Electromyographic Signal-Driven Continuous Locomotion Mode Identification Module Design for Lower Limb Prosthesis Control

  • Rohit Gupta
  • Ravinder Agarwal
Research Article - Computer Engineering and Computer Science


The purpose of current research work is to extract physiological information form surface electromyographic signal (sEMG) in efficient manner for different human locomotion and utilize it for lower limb prosthesis control. The proposed locomotion mode identification approach conserves the novelty in terms of its dependency on only single muscle electromyographic signal and independency on human gait phase. For current study, 18 healthy subjects of 21–42-year age group were engaged and their sEMG signal form two lower limb muscles has been recorded for three daily life locomotion’s. The presented approach of locomotion mode identification covers the wide group of designing factors. Here, twelve different window sizes, twelve types of feature vectors and six classifiers were compared on the ground of predictive performance and stability. The results show the best performance of overlapped windowing technique with window size of 256 ms and a shift of 32 ms. LDA emerges as best performing classifier (p value < 0.05) with a classification accuracy ranging from 89 to 99% for diverse feature subsets. Feature vector carrying time domain information reflected better performance. The multifactorial analysis reveals that the choice of feature vector as the most dominant source of performance variation (39.17% of total variance) and muscle selection as the least (1.35% of total variance). The proposed locomotion mode identification approach proves its applicability for rehabilitation and lower limb prosthesis control applications. Also, the protocol leads the researches for determining the appropriate values of designing factors involves in the model.


Electromyographic signal Rehabilitation Locomotion mode Statistical analysis Classifiers Prosthetic control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The author thanks to the Department of Electronics and Information Technology (DeitY), Government of India for providing the financial support. Also, Director, Thapar University, Patiala, Punjab, India to encourage the current research work

Supplementary material

13369_2018_3193_MOESM1_ESM.pdf (3.3 mb)
Supplementary material 1 (pdf 3359 KB)


