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Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Malešević, Nebojša LU ; Marković, DImitrije; Kanitz, Gunter; Controzzi, Marco; Cipriani, Christian and Antfolk, Christian LU (2018) In Complexity 2018.
Abstract

We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness... (More)

We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Complexity
volume
2018
pages
12 pages
publisher
Wiley Online Library
external identifiers
  • scopus:85043492681
ISSN
1076-2787
DOI
10.1155/2018/9728264
language
English
LU publication?
yes
id
ff0389a6-1e13-476e-bd2c-36ee4b801b43
date added to LUP
2018-03-22 13:39:03
date last changed
2019-01-06 13:48:21
@article{ff0389a6-1e13-476e-bd2c-36ee4b801b43,
  abstract     = {<p>We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.</p>},
  articleno    = {9728264},
  author       = {Malešević, Nebojša and Marković, DImitrije and Kanitz, Gunter and Controzzi, Marco and Cipriani, Christian and Antfolk, Christian},
  issn         = {1076-2787},
  language     = {eng},
  month        = {02},
  pages        = {12},
  publisher    = {Wiley Online Library},
  series       = {Complexity},
  title        = {Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals},
  url          = {http://dx.doi.org/10.1155/2018/9728264},
  volume       = {2018},
  year         = {2018},
}