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Exploiting the Intertemporal Structure of the Upper-Limb sEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods

Olsson, Alexander LU ; Malesevic, Nebojsa LU ; Björkman, Anders LU and Antfolk, Christian LU (2019)
Abstract
The use of natural myoelectric interfaces promises great value for a variety of potential applications, clinical and otherwise, provided a computational mapping between measured neuromuscular activity and executed motion can be approximated to a satisfactory degree. However, prevalent methods intended for such decoding of movement intent from the surface electromyogram (sEMG) based on pattern recognition typically do not capitalize on the inherently time series-like nature of the acquired signals. In this paper, we present the results from a comparative study in which the performances of traditional cross-sectional pattern recognition methods were compared with that of a classifier built on the natural assumption of temporal ordering by... (More)
The use of natural myoelectric interfaces promises great value for a variety of potential applications, clinical and otherwise, provided a computational mapping between measured neuromuscular activity and executed motion can be approximated to a satisfactory degree. However, prevalent methods intended for such decoding of movement intent from the surface electromyogram (sEMG) based on pattern recognition typically do not capitalize on the inherently time series-like nature of the acquired signals. In this paper, we present the results from a comparative study in which the performances of traditional cross-sectional pattern recognition methods were compared with that of a classifier built on the natural assumption of temporal ordering by utilizing a long short-term memory (LSTM) neural network. The resulting evaluation indicate that the LSTM approach outperforms traditional gesture recognition techniques which are based on cross-sectional inference. These findings held both when the LSTM classifier operated on conventional features and on raw sEMG and for both healthy subjects and transradial amputees. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN
978-1-5386-1312-2
978-1-5386-1312-2
DOI
10.1109/EMBC.2019.8856648
language
Swedish
LU publication?
yes
id
85463de0-65c9-4b2f-8876-82c12420bea0
date added to LUP
2019-10-08 10:28:34
date last changed
2019-10-09 10:25:21
@inproceedings{85463de0-65c9-4b2f-8876-82c12420bea0,
  abstract     = {The use of natural myoelectric interfaces promises great value for a variety of potential applications, clinical and otherwise, provided a computational mapping between measured neuromuscular activity and executed motion can be approximated to a satisfactory degree. However, prevalent methods intended for such decoding of movement intent from the surface electromyogram (sEMG) based on pattern recognition typically do not capitalize on the inherently time series-like nature of the acquired signals. In this paper, we present the results from a comparative study in which the performances of traditional cross-sectional pattern recognition methods were compared with that of a classifier built on the natural assumption of temporal ordering by utilizing a long short-term memory (LSTM) neural network. The resulting evaluation indicate that the LSTM approach outperforms traditional gesture recognition techniques which are based on cross-sectional inference. These findings held both when the LSTM classifier operated on conventional features and on raw sEMG and for both healthy subjects and transradial amputees.},
  author       = {Olsson, Alexander and Malesevic, Nebojsa and Björkman, Anders and Antfolk, Christian},
  booktitle    = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  isbn         = {978-1-5386-1312-2},
  language     = {swe},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Exploiting the Intertemporal Structure of the Upper-Limb sEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods},
  url          = {http://dx.doi.org/10.1109/EMBC.2019.8856648},
  doi          = {10.1109/EMBC.2019.8856648},
  year         = {2019},
}