Open Access
Issue
MATEC Web Conf.
Volume 292, 2019
23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
Article Number 04002
Number of page(s) 6
Section Signal Processing
DOI https://doi.org/10.1051/matecconf/201929204002
Published online 24 September 2019
  1. D. I. Rubin, J. R. Daube, Rapid MUAP quantization, in: AANEM Workshop, 2008, pp. 1–7. [Google Scholar]
  2. Raez, M. B., Hussain, M. S., Mohd-Yasin, F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biological Procedures Online, 8, 11-35 (2006). [CrossRef] [Google Scholar]
  3. Y. S. Shmaliy, S. Zhao, C. K. Ahn, Unbiased FIR filtering: an iterative alternative to Kalman filtering ignoring noise and initial conditions, IEEE Control Syst Mag 37 (5) (2017) 70–89. [Google Scholar]
  4. J. Pomarico-Franquiz, S. Khan, Y.S. Shmaliy, Combined extended FIR/Kalman filtering for indoor robot localization via triangulation, Measurement, 50 (2014) 236–242. [CrossRef] [Google Scholar]
  5. C. K. Ahn, Y. S. Shmaliy, P. Shi, Y. Zhao, Receding horizon l2-l FIR filter with imbedded deadbeat property, IEEE Trans. on Circuits and Systems-II: Express Briefs, 64 (2) (2017) 211-215. [CrossRef] [Google Scholar]
  6. Y. Xu, Y. S. Shmaliy, Y. Li, X. Chen, UWB-based indoor human localization with time-delayed data using EFIR filtering, IEEE Access, 5 (2017) 16676–16683. [CrossRef] [Google Scholar]
  7. Y. S. Shmaliy, On real-time optimal FIR estimation of linear TIE models of local clocks, IEEE Trans. on Usltrason., Ferroel., and Freg. Control, 54 (11) (2007) 2403-2406. [CrossRef] [Google Scholar]
  8. R. M. Studer, R. J. P. Figueiredo, G. S. Moschytz, An algorithm for sequential signal estimation and system identification for EMG signals, IEEE Trans. Biomed. Eng. 31 (3) (1984) 285–295. [CrossRef] [Google Scholar]
  9. S. Schiaffino, C. Reggian, Fiber types in mammalian skeletal muscles, Physiological Review 91 (4) (2011) 1447–1531. [CrossRef] [PubMed] [Google Scholar]
  10. A. J. Fuglevand, D. A. Winter, A. E. Patla, D. Stashuk, Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing, Biological Cybernetics 67 (2) (1992) 143–153. [CrossRef] [Google Scholar]
  11. D. Farina, A. Crosetti, R. Merletti, A model for the generation of synthetic intramuscular EMG signals to test decomposition algorithms, IEEE Eng. Medicine Biology Soc. 48 (2001) 66 – 77. [CrossRef] [Google Scholar]
  12. Hamilton-Wright. A, Stashuk.D.W., Physiologically based simu lation of clinical EMG signals, IEEE Trans. Biomed. Eng. 52 (2)(2005) 11–35. [CrossRef] [Google Scholar]
  13. S. N. Kale, S. V. Dudul, Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network, Appl. Comp. Intell. Soft Comput. 2009 (1) 1–9. [Google Scholar]
  14. S. Thongpanja, A. Phinyomark, F. Quaine, et al., Probability density functions of stationary surface EMG signals in noisy environments, IEEE Trans. Instrum. Measur. 63 (6) (2016) 1547–1557. [CrossRef] [Google Scholar]
  15. G. Tallison, P. Godfrey, G. Robinson, EMG signal amplitude assessment during abdominal bracing and hollowing, J. Electromyography Kinesiology 8 (1) (1996) 51–57. [CrossRef] [Google Scholar]
  16. G. Jang, J. Kim, S. Lee, Y. Choi, EMG-based continuous control scheme with simple classifier for electricpowered wheelchair, IEEE Trans. Industr. Electron. 65 (7) (2016) 3695–3705. [CrossRef] [Google Scholar]
  17. L. Chen, Y. Hao, Feature extraction and classification of EHG between pregnancy and labour group using Hilbert-Huang transform and extreme learning machine, Comput Math Methods Med 2017 (7949507) (2017) 1–9. [Google Scholar]
  18. R. D’Alessio, S. Conforto, Extraction of the envelope from surface emg signals, IEEE Eng. Medicine Biology 20 (6) (2001) 55–61. [CrossRef] [Google Scholar]
  19. H. Xie, Z. Wang, Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis, Computer Methods and Programs in Biomedicine 82 (2) (2006) 312–320. [Google Scholar]
  20. R. F. M. Kleissen, G. Zilvold, Estimation uncertainty in ensemble averaged surface EMG profiles during gait, J. Electromyography Kinesiology 4 (2) (1994) 83–94. [CrossRef] [Google Scholar]
