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Journal Publication

Data Augmentation of Surface Electromyography for Hand Gesture Recognition

This publication appears in: Sensors

Authors: P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras

Volume: 20

Issue: 17

Number of Pages: 23

Publication Date: Aug. 2020


Abstract:

The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.

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Current ETRO Authors

Mr. Panagiotis Tsinganos

+32 (0)02 629 168

ptsingan@etrovub.be

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Dr. Ir. Bruno Cornelis

+32 (0)02 629 167

bcorneli@etrovub.be

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Prof. Dr. Ir. Jan Cornelis

+32 (0)02 629 293

jpcornel@etrovub.be

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Prof. Dr. Bart Jansen

+32 (0)02 629 103

bjansen@etrovub.be

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