Advertisement

Estimation of Knee Extension Force Using Mechanomyography Signals Detected Through Clothing

  • Daqing Wang
  • Chenlei Xie
  • Haifeng Wu
  • Dun Hu
  • Qianqian Zhang
  • Lifu GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)

Abstract

This paper proposes a more flexible method for estimating the knee extension forces using mechanomyography (MMG) signal. We detect the MMG signal of the quadriceps through clothing. Then several features were extracted from three channels. A support vector machine model was applied to generate mapping between the features and the actual forces. Results indicates that the best estimation performance can be obtained using a combination of four features (the mean absolute value (MAV), the sample entropy (SampEn), the mean power frequency (MPF) and the correlation coefficients of 2 different channels (CC2Cs)). Finally, an average coefficient of determination (R2) of 0.799 and a root-mean-squared error (RMSE) of 9.18% of the maximum voluntary isometric contraction (MVC) were obtained. These results are similar to those of earlier studies, which suggests that the information in the MMG signal has not been detectably reduced when measured through clothing. Therefore, the MMG signal through clothing is able to function as a reliable estimator of muscle contraction strength and can be used as the muscle machine interface to control robotics or to monitor human activity.

Keywords

Mechanomyography Force estimation Support vector machines Quadriceps femoris 

References

  1. 1.
    Alves, N., Chau, T.: Classification of the mechanomyogram: its potential as a multifunction access pathway. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 2951--2954. IEEE (2009)Google Scholar
  2. 2.
    Barry, D.T., Leonard Jr., J.A., Gitter, A.J., et al.: Acoustic myography as a control signal for an externally powered prosthesis. Arch. Phys. Med. Rehabil. 67, 267–269 (1986)Google Scholar
  3. 3.
    Beck, T.W., Housh, T.J., Cramer, J.T., et al.: Mechanomyographic amplitude and frequency responses during dynamic muscle actions: a comprehensive review. Biomed. Eng. Online 4, 67 (2005)CrossRefGoogle Scholar
  4. 4.
    Beck, T.W., Housh, T.J., Fry, A.C., et al.: A wavelet-based analysis of surface mechanomyographic signals from the quadriceps femoris. Muscle Nerve 39, 355–363 (2009)CrossRefGoogle Scholar
  5. 5.
    Beck, T.W., Housh, T.J., Johnson, G.O., et al.: Does the frequency content of the surface mechanomyographic signal reflect motor unit firing rates? A brief review. J. Electromyogr. Kinesiol. 17, 1–13 (2007)CrossRefGoogle Scholar
  6. 6.
    Cescon, C., Farina, D., Gobbo, M., et al.: Effect of accelerometer location on mechanomyogram variables during voluntary, constant-force contractions in three human muscles. Med. Biol. Eng. Comput. 42, 121–127 (2004)CrossRefGoogle Scholar
  7. 7.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27 (2011)CrossRefGoogle Scholar
  8. 8.
    Dollar, A.M., Herr, H.: Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. Robot 24, 144–158 (2008)CrossRefGoogle Scholar
  9. 9.
    Fara, S., Gavriel, C., Vikram, C.S., et al.: Prediction of arm end-point force using multi-channel MMG. In: 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 27–32 (2014)Google Scholar
  10. 10.
    Fara, S., Vikram, C.S., Gavriel, C., et al.: Robust, ultra low-cost MMG system with brain-machine-interface applications. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering, pp. 723–726. IEEE, New York (2013)Google Scholar
  11. 11.
    Fu, W., Liu, Y., Xiong, X.: The influence of external elastic compression on the muscular strength, fatigue and activity of track and field athletes. Chin. J. Sports Med. 29, 631–635 (2010)Google Scholar
  12. 12.
    Huang, C., Chen, X., Cao, S., et al.: An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm. J. Neural Eng. 14, 046005 (2017)CrossRefGoogle Scholar
  13. 13.
    Islam, A., Sundaraj, K., Ahmad, R.B., et al.: Analysis of crosstalk in the mechanomyographic signals generated by forearm muscles during different wrist postures. Muscle Nerve 51, 899–906 (2015)CrossRefGoogle Scholar
