasabov, N., Bian, G., Vladareanu, L., & Yu, H. (2015). Feasibility of NeuCube spiking neural network architecture for EMG pattern recognition. In 2015 International Conference on Advanced Mechatronic Systems (ICAMechS) (pp. 365–369).Google Scholar
  65. 65.
    Zhang, Q., Xiong, C., & Zheng, C. (2015). Intuitive motion classification from EMG for the 3-D arm motions coordinated by multiple DoFs. In 7th IEEE/EMBS International Conference on Neural Engineering (NER), Montpellier, France (pp. 836–839).Google Scholar
  66. 66.
    Pang, M., Guo, S., & Zhang, S. (2015). Prediction of interaction force using EMG for characteristic evaluation of touch and push motions. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany (pp. 2099–2104).Google Scholar
  67. 67.
    Naik, G. R., Selvan, S. E., & Nguyen, H. T. (2016). Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(7), 734–743.CrossRefGoogle Scholar
  68. 68.
    Spanias, J. A., Perreault, E. J., & Hargrove, L. J. (2016). Detection of and compensation for EMG disturbances for powered lower limb prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(2), 226–234.CrossRefGoogle Scholar
  69. 69.
    Vidovic, M. M., Hwang, H., Amsüss, S., Hahne, J. M., Farina, D., & Müller, K. (2016). Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(9), 961–970.CrossRefGoogle Scholar
  70. 70.
    AbdelMaseeh, M., Chen, T., & Stashuk, D. W. (2016). Extraction and classification of multichannel electromyographic activation trajectories for hand movement recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(6), 662–673.CrossRefGoogle Scholar
  71. 71.
    Samuel, O. W., Li, X., Fang, P., & Li, G. (2015). Examining the effect of subjects' mobility on upper-limb motion identification based on EMG-pattern recognition. In 2016 Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) (pp. 137–141).Google Scholar
  72. 72.
    Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C. (2016). Short latency hand movement classification based on surface EMG spectrogram with PCA. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA (pp. 327–330).Google Scholar
  73. 73.
    Lee, S. W., Yi, T., Jung, J., & Bien, Z. (2017). Design of a gait phase recognition system that can cope with EMG electrode location variation. IEEE Transactions on Automation Science and Engineering, 14(3), 1429–1439.CrossRefGoogle Scholar
  74. 74.
    Jochumsen, M., Waris, A., & Kamavuako, E. N. (2018). The effect of arm position on classification of hand gestures with intramuscular EMG. Biomedical Signal Processing and Control, 43, 1–8.CrossRefGoogle Scholar
  75. 75.
    Tavakoli, M., Benussi, C., Lopes, P. A., Osorio, L. B., & de Almeida, A. T. (2018). Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier. Biomedical Signal Processing and Control, 46, 121–130.CrossRefGoogle Scholar
  76. 76.
    Camargo, J., & Young, A. (2019). Feature selection and non-linear classifiers: Effects on simultaneous motion recognition in upper limb. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 743–750.  https://doi.org/10.1109/TNSRE.2019.2903986.CrossRefGoogle Scholar
  77. 77.
    Zschorlich, V. (1989). Digital filtering of EMG-signals. Electromyography and Clinical Neurophysiology, 28(2), 81–86.Google Scholar
  78. 78.
    Conforto, S., D’Alessio, T., & Pignatelli, S. (1999). Optimal rejection of movement artefacts from myoelectric signals by means of a wavelet filtering procedure. Journal of Electromyography and Kinesiology, 9(1), 47–57.CrossRefGoogle Scholar
  79. 79.
    De Luca, C. J., Gilmore, L. D., Kuznetsov, M., & Roy, S. H. (2010). Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics, 43(8), 1573–1579.CrossRefGoogle Scholar
  80. 80.
    Ghalyan, I. F. J. (2016). Force-controlled robotic assembly processes of rigid and flexible objects: Methodologies and applications (1st ed.). Cham: Springer International Publishing.Google Scholar
  81. 81.
    Jasim, I. F., Plapper, P. W., & Voos, H. (2015). Gaussian filtering for enhanced impedance parameters identification in robotic assembly processes. In 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), Luxembourg, Luxembourg.  https://doi.org/10.1109/ETFA.2015.7301611
  82. 82.
    Ghalyan, I. F., Jaydeep, A., & Kapila, V. (2018). Learning robot-object distance using Bayesian regression with application to a collision avoidance scenario. In 48th IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2018), Washington, DC, USA.Google Scholar
  83. 83.
    Shapiro, L. G., & Stockman, G. (2001). Computer vision (1st ed.). Upper Saddle River, NJ: Prentice Hall PTR.Google Scholar
  84. 84.
    Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.zbMATHGoogle Scholar
  85. 85.
    Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.zbMATHCrossRefGoogle Scholar
  86. 86.
    Khan, M., Ahamed, S. I., Rahman, M., & Yang, J. (2012). Gesthaar: An accelerometer-based gesture recognition method and its application in NUI driven pervasive healthcare. In 2012 IEEE International Conference on Emerging Signal Processing Applications, Las Vegas, NV, USA (pp. 163–166).Google Scholar
  87. 87.
    Rahulamathavan, Y., Veluru, S., Phan, R. C., Chambers, J. A., & Rajarajan, M. (2014). Privacy-preserving clinical decision support system using Gaussian kernel-based classification. IEEE Journal of Biomedical and Health Informatics, 18(1), 56–66.CrossRefGoogle Scholar
  88. 88.
    Chen, S., Ouyang, Y., Lin, C., & Chang, C. (2018). Iterative support vector machine for hyperspectral image classification. In 25th IEEE International Conference on Image Processing (ICIP), Vancouver, BC, Canada (pp. 3309–3312).  https://doi.org/10.1109/ICIP.2018.8451145
  89. 89.
    Ghalyan, I. F., Chacko, S. M., & Kapila, V. (2018). Simultaneous robustness against random initialization and optimal order selection in Bag-of-Words modeling. Pattern Recognition Letters, 116, 135–142.CrossRefGoogle Scholar
  90. 90.
    Vapnik, V. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer.zbMATHCrossRefGoogle Scholar
  91. 91.
    Yang, K., & Shahabi, C. (2007). An efficient k nearest neighbor search for multivariate time series. Information and Computation, 205(1), 65–98.MathSciNetzbMATHCrossRefGoogle Scholar
  92. 92.
    Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2013). Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology, 10, 85–94.CrossRefGoogle Scholar
  93. 93.
    Krishna, A., Edwin, D., & Hariharan, S. (2017). Classification of liver tumor using SFTA based Naïve Bayes classifier and support vector machine. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India (pp. 1066–1070).Google Scholar
  94. 94.
    Padmavathi, S., & Ramanujam, E. (2015). Naïve Bayes classifier for ECG abnormalities using multivariate maximal time series motif. Procedia Computer Science, 47, 222–228.CrossRefGoogle Scholar
  95. 95.
    Falih, A. D. I., Dharma, W. A., & Sumpeno, S. (2017). Classification of EMG signals from forearm muscles as automatic control using Naive Bayes. In 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia (pp. 346–351).Google Scholar
  96. 96.
    Zhang, D., Zhao, X., Han, J., & Zhao, Y. (2014). A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand. In IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong (pp. 4850–4855).Google Scholar
  97. 97.
    Sharma, A., & Paliwal, K. K. (2008). Cancer classification by gradient LDA technique using microarray gene expression data. Data & Knowledge Engineering, 66(2), 338–347.CrossRefGoogle Scholar
  98. 98.
    Bandos, T. V., Bruzzone, L., & Camps-Valls, G. (2009). Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 862–873.CrossRefGoogle Scholar
  99. 99.
    Jasim, I. F., & Plapper, P. W. (2014). Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based Gaussian mixtures models. The International Journal of Advanced Manufacturing Technology, 73(5–8), 623–633. Retrieved from http://link.springer.com/article/10.1007%2Fs00170-014-5803-x.CrossRefGoogle Scholar
  100. 100.
    Jasim, I. F., & Plapper, P. W. (2014). Contact-state recognition of compliant motion robots using expectation maximization-based Gaussian Mixtures. In Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014), Munich, Germany.Google Scholar
  101. 101.
    Jasim, I. F., Plapper, P. W., & Voos, H. (2017). Contact-state modelling in force-controlled robotic peg-in-hole assembly processes of flexible objects using optimised Gaussian mixtures. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(8), 1448–1463.  https://doi.org/10.1177/0954405415598945.CrossRefGoogle Scholar
  102. 102.
    Chu, J., & Lee, Y. (2009). Conjugate-prior-penalized learning of Gaussian mixture models for multifunction myoelectric hand control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(3), 287–297.CrossRefGoogle Scholar
  103. 103.
    Vögele, A. M., Zsoldos, R. R., Krüger, B., & Licka, T. (2016). Novel methods for surface EMG analysis and exploration based on multi-modal Gaussian mixture models. PLoS One, 11(6), 1–28.CrossRefGoogle Scholar
  104. 104.
    Lorentz, G. G. (1966). Approximation of functions. New York: Holt-Rinehart-Winston.zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ibrahim F. J. Ghalyan
    • 1
    Email author
  • Ziyad M. Abouelenin
    • 1
  • Gnanapoongkothai Annamalai
    • 1
  • Vikram Kapila
    • 1
  1. 1.Department of Mechanical and Aerospace EngineeringNYU Tandon School of Engineering, Six Metrotech CenterBrooklynUSA

Personalised recommendations