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

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11 Citations (SciVal)

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.
Original languageSwedish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-5386-1312-2
ISBN (Print)978-1-5386-1312-2
DOIs
Publication statusPublished - 2019 Jul
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Berlin, Germany
Duration: 2019 Jul 232019 Jul 27

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Country/TerritoryGermany
CityBerlin
Period2019/07/232019/07/27

Subject classification (UKÄ)

  • Other Medical Engineering

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