  1. 1.
    Ziegler-Graham, K.; MacKenzie, E.J.; Ephraim, P.L.; Travison, T.G.; Brookmeyer, R.: Estimating the prevalence of limb loss in the United States: 2005 to 2050. Arch. Phys. Med. Rehabil. 89, 422–429 (2008). CrossRefGoogle Scholar
  2. 2.
    Sup, F.; Varol, H.A.; Mitchell, J.; Withrow, T.J.; Goldfarb, M.: Preliminary evaluations of a self-contained anthropomorphic transfemoral prosthesis. IEEE Trans. Mechatron. 14, 667–676 (2009). CrossRefGoogle Scholar
  3. 3.
    Au, S.; Berniker, M.; Herr, H.: Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits. Neural Netw. 21, 654–666 (2008). CrossRefGoogle Scholar
  4. 4.
    Huang, Robert D.; Lipschutz, Todd A.; Kuiken, H.: A strategy for identifying locomotion modes using surface electromyography. IEEE Trans. Biomed. Eng. 56, 65–73 (2009). CrossRefGoogle Scholar
  5. 5.
    Tucker, M.R.; Olivier, J.; Pagel, A.; Bleuler, H.; Bouri, M.; Lambercy, O.: Control strategies for active lower extremity prosthetics and orthotics: a review. J. Neuroeng. Rehabil. 12, 1–29 (2015). CrossRefGoogle Scholar
  6. 6.
    Grimes, D.L.: An active multi mode above knee prosthesis controller (1979)Google Scholar
  7. 7.
    Varol, H.A.; Sup, F.; Goldfarb, M.: Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Trans. Biomed. Eng. 57, 542–551 (2010)CrossRefGoogle Scholar
  8. 8.
    Young, A.J.; Simon, A.M.; Eey, N.P.; Hargrove, L.J.: Intent recognition in a powered lower limb prosthesis using time history information. Ann. Biomed. Eng. 42, 631–641 (2014). CrossRefGoogle Scholar
  9. 9.
    Chen, B.; Zheng, E.; Fan, X.; Liang, T.; Wang, Q.; Wei, K.; Wang, L.; Member, S.; Zheng, E.; Fan, X.; Liang, T.; Wang, Q.; Wei, K.; Wang, L.: Locomotion mode classification using a wearable capacitive sensing system. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 744–755 (2013). CrossRefGoogle Scholar
  10. 10.
    Chen, B.; Zheng, E.; Wang, Q.: A locomotion intent prediction system based on multi-sensor fusion. Sensors 14, 12349–12369 (2014). CrossRefGoogle Scholar
  11. 11.
    Young, A.J.; Simon, A.M.; Hargrove, L.J.: A training method for locomotion mode prediction using powered lower limb prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 671–677 (2014)CrossRefGoogle Scholar
  12. 12.
    Chen, B.; Wang, Q.; Wang, X.; Huang, Y.; Wei, K.; Wang, Q.: A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution. Mechatronics 32, 12–21 (2015). CrossRefGoogle Scholar
  13. 13.
    Yuan, K.; Wang, Q.; Wang, L.: Fuzzy-logic-based terrain identification with multisensor fusion for transtibial amputees. IEEE Trans. Mechatron. 20, 618–630 (2015)CrossRefGoogle Scholar
  14. 14.
    Young, A.J.; Hargrove, L.J.: A classification method for user-independent intent recognition for transfemoral amputees using powered lower limb prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 217–225 (2016). CrossRefGoogle Scholar
  15. 15.
    Farrell, M.T.; Herr, H.: A method to determine the optimal features for control of a powered lower-limb prostheses. In: 33rd Annual International Conference of the IEEE EMBS, Boston, Massachusetts, pp. 6041–6046 (2011)Google Scholar
  16. 16.
    Zhang, X.; Wang, D.; Yang, Q.; Huang, H.: An automatic and user-driven training method for locomotion mode recognition for artificial leg control. In: 34th Annual International Conference of the IEEE EMBS, San Diego, CA, USA, pp. 6116–6119 (2012)Google Scholar
  17. 17.
    Du, L.; Zhang, F.; Liu, M.; Huang, H.: Toward design of an environment-aware adaptive locomotion-mode-recognition system. IEEE Trans. Biomed. Eng. 59, 2716–2725 (2012)CrossRefGoogle Scholar
  18. 18.
    Miller, J.D.; Beazer, M.S.; Hahn, M.E.: Myoelectric walking mode classification for transtibial amputees. IEEE Trans. Biomed. Eng. 60, 2745–2750 (2013). CrossRefGoogle Scholar
  19. 19.
    Zhang, F.; Huang, H.: Source selection for real-time user intent recognition toward volitional control of artificial legs. IEEE J. Biomed. Heal. Inform. 17, 907–914 (2013)CrossRefGoogle Scholar
  20. 20.
    Liu, M.; Wang, D.; Huang, H.H.: Development of an environment-aware locomotion mode recognition system for powered lower limb prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 434–443 (2016). CrossRefGoogle Scholar
  21. 21.
    Young, A.J.; Kuiken, T.A.; Hargrove, L.J.: Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. J. Neural Eng. 11, 1–12 (2014). CrossRefGoogle Scholar
  22. 22.
    Spanias, J.A.; Simon, A.M.; Ingraham, K.A.; Hargrove, L.J.: Effect of additional mechanical sensor data on an EMG-based pattern recognition system for a powered leg prosthesis. In: IEEE EMBS Conference on Neural Engineering, Montpellier, France, pp. 22–24 (2015)Google Scholar
  23. 23.
    Joshi, D.; Hahn, M.E.: Terrain and direction classification of locomotion transitions using neuromuscular and mechanical input. Ann. Biomed. Eng. 44, 1275–1284 (2016). CrossRefGoogle Scholar
  24. 24.
    Huang, H.; Zhang, F.; Hargrove, L.J.; Dou, Z.; Rogers, D.R.; Englehart, K.B.: Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Trans. Biomed. Eng. 58, 2867–2875 (2011)CrossRefGoogle Scholar
  25. 25.
