Science China Technological Sciences

, Volume 62, Issue 1, pp 21–30 | Cite as

Bio-signal based elbow angle and torque simultaneous prediction during isokinetic contraction

  • Yu Zhou
  • Jingbiao Liu
  • Jia Zeng
  • Kairu Li
  • Honghai LiuEmail author


It is of great importance to decode motion dynamics of the human limbs such as the joint angle and torque in order to improve the functionality and provide more intuitive control in human-machine collaborative systems. In order to achieve feasible prediction, both the surface electromyography (sEMG) and A-mode ultrasound were applied to detect muscle deformation and motor intent. Six abled subjects were recruited to perform five trails elbow isokinetic flexion and extension, and each trail contained five repetitions, with muscle deformation and sEMG signals recorded simultaneously. The experimental datasets were categorized as: the ultrasound-EMG combined datasets, ultrasound-only datasets and EMG-only datasets. The support vector machine (SVM) regression model was developed for both elbow joint angle and torque prediction, based on the above three kinds of datasets. The root-mean-square error (RMSE) and the correlation coefficients (R) were applied to evaluate the prediction accuracy. The results across all the subjects for different datasets indicated that the combined datasets and the ultrasound datasets were superior to the sEMG datasets both on elbow joint angle and torque prediction, and there were no significant differences between the combined datasets and the ultrasound datasets. It turns out that elbow angle and torque can be reconstructed by A-mode ultrasound, and the significant findings pave the way towards the application of musculature-driven human-machine collaborative systems.


