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sEMG Time-Frequency Features For Hand Movements Classification

Abstract : Surface Electro-MyoGraphic (sEMG) signals acquired on the forearm can provide information on the hand movement, which can help control a prosthetic implant for disabled people. To do so, the sEMG signals must be accurately classified despite the signals' non-stationnarity, noise from sensors, involved muscles, and patient's peculiarities. This study deals with the classification of hand movement using sEMG signals, and our main goal is to compare several feature extraction methods. We focus especially on the use of time-frequency domain for feature extraction and on several linear and non-linear methods for the dimension reduction. Methods as the Discrete Orthonormal Stockwell Transform (DOST) and Multidimensional Scaling (MDS) are applied for the first time on sEMG signals, and an extensive comparison study is performed on the combinations of the proposed methods. Classical classifiers are then used on a public dataset in order to evaluate the applied methods. Both short-time Fourier transform and Stockwell transform performed well, with respectively 90% and 91% accuracy, while promising results are obtained with DOST and MDS with classification rate 87% and significant improvement in feature extraction computation time.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03223176
Contributor : Jean-Baptiste Courbot Connect in order to contact the contributor
Submitted on : Monday, May 10, 2021 - 4:59:01 PM
Last modification on : Wednesday, May 12, 2021 - 3:30:20 AM
Long-term archiving on: : Wednesday, August 11, 2021 - 8:03:29 PM

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  • HAL Id : hal-03223176, version 1

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Somar Karheily, Ali Moukadem, Jean-Baptiste Courbot, Djaffar Ould Abdeslam. sEMG Time-Frequency Features For Hand Movements Classification. 2021. ⟨hal-03223176⟩

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