Abstract
A general scheme of automated discrimination of gait patterns based on recognition of surface electromyogram of lower limbs is proposed to classify three different terrains and six different movement patterns. To verify the effectiveness of different feature extraction methods, time–frequency features such as RMS and MF, wavelet variance and matrix singularity value are employed to process the sEMG signals under different conditions. SVM is used to discriminate gait patterns based on the selected features. Comparison results indicate that feature extraction method based on matrix singularity value can obtain better results and over 92.5 % classification accuracy ratio can be achieved. Experimental result indicates the rationality and effectiveness of the proposed methods for feature extraction and pattern classification. The proposed scheme shows great potential in the application of lower limb assistance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Massimo S (2009) A lower limb EMG-driven biomechanical model for applications in rehabilitation robotics. In: Proceedings of the 14th International Conference on Advanced Robotics 2:905–911
Balestra G, Frassinelli S (2001) Time-frequency analysis of surface myoelectric signals during athletic movement. IEEE Trans Med Biol 20(6):106–115
Xie H, Wang Z (2006) Mean frequency desired via Hilbert-Huang transform with application to fatigue EMG signal analysis. Comput Methods Programs Biomed 82:114–120
Zhou P, Lowery MM (2007) Decoding a new neural-machine interface for control of artificial limbs. J Neurophysiol 98(5):2974–2982
Chen ZW, Hu TP (2002) A reconstruct digit by transplantation of a second toe for control of an electromechanical prosthetic hand. Microsurgery 22:5–10
Milica MJ (2010) An EMG system for studying motor control strategies and fatigue. In: Proceedings of the 10th Symposium on Neural Network Applications in Electrical Engineering, pp 15–18
Macro AC, Garcia C (2010) An alternative approach in muscle fatigue evaluation from the surface EMG signal. In: 32nd Annual international conference of the IEEE engineering in medicine and biology society, pp 2419–2422
Balestra F (2001) Time-frequency analysis of surface myoelectric signals during athletic movement. IEEE Trans Med Biol 20(6):106–115
Ai-Assaf Y, Ai-Nashash H (2001) Myocleetric signal segmentation and classification using wavelets based neural networks. In: Proceedings of the 23rd annual international conference (EMBS), pp 1820–1823
Nazarpour K, Sharafat AR, Firoozabadi SMP (2005) Surface EMG signal classification using a selective mix of higher order statistics. In: Proceedings of the 27th annual conference of the engineering in medicine and biology socity (IEEE 2005), pp 4208–4211
Haight JM (2005) The sensitivity of autoregressive model coefficient in quantification of trunk muscle fatigue during a sustained isometric contraction. Int J Ind Ergon 35(4):321–330
Acknowledgments
This work is partially supported by State Key Laboratory of Robotics and System (HIT) Grant #SKLRS-2010-ZD-03 and Research Funds for the Central Universities Grant #N110804005, #N120204002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, F., Hao, X., Zeng, B., Zhou, C., Wang, S. (2013). Automated Discrimination of Gait Patterns Based on sEMG Recognition. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_71
Download citation
DOI: https://doi.org/10.1007/978-3-642-38466-0_71
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38465-3
Online ISBN: 978-3-642-38466-0
eBook Packages: EngineeringEngineering (R0)