Journal ArticleDOI
A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface
TLDR
A multi-stream convolutional neural network framework to improve the recognition accuracy of gestures by learning the correlation between individual muscles and specific gestures with a “divide-and-conquer” strategy is proposed.About:
This article is published in Pattern Recognition Letters.The article was published on 2017-12-01. It has received 164 citations till now. The article focuses on the topics: Gesture recognition & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques
Muhammad Zia ur Rehman,Asim Waris,Syed Omer Gilani,Mads Jochumsen,Imran Khan Niazi,Mohsin Jamil,Mohsin Jamil,Dario Farina,Ernest Nlandu Kamavuako +8 more
TL;DR: CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features, and this data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.
Journal ArticleDOI
Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning
TL;DR: A novel multi-View convolutional neural network (CNN) framework is proposed by combining classical sEMG feature sets with a CNN-based deep learning model, taking advantage of both early and late fusion of learned multi-view deep features.
Journal ArticleDOI
Deep Learning for EMG-based Human-Machine Interaction: A Review
TL;DR: In this paper, a literature review describes the role that deep learning plays in EMG-based human-machine interaction (HMI) applications and provides an overview of typical network structures and processing schemes.
Journal ArticleDOI
EMG-based online classification of gestures with recurrent neural networks
TL;DR: The use of recurrent neural networks (RNNs) are proposed to improve the online classification of hand gestures with Electromyography (EMG) signals acquired from the forearm muscles to achieve similar accuracy for both data sets.
Journal ArticleDOI
Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition
TL;DR: A novel ternary pattern and discrete wavelet (TP-DWT) based iterative feature extraction method is proposed and a sEMG signal recognition method is presented to automate the control of prosthetic hands through surface electromyogram signals and machine learning techniques.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
Proceedings ArticleDOI
Large-Scale Video Classification with Convolutional Neural Networks
TL;DR: This work studies multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggests a multiresolution, foveated architecture as a promising way of speeding up the training.
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Tianqi Chen,Mu Li,Yutian Li,Min Lin,Naiyan Wang,Minjie Wang,Tianjun Xiao,Bing Xu,Chiyuan Zhang,Zheng Zhang +9 more
TL;DR: The API design and the system implementation of MXNet are described, and it is explained how embedding of both symbolic expression and tensor operation is handled in a unified fashion.