Skip to main content
SpringerLink
Log in

EMG Signals as a Way to Control Soft Actuators

  • Conference paper
  • First Online:
Computer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering II (CMBBE 2021)

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 38))

  • 479 Accesses

Abstract

Physical impairments have multiple causes, making them common. Locomotion disorders have been afflicting society for a long time, motivating researchers and engineers to mitigate their consequences. Nowadays, solutions such as exoskeletons and exosuits are in constant development and may become reliable options to help people in these circumstances. However, prospective solutions need a control system acting as a “bridge” between the external device (actuators) and the user. Among several possibilities, movement prediction is prioritized over movement reaction. This task may be done by capturing and processing biological signals from a user’s body. Within this paradigm, muscle electromyographic (EMG) signals were acquired, processed and sent as input to piezoelectric soft actuators.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Miranda, A.B.W., et al.: Bioinspired mechanical design of an upper limb exoskeleton for rehabilitation and motor control assessment. In: 2012 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1776–1781 (2012)

    Google Scholar 

  2. Dima-Cozma, C., et al.: The importance of healthy lifestyle in modern society: a medical, social and spiritual perspective. Eur. J. Sci. Theol. 10(3), 111–120 (2014)

    Google Scholar 

  3. Chen, B., et al.: State-of-the-art research in robotic hip exoskeletons: a general review. J. Orthop. Translation 4–13 (2020). https://doi.org/10.1016/j.jot.2019.09.006

  4. Moreno, J.C., et al.: Hybrid wearable robotic exoskeletons for human walking. Wearable Robot. Syst. Appl. 347–364 (2020). https://doi.org/10.1016/B978-0-12-814659-0.00018-7

  5. Nas, K., et al.: Rehabilitation of spinal cord injuries. World J. Orthop. 8–16 (2015). issn: 2218–5836. https://doi.org/10.5312/wjo.v6.i1.8

  6. Levesque, L., Doumit, M.: Study of human-machine physical interface for wearable mobility assist devices. Med. Eng. Phys. 33–43 (2020). https://doi.org/10.1016/j.medengphy.2020.03.008

  7. Chen, B., et al.: Recent developments and challanges of lower extremity exoskeletons. J. Orthop. Transl. 5, 26–37 (2015). https://doi.org/10.1016/j.jot.2015.09.007

    Article  Google Scholar 

  8. Bellou, E., Stevenson-Hoare, J., Escott-Price, V.: Polygenic risk and pleiotropy in neurodegenerative diseases. Neurobiol. Dis. (2020). https://doi.org/10.1016/j.nbd.2020.104953

  9. Vos, T., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. The Lancet 1545–1602 (2016). https://doi.org/10.1016/S0140-6736(16)31678-6

  10. National Spinal Cord Injury Statistical Center. https://www.nscisc.uab.edu/

  11. Kirshblum, S., et al.: Rehabilitation of persons with central nervous system tumors. Cancer (2001). https://doi.org/10.1002/1097-0142(20010815)92:4+<1029::AID-CNCR1416>3.0.CO;2-P

  12. Yip, P.K., Malaspina, A.: Spinal cord trauma and the molecular point of no return. Mol. Neurodegeneration (2012). https://doi.org/10.1186/1750-1326-7-6

  13. Kim, Y.S., et al.: A force reflected exoskeleton-type masterarm for human-robot interaction. IEEE Trans. Syst. Man Cybern Part A Syst. Hum. 35(2), 198–212 (2005). issn: 10834427. https://doi.org/10.1109/TSMCA.2004.832836

  14. van den Bogert A.J.: Exotendons for assistance of human locomotion. BioMed. Eng. Online (2003). https://doi.org/10.1186/1475-925X-2-17

  15. Sado, F., et al.: Design and control of a wearable lower-body exoskeleton for squatting and walking assistance in manual handling works. Mechatronics (2019). https://doi.org/10.1016/j.mechatronics.2019.102272

  16. Casanova, P.: Sensorização Espacial no Contexto da Reabilitação Humana (2020)

    Google Scholar 

  17. Muhammad Zahak Jamal: Signal acquisition using surface EMG and circuit design considerations for robotic prosthesis. Comput. Intell. Electromyogr. Anal-A Perspect. Current Appl. Future Challenges 18, 427–448 (2012)

    Google Scholar 

  18. Jamal, M.Z., Dong-Hyun, L., Hyun, D.J.: Real time adaptive filter based EMG signal processing and instrumentation scheme for robust signal acquisition using dry EMG electrodes. In: 2019 16th International Conference on Ubiquitous Robots (UR), pp. 683–688 (2019). https://doi.org/10.1109/URAI.2019.8768662