  14. 14.
    Islam, M.A., Sundaraj, K., Ahmad, R.B., et al.: Mechanomyography sensor development, related signal processing, and applications: a systematic review. IEEE Sens. J. 13, 2499–2516 (2013)CrossRefGoogle Scholar
  15. 15.
    Jaskolska, A., Brzenczek, W., Kisiel-Sajewicz, K., et al.: The effect of skinfold on frequency of human muscle mechanomyogram. J. Electromyogr. Kinesiol. 14, 217–225 (2004)CrossRefGoogle Scholar
  16. 16.
    Krueger, E., Scheeren, E.M., Nogueira-Neto, G.N., et al.: Advances and perspectives of mechanomyography. Revista Brasileira de Engenharia Biomédica 30, 384–401 (2014)CrossRefGoogle Scholar
  17. 17.
    Lei, K.F., Cheng, S.-C., Lee, M.-Y., et al.: Measurement and estimation of muscle contraction strength using mechanomyography based on artificial neural network algorithm. Biomed. Eng. Appl. Basis Commun. 25, 1350020 (2013)CrossRefGoogle Scholar
  18. 18.
    Matheson, G.O., Maffey-Ward, L., Mooney, M., et al.: Vibromyography as a quantitative measure of muscle force production. Scand. J. Rehabil. Med. 29, 29–35 (1997)Google Scholar
  19. 19.
    Mobasser, F., Hashtrudi-Zaad, K.: A comparative approach to hand force estimation using artificial neural networks. Biomed. Eng. Comput. Biol. 4, BECB (2012). S9335CrossRefGoogle Scholar
  20. 20.
    Nogueira-Neto, G., Scheeren, E., Krueger, E., et al.: The influence of window length analysis on the time and frequency domain of mechanomyographic and electromyographic signals of submaximal fatiguing contractions. Open J. Biophys. 3(3), 178–190 (2013)CrossRefGoogle Scholar
  21. 21.
    Orizio, C.: Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. Crit. Rev. Biomed. Eng. 21, 201–243 (1993)Google Scholar
  22. 22.
    Posatskiy, A.O., Chau, T.: The effects of motion artifact on mechanomyography: a comparative study of microphones and accelerometers. J. Electromyogr. Kinesiol. 22, 320–324 (2012)CrossRefGoogle Scholar
  23. 23.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)CrossRefGoogle Scholar
  24. 24.
    Smith, T.G., Stokes, M.J.: Technical aspects of acoustic myography (AMG) of human skeletal muscle: contact pressure and force/AMG relationships. J. Neurosci. Methods 47, 85–92 (1993)CrossRefGoogle Scholar
  25. 25.
    Staudenmann, D., Roeleveld, K., Stegeman, D.F., et al.: Methodological aspects of SEMG recordings for force estimation – a tutorial and review. J. Electromyogr. Kinesiol. 20, 375–387 (2010)CrossRefGoogle Scholar
  26. 26.
    Teague, C.N., Hersek, S., Toreyin, H., et al.: Novel methods for sensing acoustical emissions from the knee for wearable joint health assessment. IEEE Trans. Biomed. Eng. 63, 1581–1590 (2016)CrossRefGoogle Scholar
  27. 27.
    Wang, D., Wu, H., Xie, C., et al.: Suppression of motion artifacts in multichannel mechanomyography using multivariate empirical mode decomposition. IEEE Sens. J. 19(14), 5732–5739 (2019)CrossRefGoogle Scholar
  28. 28.
    Wu, H., Huang, Q., Wang, D., et al.: A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. J. Electromyogr. Kinesiol. 42, 136–142 (2018)CrossRefGoogle Scholar
  29. 29.
    Wu, H.F., Wang, D.Q., Huang, Q., et al.: Real-time continuous recognition of knee motion using multi-channel mechanomyography signals detected on clothes. J. Electromyogr. Kinesiol. 38, 94–102 (2018)CrossRefGoogle Scholar
  30. 30.
    Yokoyama, M., Koyama, R., Yanagisawa, M.: An evaluation of hand-force prediction using artificial neural-network regression models of surface EMG signals for handwear devices. J. Sens. 2017, 1–12 (2017)CrossRefGoogle Scholar
  31. 31.
    Youn, W., Kim, J.: Feasibility of using an artificial neural network model to estimate the elbow flexion force from mechanomyography. J. Neurosci. Methods 194, 386–393 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  2. 2.University of Science and Technology of ChinaHefeiChina
  3. 3.Anhui Province Key Laboratory of Intelligent Building and Building Energy SavingAnhui Jianzhu UniversityHefeiChina
  4. 4.High Magnetic Field LaboratoryChinese Academy of SciencesHefeiChina

Personalised recommendations