An online spectral information-enhanced approach for artifact detection and fault attentuation in myoelectric control

Martyna Stachaczyk, Seyed Farokh Atashzar, Dario Farina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In myocontrol of neuroprosthetic devices, multichannel electromyography (EMG) can be used to decode the intended motor command, based on distributed activation patterns of stump muscles. In this regard, the high density EMG (HD-EMG) approach allows for enhancement of the spatiotemporal resolution for motor intention detection. Despite the advantages of relying on several EMG channels, the challenge of high-density electrode systems is the dynamically changing electrode-skin contact impedance, which can affect a considerable number of electrodes over the time of data acquisition. This can result in obtaining unreliable, low-quality EMG recording with a distributed artifact pattern over the grid of EMG sensors. To address this issue, we propose a novel online approach for adaptive information extraction and enhancement for automatic artifact detection and attenuation in HD-EMG-based myocontrol of prosthetic devices. The method is based on an adaptive weighting scheme that modifies the contribution of each HD-EMG channel considering the spectral information content relative to artifacts. The technique (named IE-HD-EMG) was tested as an online pre-conditioning step for a challenging multiclass classification problem of 4-finger activation, using linear discriminant analysis. It is shown that for this application, the proposed IE-HD-EMG technique led to a superior performance in finger activation recognition (79.25% accuracy, 89% sensitivity, 89.15% specificity) in comparison to the conventional HD-EMG recording under the same condition without the proposed approach (56.25% accuracy, 61.3% sensitivity, 67% specificity). Therefore, the proposed technique can have a significant potential to expand the clinical viability of HD-EMG systems.

Original languageEnglish (US)
Title of host publication2019 IEEE 16th International Conference on Rehabilitation Robotics, ICORR 2019
PublisherIEEE Computer Society
Pages671-675
Number of pages5
ISBN (Electronic)9781728127552
DOIs
StatePublished - Jun 2019
Event16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019 - Toronto, Canada
Duration: Jun 24 2019Jun 28 2019

Publication series

NameIEEE International Conference on Rehabilitation Robotics
Volume2019-June
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901

Conference

Conference16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019
CountryCanada
CityToronto
Period6/24/196/28/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Rehabilitation
  • Electrical and Electronic Engineering

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