  19. Péter A., Arndt A., Hegyi, A., et al.: Effect of footwear on intramuscular EMG activity of plantar flexor muscles in walking. J. Electromyogr. Kinesiol. 55, 102474 (2020). https://doi.org/10.1016/j.jelekin.2020.102474

  20. Qi, J., Jiang, G., Li, G., Sun, Y., Tao, B.: Surface EMG hand gesture recognition system based on PCA and GRNN. Neural Comput. Appl. 32(10), 6343–6351 (2019). https://doi.org/10.1007/s00521-019-04142-8

    Article  Google Scholar 

  21. Asghari Oskoei, M., Hu, H.: Myoelectric control systems - a survey. Biomed. Sig. Process. Control 2, 275–294 (2007). https://doi.org/10.1016/j.bspc.2007.07.009

  22. Lenzi, T., et al.: Intention-based EMG control for powered exoskeletons. IEEE Trans. Biomed. Eng. 59(8), 2180–2190 (2012). https://doi.org/10.1109/TBME.2012.2198821

    Article  Google Scholar 

  23. Noda, T., et al.: An electromyogram based force control coordinated in assistive interaction. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2657–2662 (2013). https://doi.org/10.1109/ICRA.2013.6630942

  24. Peternel, L., et al.: Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLOS ONE (2016). https://doi.org/10.1371/journal.pone.014894

  25. Park, J., et al.: Feasibility of propertional EMG control for a hand exoskeleton: a fitts. law approach. IFAC-PapersOnLine 51, 214–219 (2018). https://doi.org/10.1016/j.ifacol.2018.11.544

  26. Zhang, K., de Silva, C.W., Chenglong, F.: Sensor fusion for predictive control of human-prosthesis-environment dynamics in assistive walking: a survey. In: CoRR abs/1903.07674 (2019). arXiv: 1903.07674. http://arxiv.org/abs/1903.07674

  27. Farina, D., et al.: Surface EMG crosstalk between knee extensor muscles: experimental and model results. Muscle Nerve 26(5), 681–695 (2002). https://doi.org/10.1002/mus.10256

    Article  Google Scholar 

  28. BITalino. BITalino. https://bitalino.com/

  29. MathWorks. MathWorks. https://www.mathworks.com/products/matlab.html

  30. Joy-it. Joy-it. https://joy-it.net/de/

  31. Python Org. Python Og. https://www.python.org/

  32. MathWorks Instrument Control Toolbox Team. BITalino Toolbox. https://www.mathworks.com/matlabcentral/fileexchange/53983-bitalino-toolbox (2021)

  33. Python Modules. Python Modules. https://pypi.org/project/pyserial/

  34. Reaz, M.B.I., Hussein, M.S., Mohd-Yasin, F.: Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. 8, 11–35 (2006). https://doi.org/10.1251/bpo115

    Article  Google Scholar 

  35. Farinha, D.M.: Processamento de sinal EMG para dispositivos de reabilitação e assistênica motora (2018)

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge funding from FCT, Portugal, MCTES, FSE and EU under grant SFRH/BD/147807/2019, and project MImBI - PTDC/EME-APL/29875/2017 financed through FEDER and FCT. This work was supported by FCT, through INEGI, under LAETA, project UIDB/50022/2020.

Author information

Authors and Affiliations

  1. INEGI, Porto, Portugal

    António Diogo André & Ana Margarida Teixeira

  2. INEGI-LAETA/FEUP, Porto, Portugal

    Pedro Martins

  3. ARAID, I3A, Universidad de Zaragoza, Zaragoza, Spain

    Pedro Martins

Authors
  1. António Diogo André
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana Margarida Teixeira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to António Diogo André .

Editor information

Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  2. University Hospital Bonn, Bonn, Germany

    Christoph Bourauel

  3. Biomechanics Research Unit, University of Liège, Liège, Belgium

    Liesbet Geris

  4. Biomechanics, Katholieke Universiteit Leuven, Leuven, Belgium

    Jos Vander Slote

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

André, A.D., Teixeira, A.M., Martins, P. (2023). EMG Signals as a Way to Control Soft Actuators. In: Tavares, J.M.R.S., Bourauel, C., Geris, L., Vander Slote, J. (eds) Computer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering II. CMBBE 2021. Lecture Notes in Computational Vision and Biomechanics, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-031-10015-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10015-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10014-7

  • Online ISBN: 978-3-031-10015-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation