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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Sammanfattning

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.
Originalspråksvenska
Titel på värdpublikation2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (elektroniskt)978-1-5386-1312-2
ISBN (tryckt)978-1-5386-1312-2
DOI
StatusPublished - 2019 juli
Evenemang41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Berlin, Tyskland
Varaktighet: 2019 juli 232019 juli 27

Konferens

Konferens41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Land/TerritoriumTyskland
OrtBerlin
Period2019/07/232019/07/27

Ämnesklassifikation (UKÄ)

  • Annan medicinteknik

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