  21. H. W. V. Yeh, C. H. Young, C. Y. Wang, et al., Quantifying sasticity with limited swinging cycles using pendulum test based on phase amplitude coupling, IEEE Trans. Neural Systems Rehabil. Eng. 24 (10) (2016) 1081–1088. [CrossRef] [Google Scholar]
  22. L. Ramíırez, M. Ruano, C. Younes, Anáalisis de bioseñales: Enfoque téecnico en el anáalisis clíınico de señnales fonocardiográaficas, 1st Edition, Univ. Nacional de Colombia, 2016. [Google Scholar]
  23. L. Myers, M. Lowery, M. OMalley, et al., Rectification and non-linear pre-processing of EMG signals for cortico-muscular analysis, J. Neuroscience Methods 124 (2) (2003) 157–165. [CrossRef] [Google Scholar]
  24. T. J. Roberts, G. A. M., Interpreting muscle function from EMG: lessons learned from direct measurements of muscle force, Integrative and Comparative Biology 48 (2) (2008) 312–320. [CrossRef] [Google Scholar]
  25. Y. Zhan, S. Guo, K. M. Kendrick, J. Feng, Filtering noise for synchronised activity in multi-trial electrophysiology data using Wiener and Kalman filters, BioSystems 96 (1) (2009) 1–13. [CrossRef] [Google Scholar]
  26. N. M. Lopez, F. diSciascio, C. M. Soria, M. E. Valentinuzzi, Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm, BioMedical Eng OnLine 8 (5) (2009) 1–13. [CrossRef] [Google Scholar]
  27. C. S. L. Tsui, J. Q. Gan, S. J. Roberts, A self-paced braincomputer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training, Med Biol Eng Comput 47 (3) (2009) 257–265. [CrossRef] [Google Scholar]
  28. L. L. Menegaldo, Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters, Biol Cybern 111 (5-7) (2017) 335–346. [CrossRef] [Google Scholar]
  29. T. Triwiyantoa, O. Wahyunggoroa, H. A. Nugrohoa, H. Herianto, Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature, Computers Electr. Eng. 71 (2018) 284–293. [CrossRef] [Google Scholar]
  30. Y. S. Shmaliy, F. Lehmann, S. Zhao, C. K. Ahn, Comparing robustness of the Kalman, H, and UFIR filters, IEEE Trans. Signal Process. 66 (13) (2018) 3447–3458. [CrossRef] [Google Scholar]
  31. G. De Luca, Fundamental Concepts in EMG Signal Acquisition, DelSys Inc., 2003. [Google Scholar]
  32. J. C. Ives, J. K. Wigglesworth, Sampling rate effects on surface EMG timing and amplitude measures, Clinical Biomechanics 18 (6) (2003) 543–552. [CrossRef] [Google Scholar]
  33. Y. S. Shmaliy, Unbiased FIR filtering of discrete time polynomial state space models, IEEE Trans. Signal Process, 57 (4) (2009) 1241–1249. [CrossRef] [Google Scholar]
  34. Y. S. Shmaliy, A. V. Marienko, A. V. Savchuk, GPS-based optimal Kalman estimation of time error, frequency offset, and aging, in: 31st PTTI Meeting, 1999, pp. 431–440. [Google Scholar]
  35. A. E. Bryson, L. J. Henrikson, Estimation using sampled data containing sequentially correlated noise, J. Spacecraft Rockets 5 (6) (1968) 662–665. [CrossRef] [Google Scholar]
  36. Y. S. Shmaliy, S. Khan, S. Zhao, Ultimate iterative unbiased FIR filtering algorithm, Measurement, 92 (2016) 236–242. [CrossRef] [Google Scholar]
  37. Y. S. Shmaliy, S. H. Khan, S. Zhao, O. Ibarra-Manzano, General unbiased FIR filter with applications to GPS-based steering of oscillator frequency, IEEE Trans. on Control Systems Technology, 25 (3) (2017) 1141-1148. [CrossRef] [Google Scholar]
  38. O. Aviles, J. Rodriguez, M. Herrera, G. Martinez, UCI Machine Learning Repository. Bogota: Iniversidad Militar Nueva Granada, Tecno Parque SENA nodo Manizales. (2012). URL http://archive.ics.uci.edu/ml/datasets/emg+dataset+in+lower+limb [Google Scholar]
  39. D. Dua, E. Karra Taniskidou, UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. (2017). URL http://archive.ics.uci.edu/ml [Google Scholar]
  40. M. Atzori, A. Gijsberts, C. Castellini, at al, Electromyography data for non-invasive naturally controlled robotic hand protheses. Scientific Data 1:140053 (2014). URL https://doi.org/10.1038/sdata.2014.53 [Google Scholar]

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