medical-11

EMG Gesture Recognition

3260 papers • 126 benchmarks • 313 datasets

Electromyographic Gesture Recognition

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Datasets

NinaPro DB2

Ninapro DB5

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Most implemented papers

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Ulysse Côté-Allard, C. Fall, Alexandre Drouin, A. Campeau-Lecours, C. Gosselin, K. Glette, François Laviolette, B. Gosselin•Tue Jan 09 2018

The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% and real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

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sEMG Gesture Recognition with a Simple Model of Attention

David Josephs, Carson Drake, Andrew Heroy, John Santerre•Thu Jun 04 2020

The proposed methodology's model simplicity represents a compelling alternative to the convolutional neural network (CNN) approaches utilized in recent research.

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An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

Ali Moin, Andy Zhou, Abbas Rahimi, Alisha Menon, Simone Benatti, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan, F. Burghardt, L. Benini, A. Arias, J. Rabaey•Tue Feb 27 2018

This work uses brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning and achieves an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days.

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Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

István Ketykó, Ferenc Kovács, K. Varga•Sun Jan 20 2019

Analysis performed on sparse and High-Density sEMG public datasets validate that the proposed model and deep-learning-based domain adaptation method outperforms state-of-the-art methods on recognition accuracy enhancement.

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A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

K. Glette, B. Gosselin, Ulysse Cot'e-Allard, Gabriel Gagnon-Turcotte, A. Phinyomark, E. Scheme, Franccois Laviolette•Mon Feb 15 2021

A new type of dataset is proposed to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol that includes the four main dynamic factors and uses an EMG-independent controller to guide movements.

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Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

J. Rouat, Y. Beilliard, Nikhil Garg, Ismael Balafrej, D. Drouin, F. Alibart•Tue Jun 08 2021

A simple yet novel approach is proposed for optimizing the spike encoding algorithm’s hyper-parameters inspired by the readout layer concept in reservoir computing, which reports performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.

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