Gesture Recognition Through Classification of Acoustic Muscle Sensing for Prosthetic Control

(Extended Abstract)
  • Samuel WilsonEmail author
  • Ravi Vaidyanathan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10384)


In this paper we present the initial evaluation of a new upper limb prosthetic control system to be worn on the residual limb, which is capable of identifying hand gestures through muscle acoustic signatures (mechanomyography, or MMG) measured from the upper arm. We report the development of a complete system consisting of a bespoke inertial measurement unit (IMU) to monitor arm motion and a skin surface sensor capturing acoustic muscle activity associated with digit movement. The system fuses the orientation of the arm with the synchronized output of six MMG sensors, which capture the low frequency vibrations produced during muscle contraction, to determine which hand gesture the user is making. Twelve gestures split into two test categories were examined, achieving a preliminary average accuracy of 89% on the offline examination, and 68% in the real time tests.


Mechanomyography Prosthetic control Gesture recognition 



We express our gratitude to Dr Enrico Franco and to those tested. The work is supported by the US Office of Naval Research Global (ONR-G) (N62909-14-1-N221) and UK-India Educational Research Initiative (UK-IERI) (IND/CONT/E/14-15/366).


  1. 1.
    Woodward, R., Gardner, M., Angeles, P., Shefelbine, S., Vaidyanathan, R.: A novel acoustic interface for bionic hand control. In: Natraj, A., Cameron, S., Melhuish, C., Witkowski, M. (eds.) TAROS 2013. LNCS (LNAI), vol. 8069, pp. 296–297. Springer, Heidelberg (2014). doi: 10.1007/978-3-662-43645-5_32 Google Scholar
  2. 2.
    Zeng, Y., Yang, Z.Y., Cao, W., Xia, C.M.: Hand-motion patterns recognition based on mechanomyographic signal analysis. In: 2009 International Conference on Future Biomedical Information Engineering (Fbie 2009), pp. 21–24 (2009)Google Scholar
  3. 3.
    Atzori, M., et al.: Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data 1, 140053 (2014). PMC Web. 11 May 2017CrossRefGoogle Scholar
  4. 4.
    Al-Timemy, A.H., Bugmann, G., Escudero, J., Outram, N.: Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J. Biomed. Health Inf. 17(3), 608–618 (2013)CrossRefGoogle Scholar
  5. 5.
    Silva, J., Chau, T., Goldenberg, A.: MMG-based multisensor data fusion for prosthesis control, pp. 2909–2912 (2003)Google Scholar
  6. 6.
    Posatskiy, A.O., Chau, T.: Design and evaluation of a novel microphone-based mechanomyography sensor with cylindrical and conical acoustic chambers. Med. Eng. Phys. 34, 1184–1190 (2012)CrossRefGoogle Scholar
  7. 7.
    Silva, J., Chau, T., Naumann, S., Heim, W., Goldenberg, A.A.: Optimization of the signal-to-noise ratio of silicon-embedded microphones for mechanomyography. In: Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1493–1496 (2003)Google Scholar
  8. 8.
    Mace, M., Abdullah-Al-Mamun, K., Vaidyanathan, R., Wang, S., Gupta, L.: Real-time implementation of a non-invasive tongue-based human-robot interface. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, pp. 5486–5491 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Imperial College LondonLondonUK

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