Dynamic Electromyographic Models to Assess Elbow Joint Torque

Abstract:

Many studies have investigated the relationship between surface EMG and joint torque. Most studies have used EMG amplitude to assess elbow joint torque with dynamic models. In this paper, we used signal length and normalized zero crossing rates together with EMG amplitude to assess elbow joint torque with EMG-to-Torque models. We compared the performance of single feature EMG-to-Torque models and multi-feature EMG-to-Torque models by calculating the RMS error between estimated torque and true torque. The results show that multi-channel and multiple feature combination is superior to that of the single feature only. In this study, surface EMG signals were recorded from biceps and triceps muscles of 15 subjects. Single-channel and single feature linear model, multi-channel and single-feature model, multi-channel and single-feature model, multi-channel and multi-feature linear model were all used to assess elbow joint torque. The lowest RMS error is 7.6% achieved by four-channel multi-feature 18-order linear model.

• Periodical: Applied Mechanics and Materials (Volume 461)
• Pages: 608-617
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.461.608
• Online since: November 2013
• Authors: Wan Zhong Chen, Yue Yu Fan, Jun Lei, Bao Feng Sun
• Keywords: Dynamic Linear Model, EMG Amplitude, EMG-to-Torque, Normalized Zero Crossing Rates, Signal Length

References

[1] N. Hogan, R.W. Mann, Myoelectric signal processing: optimal estimation applied to electromyography-Part1: derivation of the optimal myoprocessor, IEEE Trans. Biomed. Eng., 27: 382-395, July (1980).

DOI: 10.1109/tbme.1980.326652

[2] Siegler S, Hillstrom HJ, Freedman W, Moskowitz G, Effect of myoelectric signal processing on the relationship between muscle force and processed EMG, Am J. Phys Med. 64(3): 130-149, (1985).

[3] P. M. H. Rack and D. R. Westbury, The effects of length and stimulus rate on tension in the isometric cat soleus muscle, J. Physiol. 204: 443-460, (1969).

DOI: 10.1113/jphysiol.1969.sp008923

[4] S. H. M. Brown and S. M. McGill, Co-activation alters the linear versus non-linear impression of the EMG-torque relationship of trunk muscles, J. Biomech., 41: 491–497, (2008).

DOI: 10.1016/j.jbiomech.2007.10.015

[5] A.L. Hof and IW. Van der Berg, EMG to force processing III: estimation of model parameters for the human triceps surae muscle and assessment of the accuracy by means of a torque plate, J. Biomech., 14: 771-785, (1981).

DOI: 10.1016/0021-9290(81)90033-6

[6] Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Dec. 19, (1993).

[7] Clancy EA, Bida O and Rancourt D, Influence of Advanced Electromyogram (EMG) Amplitude Processors on EMG-to-Torque Estimation during Constant-Posture, Force-Varying Contractions, J. Biomech. 39: 2690–2698, (2006).

DOI: 10.1016/j.jbiomech.2005.08.007

[8] L. Ljung, System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hall, p.491–519, (1999).

[9] Levmar, A brief description of the Levenberg-Marquardt algorithm implemented by Levrnar, Manolis A. Lourakis Institute of Computer Science, Greece. Feb. 11, (2005).

[10] Clancy EA, Liu Lukai et al. Identification of Constant-Posture EMG–Torque Relationship About the Elbow Using Nonlinear Dynamic Models, IEEE Trans. Biomed. Eng. 59(1): 205 - 212, (2012).

DOI: 10.1109/tbme.2011.2170423

[11] Clancy EA, Farry KA, Adaptive whitening of the electromyogram to improve amplitude estimation, IEEE Trans. Biomed. Eng. 47(6): 709–719, (2000).

DOI: 10.1109/10.844217

[12] Hudgins B, Parker P, Scott RN. A New Strategy for Multifunction Myoelectric Control, IEEE Trans. Biomed Eng. 40(1): 82–94, (1993).

DOI: 10.1109/10.204774

[13] O. Bida, D. Rancourt. et al. Electromyogram (EMG) Amplitude Estimation and Joint Torque Model Performance, Proceeding of the IEEE 31st Annual Northeast Bioengineering Conference, (2005).

DOI: 10.1109/nebc.2005.1432004