Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth

Olsson, Alexander E. LU ; Sager, Paulina ; Andersson, Elin ; Björkman, Anders LU ; Malešević, Nebojša LU and Antfolk, Christian LU orcid (2019) In Scientific Reports 9(1).
Abstract

In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural... (More)

In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
9
issue
1
article number
7244
publisher
Nature Publishing Group
external identifiers
  • scopus:85065673406
  • pmid:31076600
ISSN
2045-2322
DOI
10.1038/s41598-019-43676-8
language
English
LU publication?
yes
id
dbad3679-af5d-4c5b-8552-2d7012eb645d
date added to LUP
2019-05-27 16:05:33
date last changed
2025-10-16 22:46:43
@article{dbad3679-af5d-4c5b-8552-2d7012eb645d,
  abstract     = {{<p>In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.</p>}},
  author       = {{Olsson, Alexander E. and Sager, Paulina and Andersson, Elin and Björkman, Anders and Malešević, Nebojša and Antfolk, Christian}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth}},
  url          = {{http://dx.doi.org/10.1038/s41598-019-43676-8}},
  doi          = {{10.1038/s41598-019-43676-8}},
  volume       = {{9}},
  year         = {{2019}},
}