Skip to main content
SpringerLink
Log in

Muscle Strength Assessment System Using sEMG-Based Force Prediction Method for Wrist Joint

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Tele-assessment systems are crucial for home-based rehabilitation, as they allow therapists to assess the status of patients and adjust the parameters of various home-based training devices. Traditional force/torque sensors are commonly used in tele-assessment systems to detect muscle strength because such sensors are convenient. However, muscle activity can be measured using surface electromyography (sEMG), which records the activation level of skeleton muscles and is a more accurate method for determining the amount of force exerted. Thus, in this paper, a method for predicting muscle strength using only sEMG signals is proposed. The sEMG signals measure the isometric downward touch motions and are recorded from four muscles of the forearm. The prediction function is derived from a musculoskeletal model. The parameters involved are calibrated using the Bayesian linear regression algorithm. To avoid the complex modeling of the entire movement, a neural network classifier is trained to recognize the force-exerting motion. Experimental results show that the mean root-mean-square error of the proposed method is below 2.5 N. In addition, the effects of the high-pass cutoff frequency and the co-activation of flexors and extensors for EMG force prediction are discussed in this paper. The performance of the proposed method is validated further in real-time by a remote predicted-force evaluation experiment. A haptic device (Phantom Premium) is used to represent the predicted force at the therapist’s remote site. Experimental results show that the proposed method can provide acceptable prediction results for tele-assessment systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Lloyd-Jones, D., Adams, R. J., Brown, T. M., Carnethon, M., Dai, S., De Simone, G., et al. (2010). Heart disease and stroke statistics–2010 update: A report from the American Heart Associate. Circulation, 121, 46–215.

    Article  Google Scholar 

  2. Song, Z., Guo, S., & Fu, Y. (2011). Development of an upper extremity motor function rehabilitation system and an assessment system. International Journal of Mechatronics Automation, 1, 19–28.

    Article  Google Scholar 

  3. Song, Z., Guo, S., Pang, M., Zhang, S., Xiao, N., Gao, B., & Shi, L. (2014). Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint. Journal of Medical and Biological Engineering, 34, 188–196.

    Article  Google Scholar 

  4. Zhang, S., Guo, S., Gao, B., Hirata, H., & Ishihara, H. (2015). Design of a novel telerehabilitation system with a force-sensing mechanism. Sensors, 15, 11511–11527.

    Article  Google Scholar 

  5. Park, H. S., Peng, Q., & Zhang, L. Q. (2008). A portable telerehabilitation system for remote evaluations of impaired elbows in neurological disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16, 245–254.

    Article  Google Scholar 

  6. Disselhorst-Klug, C., Schmitz-Rode, T., & Rau, G. (2009). Surface electromyography and muscle force: Limits in sEMG-force relationship and new approaches for applications. Clinical Biomechanics, 24, 225–235.

    Article  Google Scholar 

  7. Staudenmann, D., Roeleveld, K., Stegeman, D. F., & van Dieën, J. H. (2010). Methodological aspects of SEMG recordings for force estimation—a tutorial and review. Journal of Electromyography and Kinesiology, 20, 375–387.

    Article  Google Scholar 

  8. Savelberg, H. H., & Herzog, W. (1997). Prediction of dynamic tendon forces from electromyographic signals: An artificial neural network approach. Journal of Neuroscience Methods, 78, 65–74.

    Article  Google Scholar 

  9. Cavallaro, E., Rosen, J., Perry, J. C., Burns, S., & Hannaford, B. (2005). Hill-based model as a myoprocessor for a neural controlled powered exoskeleton arm-parameters optimization. Proeedings of the IEEE International Conference on Robotics & Automation, 4525–4530.

  10. Cavallaro, E. E., Rosen, J., Perry, J. C., & Burns, S. (2006). Real-time myoprocessors for a neural controlled powered exoskeleton arm. IEEE Transactions on Biomedical Engineering, 53, 2387–2396.

    Article  Google Scholar 

  11. Manal, K., & Buchanan, T. S. (2003). A one-parameter neural activation to muscle activation model: Estimating isometric joint moments from electromyograms. Journal of Biomechanics, 36, 1197–1202.

    Article  Google Scholar 

  12. Potvin, J. R., Norman, R. W., & McGill, S. M. (1996). Mechanically corrected EMG for the continuous estimation of erector spine muscle loading during repetitive lifting. European Journal of Applied Physiology and Occupational Physiology, 74, 119–132.

    Article  Google Scholar 

  13. Lloyd, D. G., & Besier, T. F. (2003). An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics, 36, 765–776.

    Article  Google Scholar 

  14. Fleischer, C., & Hommel, G. (2008). A human–exoskeleton interface utilizing electromyography. IEEE Transactions on Robotics, 24, 872–882.

    Article  Google Scholar 

  15. Fleischer, C., Reinicke, C., & Hommel, G. (2005). Predicting the intended motion with EMG signals for an exoskeleton orthosis controller. Proceedings of IEEE International Conference on Intelligent Robot and Systems, 2029–2034.

  16. Staudenmann, D., Kingma, I., Stegeman, D. F., & van Dieën, J. H. (2005). Towards optimal multi-channel EMG electrode configurations in muscle force estimation: A high density EMG study. Journal of Electromyography Kinesiology, 15, 1–11.

    Article  Google Scholar 

  17. Artemiadis, P. K., & Kyriakopoulos, K. J. (2011). A switching regime model for the EMG-based control of a robot arm. IEEE Transactions on Systems Man Cybernatics B, 41, 53–63.

    Article  Google Scholar 

  18. Amarantini, D., & Martin, L. (2004). A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions. Journal of Biomechanics, 37, 1393–1404.

    Article  Google Scholar 

  19. SENIAM project. http://www.seniam.org/.

  20. Lew, H. L., & Tsai, S. J. (2007). Johnson’s practical electromyography (4th ed.). Philadelphia: Lippincott Williams & Wilkins.

    Google Scholar 

  21. Hashemi, J., Morin, E., Mousavi, P., Mountjoy, K., & Hashtrudi-Zaad, K. (2012). EMG-force modeling using parallel cascade identification. Jounal of Electromyography Kinesiology, 22, 467–477.

    Google Scholar 

  22. Thelen, D. G., Schultz, A. B., Fassois, S. D., & Ashton-Miller, J. A. (1994). Identification of dynamic myoelectric signal-to-force models during isometric lumbar muscle contractions. Journal of Biomechanics, 27, 907–919.

    Article  Google Scholar 

  23. Buchanan, T. S., Lloyd, D. G., Manal, K., & Besier, T. F. (2004). Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command. Journal of Applied Biomechanics, 20, 367–395.

    Google Scholar 

  24. Manal, K., Gonzalez, R. V., Lloyd, D. G., & Buchanan, T. S. (2002). A real-time EMG driven virtual arm. Computers in Biology and Medicine, 32, 25–36.

    Article  Google Scholar 

  25. Tang, X., Liu, Y., Lv, C., & Sun, D. (2012). Hand motion classification using a multi-channel surface electromyography sensor. Sensors, 12, 1130–1147.

    Article  Google Scholar 

  26. Ju, Z., Ouyang, G., Wilamowska-Korsak, M., & Liu, H. (2013). Surface EMG based hand manipulation identification via nonlinear feature extraction and classification. IEEE Sensors Journal, 13, 3302–3311.

    Article  Google Scholar 

  27. Chen, X., & Wang, Z. J. (2013). Pattern recognition of number gestures based on a wireless surface EMG system. Biomedical Signal Process Control, 8, 184–192.

    Article  Google Scholar 

  28. Amsüss, S., Goebel, P. M., Jiang, N., Graimann, B., Paredes, L., & Farina, D. (2014). Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Transactions on Biomedical Engineering, 61, 1167–1176.

    Article  Google Scholar 

  29. Bu, N., Okamoto, M., & Tsuji, T. (2009). A hybrid motion classification approach for EMG-based human–robot interfaces using Bayesian and neural networks. IEEE Transactions on Robotics, 25, 502–511.

    Article  Google Scholar 

  30. Pang, M., Guo, S., Huang, Q., Ishihara, H., & Hirata, H. (2015). Electromyography-based quantitative representation method for upper-limb elbow joint angle in sagittal plane. Journal of Medical and Biological Engineering, 35, 165–177.

    Article  Google Scholar 

  31. Potvin, J. R., & Brown, S. H. M. (2004). Less is more: High pass filtering, to remove up to 99 % of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates. Journal of Electromyography Kinesiology, 14, 389–399.

    Article  Google Scholar 

  32. Staudenmann, D., Potvin, J. R., Kingma, I., Stegeman, D. F., & van Dieën, J. H. (2007). Effects of EMG processing on biomechanical models of muscle joint systems: Sensitivity of trunk muscle moments, spinal forces, and stability. Journal of Biomechanics, 40, 900–909.

    Article  Google Scholar 

  33. Brown, S. H., Brookham, R. L., & Dickerson, C. R. (2010). High-pass filtering surface EMG in an attempt to better represent the signals detected at the intramuscular level. Muscle and Nerve, 41, 234–239.

    Google Scholar 

Download references

Acknowledgments

This research was partly supported by National Natural Science Foundation of China (61375094), Key Research Program of the National Science Foundation of Tianjin (13JCZDJC26200), National High-Tech Research and Development Program of China (2015AA043202), and the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) (15K2120).

Author information

Authors and Affiliations

  1. Graduate School of Engineering, Kagawa University, Takamatsu, Kagawa, 761-0396, Japan

    Songyuan Zhang & Muye Pang

  2. Institute of Advanced Biomedical Engineering System, School of Life Science and Technology, Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Haidian District, Beijing, 100081, China

    Shuxiang Guo, Baofeng Gao & Qiang Huang

  3. Department of Intelligent Mechanical Systems Engineering, Kagawa University, Takamatsu, Kagawa, 761-0396, Japan

    Shuxiang Guo, Hideyuki Hirata & Hidenori Ishihara

Authors
  1. Songyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuxiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baofeng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muye Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hideyuki Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hidenori Ishihara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Songyuan Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Guo, S., Gao, B. et al. Muscle Strength Assessment System Using sEMG-Based Force Prediction Method for Wrist Joint. J. Med. Biol. Eng. 36, 121–131 (2016). https://doi.org/10.1007/s40846-016-0112-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-016-0112-5

Keywords

Search

Navigation