Abstract
This paper investigates the processing of surface electromyographic (sEMG) signals collected from the forearm of a human subject and, based on which, a control strategy is developed for an exoskeleton arm. In this study, we map the motion of elbow and wrist to the corresponding joints of an exoskeleton arm. Linear Discriminant Analysis (LDA) based classifiers are introduced as the indicator of the motion type of the joints, and then with the force of corresponding agonist muscles the control signal is produced. In the strategy, which is different from the conventional method, we assign one classifier for each joint, decomposing the motion of the two joints into independent parts, making the recognition of the forearm motion a combination of the results of different joints. In addition, training time is reduced and recognition of motion is simplified.
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References
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)
Au, A.T.C., Kirsch, R.F.: EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals. IEEE Trans. Rehabil. Eng. 8(4), 471–480 (2000)
Huang, Y., Englehart, K., Hudgins, B., Chan, A.D.C.: A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans. Biomed. Eng. 52(11), 1801–1811 (2005)
Oskoei, M.A., Hu, H.: Support Vector Machine-based Classification Scheme for Myoelectric Control Applied to Upper Limb. IEEE Trans. Biomed. Eng. 55(8), 1956–1965 (2008)
Shenoy, P., Miller, K.J., Crawford, B., Rao, R.P.N.: Online Electromyographic Control of a Robotic Prosthesis. IEEE Trans. Biomed. Eng. 55(3), 1128–1135 (2008)
Artemiadis, P.K., Kyriakopoulos, K.J.: Estimating Arm Motion and Force using EMG signals: On the Control of Exoskeletons. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (September 2008)
Kuan, J., Huang, T., Huang, H.: Human Intention Estimation Method for a New Compliant Rehabilitation and Assistive Robot. In: SICE Annual Conference, The Grand Hotel, Taipei, Taiwan, August 18-21 (2010)
Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J. Biomech., 765–776 (2003)
Hayashibe, M., Guiraud, D., Poignet, P.: EMG-to-force estimation with full-scale physiology based muscle model. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, October 11-15 (2009)
Nakano, T., Nagata, K., Yamada, M., Magatani, K.: Application of least square method for muscular strength estimation in hand motion recognition using surface EMG. In: International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, September 2-6 (2009)
Hoozemans, M.J.M., van Dieen, J.H.: Prediction of handgrip forces using surface EMG of forearm muscles. J. Electromyogr. Kinesiol., 358–366 (2005)
Potvin, J.R., Brown, S.H.M.: Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates. J. Electromyogr. Kinesiol., 389–399 (2004)
Ye, J., Li, T., Xiong, T., Janardan, R.: Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data. IEEE Trans. Comput. Biol. Bioinf. 1(4) (2004)
Lorrain, T., Jiang, N., Farina, D.: Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses. Journal of Neuro Engineering and Rehabilitation (2011)
Scheme, E.J., Englehart, K.B., Hudgins, B.S.: Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions. IEEE Trans. Biomed. Eng. 58(6), 1698–1705 (2011)
Potvin, J.R., Norman, R.W., McGill, S.M.: Mechanically corrected EMG for the continuous estimation of erector spine muscle loading during repetitive lifting. Eur. J. Appl. Physiol. 74, 119–132 (1996)
Hudgins, B., Parker, P., Scott, R.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82–94 (1993)
Tkach, D., Huang, H., Kuiken, T.A.: Study of stability of time-domain features for electromyographic pattern recognition. Journal of NeuroEngineering and Rehabilitation (2010)
Farina, D., Merletti, R.: Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions. J. Electromyogr. Kinesiol. 10, 337–349 (2000)
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Wang, B., Yang, C., Li, Z., Smith, A. (2012). sEMG-Based Control of an Exoskeleton Robot Arm. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33515-0_7
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DOI: https://doi.org/10.1007/978-3-642-33515-0_7
Publisher Name: Springer, Berlin, Heidelberg
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