    Wilson, D.H.; Atkeson, C.: Active capacitive sensing: exploring a new wearable sensing modality for activity recognition. Pervasive Comput. 6030, 319–336 (2010). Google Scholar
  26. 26.
    Wang, L.; Wang, Q.; Zheng, E.; Wang, L.; Wei, K.; Wang, Q.: A non-contact capacitive sensing system for recognizing locomotion modes of transtibial. IEEE Trans. Biomed. Eng. 61, 2911–2920 (2014). CrossRefGoogle Scholar
  27. 27.
    Pati, S.; Joshi, D.; Mishra, A.: Locomotion classification using EMG signal. In: 2010 International Conference on Information and Emerging Technologies, Karachi, pp. 1–6 (2010)Google Scholar
  28. 28.
    Huang, H.; Zhang, F.; Sun, Y.L.; He, H.: Design of a robust EMG sensing interface for pattern classification. J. Neural Eng. 7, 56005 (2010). CrossRefGoogle Scholar
  29. 29.
    Adewuyi, A.A.; Hargrove, L.J.; Kuiken, T.A.; Medicine, P.: Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control. Front. Neurorobot. (2016).
  30. 30.
    Spry, S.; Zebas, C.; Visser, M.: What is leg dominance. In: Hamill, J. (ed.) ISBS -XI Conference Proceedings Archive, pp. 165–168. International Society of Biomechanics in Sports, Amherst (1993)Google Scholar
  31. 31.
    Sadeghi, H.; Allard, P.; Prince, F.; Labelle, H.: Symmetry and limb dominance in able-bodied gait: a review. Gait Posture 12, 34–45 (2000). CrossRefGoogle Scholar
  32. 32.
    Gentry, V.; Gabbard, C.: Foot-preference behavior: a developmental perspective. J. Gen. Psychol. 122, 37-27 (1995). CrossRefGoogle Scholar
  33. 33.
    SENIAM: Sensors location: recommendations for sensor locations on individual muscles.
  34. 34.
    Rouhani, H.; Favre, J.; Crevoisier, X.; Aminian, K.: Measurement of multi-segment foot joint angles during gait using a wearable system. J. Biomech. Eng. 134, 61006 (2012). CrossRefGoogle Scholar
  35. 35.
    Chao, E.Y.S.; Volokh, K.Y.; Yoshida, H.; Shiba, N.; Ide, T.: Discrete element analysis in musculoskeletal biomechanics. Mol. Cell. Biomech. 7, 175–92 (2010)Google Scholar
  36. 36.
    Zheng, E.; Wang, Q.: Noncontact capacitive sensing based locomotion transition recognition for amputees with robotic transtibial prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 161–170 (2017). CrossRefGoogle Scholar
  37. 37.
    Chen, B.; Zheng, E.; Wang, Q.; Wang, L.: A new strategy for parameter optimization to improve phase-dependent locomotion mode recognition. Neurocomputing 149, 585–593 (2015). CrossRefGoogle Scholar
  38. 38.
    Gupta, R.; Agarwal, R.: Feature reduction and selection of SEMG signal for locomotion identification. In: 9th International Conference on Advances in Metrology, AdMet-2016, New Delhi, India (2016)Google Scholar
  39. 39.
    Hudgins, B.; Parker, P.; Scott, N.R.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40, 82–94 (1993)CrossRefGoogle Scholar
  40. 40.
    Du, Y.-C.; Lin, C.-H.; Shyu, L.-Y.; Chen, T.: Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis. Expert Syst. Appl. 37, 4283–4291 (2010). CrossRefGoogle Scholar
  41. 41.
    Amancio, D.R.; Comin, C.H.; Casanova, D.; Travieso, G.; Bruno, O.M.; Rodrigues, F.A.; Da Fontoura Costa, L.: A systematic comparison of supervised classifiers. PLoS ONE 9, 1–14 (2014). CrossRefGoogle Scholar
  42. 42.
    Afzal, T.; Iqbal, K.; White, G.; Wright, A.B.: A method for locomotion mode identification using muscle synergies. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 608–617 (2017). CrossRefGoogle Scholar
  43. 43.
    Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 30, 451–462 (2000). CrossRefGoogle Scholar
  44. 44.
    Sreerama, K.M.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min. Knowl. Discov. 2, 345–389 (1998). CrossRefGoogle Scholar
  45. 45.
    Cover, T.; Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967). CrossRefzbMATHGoogle Scholar
  46. 46.
    Zhang, H.: The optimality of naive Bayes. In: Proceedings of Seventeenth International Florida Artificial Intelligence Research Society Conference FLAIRS 2004, vol. 1, pp. 1–6 (2004).
  47. 47.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recog. Lett. 27, 861–874 (2006)CrossRefGoogle Scholar
  48. 48.
    Hand, D.J.; Till, R.J.: A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn. 45, 171–186 (2001). CrossRefzbMATHGoogle Scholar
  49. 49.
    Parmar, C.; Grossmann, P.; Bussink, J.; Lambin, P.; Aerts, H.J.W.L.: Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 5(13087), 1–11 (2015). Google Scholar
  50. 50.
    Warren, D.J.; Member, S.; Kellis, S.; Nieveen, J.G.; Wendelken, S.M.; Davis, S.; Clark, G.A.; Normann, R.A.; Hutchinson, D.T.; Fellow, V.J.M.: Recording and decoding for neural prostheses. Proc. IEEE 104, 374–391 (2016). CrossRefGoogle Scholar
  51. 51.
    Geethanjali, P.; Ray, K.K.: A low-cost real-time research platform for EMG pattern recognition-based prosthetic hand. IEEE/ASME Trans. Mechatron. 20, 1948–1955 (2015)CrossRefGoogle Scholar
  52. 52.
    Huang, S.; Ferris, D.P.: Muscle activation patterns during walking from transtibial amputees recorded within the residual limb-prosthetic interface. J. Neuroeng. Rehabil. 9, 55 (2012). CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.EIEDThapar UniversityPatialaIndia

Personalised recommendations