Abstract
As a typical biomedical signal, the electromyography (EMG) is now widely used as a human-machine interface in the control of robotic rehabilitation devices such as prosthetic hands and legs. Immediately detecting and eliciting of a valid EMG signal are greatly anticipated for ensuring a fast-response and high-precision EMG control scheme. This paper utilizes two schemes, Teager-Kaise Engergy (TKE) operator and Morphological Close Operation (MCO), to improve the accuracy of the onset/offset detection of EMG activities. The TKE operator is used to amplify the EMG signal’s amplitude change on the initiation/cessation phases, while the MCO is adopted to filter out the false positives of the binary sequence obtained by the fore TKE operation. This method is simple and easily to be implemented. After selecting appropriate filtering parameters (T 1, T 2 and j), it can achieve precise onset detection (absolute error <10ms) over a variety of signal-to-noise ratios (SNR) of the biomedical signal.
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References
Luca, C.J.D.: The use of surface eletromyography in biomechanics. Journal of Applied Biomechanics 13, 135–163 (1997)
Staude, G.H.: Precise onset detection of human motor responses using a whitening filter and the log-likelihood-ratio test. IEEE Trans. Biomed. Eng. 48, 1292–1305 (2001)
Maeda, Y., Tanaka, T., Nakajima, Y., Shimizu, K.: Analysis of Postural Adjustment Responses to Perturbation Stimulus by Surface Tilts in the Feet-together Position. Journal of Medical and Biological Engineering 31, 301–305 (2011)
Wentink, E.C., Beijen, S.I., Hermens, H.J., Rietman, J.S., Veltink, P.H.: Intention detection of gait initiation using EMG and kinematic data. Gait & Posture 37, 223–228 (2013)
Difabio, R.P.: Reliability of Computerised Surface Electromyography for Determining the Onset of Muscle Activity. Phys. Ther. 67, 43–48 (1987)
Englehart, K., Hudgins, B.: A Robust, Real-Time Control Scheme for Multifunction Myoelectric Control. IEEE Transaction on Biomedical Engineering 50, 848–854 (2003)
Zecca, M., Micera, S., Carrozza, M.C., Dario, P.: Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering 30, 459–485 (2002)
Scheme, E., Englehart, K.: Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. Journal of Rehabilitation Research and Development 48, 643–660 (2011)
Oskoei, M.A., Hu, H.: Myoelectric control systems—A survey. Biomedical Signal Processing and Control 2, 275–294 (2007)
Hargrove, L., Losier, Y., Lock, B., Englehart, K., Hudgins, B.: A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. In: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society. EMBS 2007, pp. 4842–4845. IEEE Press, New York (2007)
Peerdeman, B., Boere, D., Witteveen, H., in’t Veld, R.H., Hermens, H., Stramigioli, S., Rietman, H., Veltink, P., Misra, S.: Myoelectric forearm prostheses: State of the art from a user-centered perspective. Journal of Rehabilitation Research and Development 48, 719–737 (2011)
Oskoei, M.A., Hu, H.: Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb. IEEE Trans. Biomed. Eng. 55, 1956–1965 (2008)
Bitzer, S., van der Smagt, P.: Learning EMG control of a robotic hand: towards active prostheses. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2819–2823. IEEE Press, New York (2006)
Hodges, P.W., Bui, B.H.: A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography. Electromyography and Motor Control-Electroencephalography and Clinical Neurophysiology 101, 511–519 (1996)
Bonato, P., D’Alessio, T., Knaflitz, M.: A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Trans. Biomed. Eng. 45, 287–299 (1998)
Merlo, A., Farina, D., Merletti, R.: A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Transactions on Biomedical Engineering 50, 316–323 (2003)
Lee, J., Shim, H., Lee, H., Lee, Y., Yoon, Y.: Detection of onset and offset time of muscle activity in surface EMGs using the Kalman smoother. In: World Congress on Medical Physics and Biomedical Engineering 2006 IFMBE Proceedings, pp. 1103–1106
Xu, Q., Quan, Y.Z., Yang, L., He, J.P.: An Adaptive Algorithm for the Determination of the Onset and Offset of Muscle Contraction by EMG Signal Processing. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 65–73 (2013)
Teager, H.M.: Evidence for Nonlinear Sound Reduction Mechanisms in the Vocal Tract, pp. 241–261. Kluwer Acad Publ (1990)
Lemyre, C., Jelinek, M., Lefebvre, R.: New approach to voiced onset detection in speech signal and its application for frame error concealment. In: IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2008, pp. 4757–4760. IEEE, Las Vegas (2008)
Li, X.Y., Zhou, P., Aruin, A.S.: Teager-Kaiser energy operation of surface EMG improves muscle activity onset detection. Annals of Biomedical Engineering 35, 1532–1538 (2007)
Solnik, S., DeVita, P., Rider, P., Long, B., Hortobágyi, T.: Teager-Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio. Acta Bioeng Biomech 10, 65 (2008)
Hamilton-Wright, A., Stashuk, D.W.: Physiologically based simulation of clinical EMG signals. IEEE Trans Biomed Eng 52, 171–183 (2005)
Duchêne, J., Hogrel, J.-Y.: A Model of EMG Generation. IEEE Trans. Biomed. Eng. 47, 192–201 (2000)
Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y.: EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Systems with Applications 40, 4832–4840 (2013)
Staude, G., Flachenecker, C., Daumer, M., Wolf, W.: Onset Detection in Surface Electromyographic Signals: A Systematic Comparison of Methods. EURASIP Journal on Applied Signal Processing 2001, 67–81 (2001)
Lidierth, M.: A Computer based Method for Automated Measurement of the Periods of Muscular Activity from an EMG and its Application to Locomotor Emgs. Electroenceph. Clin. Neurophysiol. 64, 378–380 (1986)
Schluter, J., Bock, S.: Improved Musical Onset Detection with Convolutional Neural Networks, pp. 6979–6983. Florence, Italy (2014)
Taylor, C.L., Schwarz, R.J.: The anatomy and mechanics of the human hand. Artificial limbs 2, 22–35 (1955)
Bowman, A.W., Azzalini, A.: Applied Smoothing Techniques for Data Analysis. Oxford University Press, New York (1997)
Englehart, K., Hudgins, B., Parker, P.A.: A Wavelet-Based Continuous Classification Scheme for Multifunction Myoelectric Control. IEEE Trans. Biomed. Eng. 48, 302–311 (2001)
Yang, D., Zhao, J., Jiang, L., Liu, H.: Dynamic hand motion recognition based on transient and steady-state EMG signals. International Journal of Humanoid Robotics 9, 11250007 (2012)
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Yang, D., Huang, Q., Yang, W., Liu, H. (2015). EMG Onset Detection Based on Teager–Kaiser Energy Operator and Morphological Close Operation. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_24
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DOI: https://doi.org/10.1007/978-3-319-22879-2_24
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