System for the Measurement of sEMG and Angular Displacement of the Ankle-Foot Joint Complex for Muscle Co-activation Detection in the Diagnosis of Foot Drop Pathology

  • Santiago Noriega
  • Maria C. Rojas
  • Cecilia MurrugarraEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 685)


The measurement of physiological variables for the assessment of pathologies is gaining a lot of strength and attention, even more so when it comes to wearable embedded systems able to offer comfort and precision in measurements. This work aimed to develop an electronic wearable sensing and wireless system that could measure the electrical activity of the Tibialis Anterior and Peroneus Longus muscles through bipolar surface electromyography; the system also identifies the muscle activations of antagonistic muscles, a phenomenon known as muscle co-activations. Sensing the angular displacement of the joint complex of the ankle in the sagittal and frontal planes through an Inertial Measurement Unit sensor system. A system with modular and smart architecture was designed and develop. It is based around five stages in charge of sensing, processing, and transmitting the data registers to a PC or a mobile device. The surface electromyography module is built around a two-channel amplifier sampling at 1 ksps/ch with a resolution of 10 bits; the angular displacement stage is based around an IMU sensor sampling at 1 ksps and 16 bits of resolution. Both data registers are transmitted wirelessly. A prototype with the architecture previously described was developed and tested. A statistical analysis of the data collected compared with commercial instruments was deployed, showing a Mean Square Error of \({\le }{\pm }5.5\%\) for the sEMG and an average error of \({\le }{\pm }1.5^{\circ }\) in the angular displacement measurements. The measurements made and the data verification protocol show that the equipment fully complies with all the technical and functional requirements of the project. Additionally, a record of muscle co-activation is presented, which can provide additional information, not only of the physiological state of the muscles but also the status of the pathology. By reducing the size of the device, improving the experimental setup and making small improvements to the hardware, the developed system opens a new panorama in the assessment and characterization of pathological conditions in real patients and therefore in the rehabilitation field.


Foot drop Rehabilitation Modular systems sEMG amplifier Smart sensing system 



This work was supported by the Faculty of Engineering and Electronic Engineering Program of Universidad El Bosque, with the research project PFI-2017-EL-011.


  1. 1.
    Aldemir, C., Duygun, F.: New and unusual causes of foot drop. Med. Sci. - Int. Med. J. 6, 49–495 (2017)Google Scholar
  2. 2.
    Westhout, F.D., Paré, L.S., Linskey, M.E.: Central causes of foot drop: rare and underappreciated differential diagnoses. J. Spinal Cord Med. 30(1), 62–66 (2007)CrossRefGoogle Scholar
  3. 3.
    Stegeman, D., Hermens, H.: Standards for surface electromyography: the European project surface EMG for non-invasive assessment of muscles (SENIAM). Roessingh Res. Dev. 108–112 (2007)Google Scholar
  4. 4.
    Hof, A.L.: EMG and muscle force: an introduction. Hum. Mov. Sci. 3(1–2), 119–153 (1984)CrossRefGoogle Scholar
  5. 5.
    Merletti, R., Rainoldi, A., Farina, D.: Surface electromyography for noninvasive characterization of muscle. Electromyographie superficielle pour une caracterisation mesuree du muscle. Exercise Sport Sci. Rev. 29(1), 20–25 (2001)CrossRefGoogle Scholar
  6. 6.
    Neblett, R.: Surface electromyographic (SEMG) biofeedback for chronic low back pain. Healthcare 4(2), 27 (2016)CrossRefGoogle Scholar
  7. 7.
    Kutilek, P., Hybl, J., Kauler, J., Viteckova, S.: Prosthetic 6-DOF arm controlled by emg signals and multi-sensor system. In: Proceedings of 15th International Conference MECHATRONIKA (March 2017), pp. 1–5 (2012)Google Scholar
  8. 8.
    Massó, N., Rey, F., Romero, D., Gual, G., Costa, L., Germán, A.: Surface electromyography applications in the sport. Apunts Med. Esport 45(165), 121–130 (2010)Google Scholar
  9. 9.
    Cerone, G.L., Botter, A., Gazzoni, M.: A modular, smart, and wearable system for high density sEMG detection. IEEE Trans. Biomed. Eng. 66(12), 3371–3380 (2019). Scholar
  10. 10.
    Day, S.: Important factors in surface EMG measurement. Bortec Biomed. Ltd. 1–17 (2002)Google Scholar
  11. 11.
    Melaku, A., Kumar, D.K., Bradley, A.: Influence of inter-electrode distance on EMG, pp. 1082–1085, May 2005Google Scholar
  12. 12.
    Ghapanchizadeh, H., Ahmad, S.A., Ishak, A.J.: Effect of surface electromyography electrode position during wrist extension and flexion based on time and frequency domain analyses. Int. J. Control Theory Appl. 9(5), 2643–2650 (2016)Google Scholar
  13. 13.
    Frey-Law, L.A., Avin, K.G.: Muscle coactivation: a generalized or localized motor control strategy? (2013)Google Scholar
  14. 14.
    Aneri, M., Rutvij, H.: A review on applications of ambient assisted living. Int. J. Comput. Appl. 176(8), 1–7 (2017)Google Scholar
  15. 15.
    Beyaz, A.: Posture determination by using flex sensor and image analysis technique. Agric. Sci. Digest - Res. J. 37(04), 257–262 (2017)Google Scholar
  16. 16.
    Vyawahare, V.M., Pardhi, D.: Design of a prosthetic arm using flex. Int. J. Electron. Commun. Eng. Technol. 8(2), 1–6 (2017)Google Scholar
  17. 17.
    Masdar, A., Ibrahim, B.S.K.K., Hanafi, D., Jamil, M.M.A., Rahman, K.A.A.: Knee joint angle measurement system using gyroscope and flex-sensors for rehabilitation. In: BMEiCON 2013 - 6th Biomedical Engineering International Conference, pp. 5–9, October 2013Google Scholar
  18. 18.
    Khayani, S.B.: Development of wearable sensors for body joint angle measurement, p. 70, May 2011Google Scholar
  19. 19.
    Zhang, Z., Dong, Y., Ni, F., Jin, M., Liu, H.: A method for measurement of absolute angular position and application in a novel electromagnetic encoder system (2015)Google Scholar
  20. 20.
    Desa, H., Azfar, A.Z.: Study of inertial measurement unit sensor, May 2014Google Scholar
  21. 21.
    Kumar, K., Varghese, A., Reddy, P.K., Narendra, N., Swamy, P., Chandra, M.G., Balamuralidhar, P.: An improved tracking using IMU and vision fusion for mobile augmented reality applications. Int. J. Multimedia Appl. (IJMA) 6(5), 13–29 (2014)CrossRefGoogle Scholar
  22. 22.
    Adcock, B., Hansen, A., Roman, B., Teschke, G.: Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. Adv. Imaging Electron Phys. 182, 187–279 (2014). Scholar
  23. 23.
    Nordin, M.: Biomecanica Basica del Sistema Muscoesqueletico-Nordin.pdf (2004)Google Scholar
  24. 24.
    Ervilha, U.F., Graven-Nielsen, T., Duarte, M.: A simple test of muscle coactivation estimation using electromyography (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Santiago Noriega
    • 1
  • Maria C. Rojas
    • 1
  • Cecilia Murrugarra
    • 1
    Email author
  1. 1.Electronic Engineering Program, Faculty of EngineeringUniversidad El BosqueBogotaColombia

Personalised recommendations