The electrical activity of the muscles that control finger movements can be extracted during the performance of these movements and using machine learning techniques, the myoelectric signals can be decoded and classified according to the movement that generated the specific signal. The focus of this paper is to classify sEMG signal using easily accessible cheap hardware to capture the signal. Furthermore, to employ neural networks to classify the signal using established methodology i.e. feature extraction, with the highest possible accuracy. To classify these sEMG signals, an LSTM network has been developed and was able to classify 12 individual finger movements with accuracies reaching 90%.
|Title of host publication||2020 International Symposium on Community-Centric Systems, CcS 2020|
|Number of pages||8|
|Publication status||Published - 20 Oct 2020|
|Name||2020 International Symposium on Community-Centric Systems, CcS 2020|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
- Finger Movement Classification