Exploiting the Intertemporal Structure of the Upper-Limb sEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods
(2019) 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)- 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/85463de0-65c9-4b2f-8876-82c12420bea0
- author
- Olsson, Alexander LU ; Malesevic, Nebojsa LU ; Björkman, Anders LU and Antfolk, Christian LU
- organization
- publishing date
- 2019-07
- 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.
- conference name
- 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- conference location
- Berlin, Germany
- conference dates
- 2019-07-23 - 2019-07-27
- external identifiers
-
- scopus:85077861105
- pmid:31947357
- 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
- 2023-04-10 01:12:38
@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}}, }