Extreme learning machine classification method for lower limb movement recognition
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In order to identify the lower limb movements accurately and quickly, a recognition method based on extreme learning machine (ELM) is proposed. The recognizing target set is constructed by decomposing the daily actions into different segments. To get the recognition accuracy of seven movements based on the surface electromyography, the recognition feature vector space is established by integrating short-time statistical characteristics under time domain, and locally linear embedding algorithm is used to reduce the computational complexity and improve robustness of algorithm. Compared with BP, the overall recognition accuracy for each subject in the best dimension with ELM is above 95%.
KeywordsMovement recognition Surface EMG ELM-LLE Multi-classification
The authors would like to gratefully acknowledge the reviewers’ comments. This work is supported by Jiangxi Science and Technology Plan (20161BBE50058), Science and Technology Major Project of Zhejiang province (2013C03017-1), Taizhou Science and Technology Plan (13ZJU007).
- 2.Chen, Y., Yang, C.J.: The human-machine intelligent system. Zhejiang University Press Inc., Hangzhou (2006)Google Scholar
- 3.Bulea, T.C., Prasad, S., Kilicarslan, A., Contrerasvidal, J.L.: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6341–6344. doi: 10.1109/EMBC.2013.6611004
- 12.Chen, Y.Z., Zhou, Y.Q., Cheng, X.L., Mi, Y.Z.: Upper limb motion recognition based on two-step SVM classification method of surface EMG. Int. J. Control. Autom. 6(3), 249–265 (2013)Google Scholar
- 14.Naeem, U.J., Xiong, C.H., Abdullah, A.A.: EMG-muscle force estimation model based on back-propagation neural network. In: IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems, pp. 222–227 (2012). doi: 10.1109/VECIMS.2012.6273225
- 16.Kiguchi, K.: A study on EMG-based human motion prediction for power assist exoskeletons. In: International Symposium on Computational Intelligence in Robotics and Automation, pp. 190–195 (2007). doi: 10.1109/CIRA.2007.382917
- 19.Cao, J.W., Xiong, L.L.: Protein sequence classification with improved extreme learning machine algorithms. Biomed Res. Int. 2014(1), 660–677 (2014)Google Scholar
- 22.Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by local linear embedding. Science 290(5), 2323–2326 (2012)Google Scholar
- 24.Wu, J.F.: Research on Information Acquisition Technology for Human Lower Limb Movement Based on EMG Signals. Zhejiang University, Hangzhou (2008)Google Scholar
- 27.Lawrence, K.S., Sam, T.R.: Nonlinear dimensionality reduction by locally linear embedding. Science. 290(12), 2323–2326 (2000)Google Scholar