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Gesture Recognition Through Classification of Acoustic Muscle Sensing for Prosthetic Control

(Extended Abstract)

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10384)

Abstract

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.

Keywords

  • Mechanomyography
  • Prosthetic control
  • Gesture recognition

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Acknowledgements

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).

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Correspondence to Samuel Wilson .

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Wilson, S., Vaidyanathan, R. (2017). Gesture Recognition Through Classification of Acoustic Muscle Sensing for Prosthetic Control. In: Mangan, M., Cutkosky, M., Mura, A., Verschure, P., Prescott, T., Lepora, N. (eds) Biomimetic and Biohybrid Systems. Living Machines 2017. Lecture Notes in Computer Science(), vol 10384. Springer, Cham. https://doi.org/10.1007/978-3-319-63537-8_61

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  • DOI: https://doi.org/10.1007/978-3-319-63537-8_61

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  • Publisher Name: Springer, Cham

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