期刊
出版社
IEEE
DOI: 10.1109/embc.2019.8856648
关键词
-
资金
- Crafoord Foundation
- Promobilia Foundation
- European Commission under the DeTOP project [LEIT-ICT-24-2015, 687905]
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.
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