angle torque surface electromyography (sEMG) ultrasound support vector machine (SVM) regression isokinetic contraction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ding H, Yang X, Zheng N, et al. Tri-Co robot: A Chinese robotic research initiative for enhanced robot interaction capabilities. Natl Sci Rev, 2017, 485: nwx148Google Scholar
  2. 2.
    Artemiadis P K, Kyriakopoulos K J. An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Trans Inform Technol Biomed, 2010, 14: 582–588CrossRefGoogle Scholar
  3. 3.
    Lambrecht J M, Pulliam C L, Kirsch R F. Virtual reality environment for simulating tasks with a myoelectric prosthesis: An assessment and training tool. JPO J Prosthetics Orthotics, 2011, 23: 89–94CrossRefGoogle Scholar
  4. 4.
    Fang Y, Hettiarachchi N, Zhou D, et al. Multi-modal sensing techniques for interfacing hand prostheses: A review. IEEE Senss J, 2015, 15: 6065–6076CrossRefGoogle Scholar
  5. 5.
    Parker P, Englehart K, Hudgins B. Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol, 2006, 16: 541–548CrossRefGoogle Scholar
  6. 6.
    Hudgins B, Parker P, Scott R N. A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng, 1993, 40: 82–94CrossRefGoogle Scholar
  7. 7.
    Li G, Schultz A E, Kuiken T A. Quantifying pattern recognition— Based myoelectric control of multifunctional transradial prostheses. IEEE Trans Neur Syst Rehab Eng, 2010, 18: 185–192CrossRefGoogle Scholar
  8. 8.
    Hargrove L J, Scheme E J, Englehart K B, et al. Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis. IEEE Trans Neural Syst Rehabil Eng, 2010, 18: 49–57CrossRefGoogle Scholar
  9. 9.
    Ameri A, Kamavuako E N, Scheme E J, et al. Support vector regression for improved real-time, simultaneous myoelectric control. IEEE Trans Neural Syst Rehabil Eng, 2014, 22: 1198–1209CrossRefGoogle Scholar
  10. 10.
    Hahne J M, Biessmann F, Jiang N, et al. Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng, 2014, 22: 269–279CrossRefGoogle Scholar
  11. 11.
    Hodges P W, Pengel L H M, Herbert R D, et al. Measurement of muscle contraction with ultrasound imaging. Muscle Nerve, 2003, 27: 682–692CrossRefGoogle Scholar
  12. 12.
    Kiesel K B, Uhl T L, Underwood F B, et al. Measurement of lumbar multifidus muscle contraction with rehabilitative ultrasound imaging. Manual Ther, 2007, 12: 161–166CrossRefGoogle Scholar
  13. 13.
    Shi J, Hu S X, Liu Z, et al. Recognition of finger flexion from ultrasound image with optical flow: A preliminary study. In: International Conference on Biomedical Engineering and Computer Science. IEEE, 2010. 1–4Google Scholar
  14. 14.
    Castellini C, Passig G, Zarka E. Using ultrasound images of the forearm to predict finger positions. IEEE Trans Neural Syst Rehabil Eng, 2012, 20: 788–797CrossRefGoogle Scholar
  15. 15.
    Sierra González D, Castellini C. A realistic implementation of ultrasound imaging as a human-machine interface for upper-limb amputees. Front Neurorobot, 2013, 7: 17CrossRefGoogle Scholar
  16. 16.
    Sikdar S, Rangwala H, Eastlake E B, et al. Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system. IEEE Trans Neural Syst Rehabil Eng, 2014, 22: 69–76CrossRefGoogle Scholar
  17. 17.
    Li Y, He K, Sun X, et al. Human-machine interface based on multichannel single-element ultrasound transducers: A preliminary study. In: IEEE International Conference on E-Health Networking, Applications and Services. IEEE, 2016. 1–6Google Scholar
  18. 18.
    Huang Y, Yang X, Li Y, et al. Ultrasound-based sensing models for finger motion classification. IEEE J Biomed Health Inform, 2018, 22: 1395–1405CrossRefGoogle Scholar
  19. 19.
    Sun X, Yang X, Zhu X, et al. Dual-frequency ultrasound transducers for the detection of morphological changes of deep-layered muscles. IEEE Senss J, 2018, 18: 1373–1383CrossRefGoogle Scholar
  20. 20.
    Yang X, Sun X, Zhou D, et al. Towards wearable A-mode ultrasound sensing for real-time finger motion recognition. IEEE Trans Neural Syst Rehabil Eng, 2018, 26: 1199–1208CrossRefGoogle Scholar
  21. 21.
    Akhlaghi N, Baker C A, Lahlou M, et al. Real-time classification of hand motions using ultrasound imaging of forearm muscles. IEEE Trans Biomed Eng, 2016, 63: 1687–1698CrossRefGoogle Scholar
  22. 22.
    Hislop H J, Perrine J. The isokinetic concept of exercise. Phys Ther, 1967, 47: 114–117CrossRefGoogle Scholar
  23. 23.
    Delitto A, Rose S J, Crandell C E, et al. Reliability of isokinetic measurements of trunk muscle performance. Spine, 1991, 16: 800–803CrossRefGoogle Scholar
  24. 24.
    Lucas M F, Gaufriau A, Pascual S, et al. Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization. Biomed Signal Processing Control, 2008, 3: 169–174CrossRefGoogle Scholar
  25. 25.
    Xie H B, Zheng Y P, Guo J Y, et al. Estimation of wrist angle from sonomyography using support vector machine and artificial neural network models. Med Eng Phys, 2009, 31: 384–391CrossRefGoogle Scholar
  26. 26.
    Qiu S, Feng J, Xu J, et al. Sonomyography analysis on thickness of skeletal muscle during dynamic contraction induced by neuromuscular electrical stimulation: A pilot study. IEEE Trans Neural Syst Rehabil Eng, 2017, 25: 62–70CrossRefGoogle Scholar
  27. 27.
    Guo J Y, Zheng Y P, Xie H B, et al. Towards the application of onedimensional sonomyography for powered upper-limb prosthetic control using machine learning models. Prosthet Orthot Int, 2013, 37: 43–49CrossRefGoogle Scholar
  28. 28.
    Hettiarachchi N, Ju Z, Liu H. A new wearable ultrasound muscle activity sensing system for dexterous prosthetic control. In: IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2016. 1415–1420Google Scholar
  29. 29.
    Szabo T L. Diagnostic Ultrasound Imaging: Inside Out. 2nd ed. Amsterdam: Elsevier Academic Press, 2014Google Scholar
  30. 30.
    Smola A J, Schölkopf B. A tutorial on support vector regression. Stat Comput, 2004, 14: 199–222MathSciNetCrossRefGoogle Scholar
  31. 31.
    Liu M M, Herzog W, Savelberg H H C M. Dynamic muscle force predictions from EMG: An artificial neural network approach. J Electromyogr Kinesiol, 1999, 9: 391–400CrossRefGoogle Scholar
  32. 32.
    Pau J W L, Xie S S Q, Pullan A J. Neuromuscular interfacing: Establishing an EMG-driven model for the human elbow joint. IEEE Trans Biomed Eng, 2012, 59: 2586–2593CrossRefGoogle Scholar
  33. 33.
    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, 2017, 14: 046005CrossRefGoogle Scholar
  34. 34.
    Shi J, Zheng Y P, Huang Q H, et al. Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: Sonomyography assessment for arm muscles. IEEE Trans Biomed Eng, 2008, 55: 1191–1198CrossRefGoogle Scholar
  35. 35.
    Tenore F, Ramos A, Fahmy A, et al. Towards the control of individual fingers of a prosthetic hand using surface EMG signals. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. International Conference of the IEEE. IEEE, 2007. 6145–6148Google Scholar
  36. 36.
    Amma C, Krings T, Schultz T. Advancing muscle-computer interfaces with high-density electromyography. In: ACM Conference on Human Factors in Computing Systems. ACM, 2015. 929–938Google Scholar

Copyright information

© Science in China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yu Zhou
    • 1
  • Jingbiao Liu
    • 1
  • Jia Zeng
    • 1
  • Kairu Li
    • 2
  • Honghai Liu
    • 1
    Email author
  1. 1.State Key Laboratory of Mechanical System and Vibration, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of ComputingThe University of PortsmouthPortsmouthUK

Personalised recommendations