Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32722
Electromyography Pattern Classification with Laplacian Eigenmaps in Human Running

Authors: Elnaz Lashgari, Emel Demircan


Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: Electrocardiogram, manifold learning, Laplacian Eigenmaps, running pattern.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1035


[1] Z. Kourtzi, H. H. Bülthoff, M. Erb, and W. Grodd, "Object-selective responses in the human motion area MT/MST," Nature neuroscience, vol. 5, pp. 17-18, 2002.
[2] D. B. Chaffin, G. Andersson, and B. J. Martin, Occupational biomechanics: Wiley New York, 1999.
[3] A. Bashashati, M. Fatourechi, R. K. Ward, and G. E. Birch, "A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals," Journal of Neural engineering, vol. 4, p. R32, 2007.
[4] O. Khatib, E. Demircan, V. De Sapio, L. Sentis, T. Besier, and S. Delp, "Robotics-based synthesis of human motion," Journal of Physiology-Paris, vol. 103, pp. 211-219, 2009.
[5] N. S. Pollard, J. K. Hodgins, M. J. Riley, and C. G. Atkeson, "Adapting human motion for the control of a humanoid robot," in Robotics and Automation, 2002. Proceedings. ICRA'02. IEEE International Conference on, 2002, pp. 1390-1397.
[6] S. K. Au, P. Bonato, and H. Herr, "An EMG-position controlled system for an active ankle-foot prosthesis: an initial experimental study," in Rehabilitation robotics, 2005. ICORR 2005. 9th international conference on, 2005, pp. 375-379.
[7] S. Kim, J. E. Clark, and M. R. Cutkosky, "iSprawl: Design and tuning for high-speed autonomous open-loop running," The International Journal of Robotics Research, vol. 25, pp. 903-912, 2006.
[8] R. Jimenez-Fabian and O. Verlinden, "Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons," Medical engineering & physics, vol. 34, pp. 397-408, 2012.
[9] M. A. Oskoei and H. Hu, "Myoelectric control systems—A survey," Biomedical Signal Processing and Control, vol. 2, pp. 275-294, 2007.
[10] M. G. Gazendam and A. L. Hof, "Averaged EMG profiles in jogging and running at different speeds," Gait & posture, vol. 25, pp. 604-614, 2007.
[11] A. Hof, H. Elzinga, W. Grimmius, and J. Halbertsma, "Speed dependence of averaged EMG profiles in walking," Gait & posture, vol. 16, pp. 78-86, 2002.
[12] D. Winter and H. Yack, "EMG profiles during normal human walking: stride-to-stride and inter-subject variability," Electroencephalography and clinical neurophysiology, vol. 67, pp. 402-411, 1987.
[13] R. H. Gabel and R. A. Brand, "The effects of signal conditioning on the statistical analyses of gait EMG," Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, vol. 93, pp. 188-201, 1994.
[14] V. von Tscharner, B. Goepfert, and B. M. Nigg, "Changes in EMG signals for the muscle tibialis anterior while running barefoot or with shoes resolved by non-linearly scaled wavelets," Journal of biomechanics, vol. 36, pp. 1169-1176, 2003.
[15] T. F. Novacheck, "The biomechanics of running," Gait & posture, vol. 7, pp. 77-95, 1998.
[16] B. Chen and N. Wan, "Determining EMG embedding and fractal dimensions and its application," in Engineering in medicine and biology society, 2000. Proceedings of the 22nd annual international conference of the IEEE, 2000, pp. 1341-1344.
[17] H. Kyröläinen, P. V. Komi, and A. Belli, "Changes in Muscle Activity Patterns and Kinetics With Increasing Running Speed," The Journal of Strength & Conditioning Research, vol. 13, pp. 400-406, 1999.
[18] J. B. Tenenbaum, V. De Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," science, vol. 290, pp. 2319-2323, 2000.
[19] M. Belkin and P. Niyogi, "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering," in NIPS, 2001, pp. 585-591.
[20] S. R. Hamner and S. L. Delp, "Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds," Journal of biomechanics, vol. 46, pp. 780-787, 2013.
[21] G. Lu, J.-S. Brittain, P. Holland, J. Yianni, A. L. Green, J. F. Stein, et al., "Removing ECG noise from surface EMG signals using adaptive filtering," Neuroscience letters, vol. 462, pp. 14-19, 2009.
[22] C. Marque, C. Bisch, R. Dantas, S. Elayoubi, V. Brosse, and C. Perot, "Adaptive filtering for ECG rejection from surface EMG recordings," Journal of electromyography and kinesiology, vol. 15, pp. 310-315, 2005.
[23] E. Lashgari, M. Jahed, and B. Khalaj, "Manifold learning for ECG arrhythmia recognition," in Biomedical Engineering (ICBME), 2013 20th Iranian Conference on, 2013, pp. 126-131.
[24] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, "Classification of the myoelectric signal using time-frequency based representations," Medical engineering & physics, vol. 21, pp. 431-438, 1999.
[25] A. Phinyomark, P. Phukpattaranont, and C. Limsakul, "Feature reduction and selection for EMG signal classification," Expert Systems with Applications, vol. 39, pp. 7420-7431, 2012.
[26] D. De Ridder and R. P. Duin, "Locally linear embedding for classification," Pattern Recognition Group, Dept. of Imaging Science & Technology, Delft University of Technology, Delft, The Netherlands, Tech. Rep. PH-2002-01, pp. 1-12, 2002.
[27] T. M. Cover and P. E. Hart, "Nearest neighbor pattern classification," Information Theory, IEEE Transactions on, vol. 13, pp. 21-27, 1967.
[28] B. Scholkopft and K.-R. Mullert, "Fisher discriminant analysis with kernels," Neural networks for signal processing IX, vol. 1, p. 1, 1999.
[29] G. H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers," in Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, 1995, pp. 338-345.
[30] P. Langley and S. Sage, "Induction of selective Bayesian classifiers," in Proceedings of the Tenth international conference on Uncertainty in artificial intelligence, 1994, pp. 399-406.
[31] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, pp. 1137-1145.
[32] Scholkmann, F., Boss, J. and Wolf, M., 2012. An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals.Algorithms, 5(4), pp.588-603.