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Method for muscle activity characterization using wearable devices

  • ,
  • Oscar Linares
    ,
  • Karin Rojas
    ,
  • Edward Flores
    ,
  • Nicanor Benítes
    ,
  • Aly Auccahuasi
  • ,
  • Universidad Continental, Huancayo
    ,
  • Universidad Tecnológica del Perú
    ,
  • Universidad Nacional Federico Villarreal
    ,
  • Universidad Nacional Mayor de San Marcos
    ,
  • Universidad de Ingenieria y Tecnologia – UTEC
Research Output:
Chapter in Book/Report/Conference proceeding
Chapter
Peer-review

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Chapter
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 359-377 (19 pages)

Publication milestones

  • Published - 26/12/2023

Publication status

Published - 26/12/2023

Publisher

River Publishers
9788770040174

ISBN (Electronic)

9788770040167

Publication IDs

  • Scopus: 85183699406

Host publication title

Advancement of Data Processing Methods for Artificial and Computing Intelligence

Abstract

Currently, many upper limb prosthesis solutions are being presented, which are activated through the recording and processing of muscle activity, for which electromyography signal acquisition circuits are developed, where they are processed to find a level of activation necessary to activate various mechanisms that belong to the prosthesis. In the present work, we use a wearable device that performs the simultaneous recording of eight muscles, because it has integrated eight acquisition channels, and the device allows the recording and wireless sending of signals to various devices. As a result, we present an acquisition protocol where the registration of the arm muscles is performed, where we separate each of the signals that correspond to a particular muscle. The proposed method can be used in various applications where it is required to characterize the work of certain muscles in certain activities, which can create a database of the behavior of each muscle for certain activ-ities, in order to improve the design of prostheses. As conclusion, we present how these signals can be used to recognize characteristic patterns using arti-ficial intelligence techniques.