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??????本文目錄如下：??????
目錄
??1 概述
??2 運行結果
??3?參考文獻
??4 Matlab代碼實現(xiàn)
??1 概述
文獻來源：
?特征提取是從肌電信號中提取有用和有價值的信息的重要步驟。然而，特征提取的過程需要先前的知識和專業(yè)知識。本文提出了一種無特征EMG模式識別技術，以解決特征提取問題。首先，使用譜圖將原始EMG信號轉換為時頻表示（TFR）。然后，將TFR或譜圖圖像直接輸入卷積神經(jīng)網(wǎng)絡（CNN）進行分類。提出了兩種CNN模型，可以從譜圖圖像中自動學習特征，無需手動特征提取。使用公開獲取的NinaPro數(shù)據(jù)庫中獲取的EMG數(shù)據(jù)對所提出的CNN模型進行評估。我們的結果表明，CNN分類器可以為手部和腕部運動的識別提供最佳的平均分類準確率為88.04%。
原文摘要：
摘要：
Feature extraction is important step to extract the useful and valuable information from the electromyography (EMG) signal. However, the process of feature extraction requires prior knowledge and expertise. In this paper, a featureless EMG pattern recognition technique is proposed to tackle the feature extraction problem. Initially, spectrogram is employed to transform the raw EMG signal into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into the convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the spectrogram images without the need of manual feature extraction. The proposed CNN models are evaluated using the EMG data acquired from the publicly access NinaPro database. Our results show that CNN classifier can offer the best mean classification accuracy of 88.04% for the recognition of the hand and wrist movements.
??2 運行結果
部分代碼：
%---Input--------------------------------------------------------------
% imgs ? ? ?: feature vector (height x width x channel x instances)
% label ? ? : label vector (instances x 1)
% kfold ? ? : Number of cross-validation
% LR ? ? ? ?: Learning rate
% nB ? ? ? ?: Number of mini batch
% MaxEpochs : Maximum number of Epochs
% FC ? ? ? ?: Number of fully connect layer (number of classes)
% nC ? ? ? ?: Number of convolutional layer (up to 3)
% nF1 ? ? ? : Number of filter in first convolutional layer
% sF1 ? ? ? : Size of filter in first convolutional layer
% nF2 ? ? ? : Number of filter in second convolutional layer
% sF2 ? ? ? : Size of filter in second convolutional layer
% nF3 ? ? ? : Number of filter in third convolutional layer
% sF3 ? ? ? : Size of filter in third convolutional layer
%---Output-------------------------------------------------------------
% A struct that contains three results as follows:
% acc ? ? ? : Overall accuracy
% con ? ? ? : Confusion matrix
% t ? ? ? ? : computational time (s)
%-----------------------------------------------------------------------
%% (1) Convolutional Neural Network with one convolutional layer
clc, clear
% Benchmark dataset
[imgs,label] = digitTrain4DArrayData;?
% Parameter setting
opts.kfold ? ? = 5;
opts.LR ? ? ? ?= 0.01;?
opts.nB ? ? ? ?= 100;?
opts.MaxEpochs = 20;?
opts.nC ? ? ? ?= 1;?
opts.FC ? ? ? ?= 10;
opts.nF1 ? ? ? = 16;?
opts.sF1 ? ? ? = [3, 3];
% Convolutional Neural Network
CNN = jCNN(imgs,label,opts);
% Accuracy
accuray = CNN.acc;
% Confusion matrix
confmat = CNN.con;
%% (2) Convolutional Neural Network with two convolutional layers
clc, clear
% Benchmark dataset
[imgs,label] = digitTrain4DArrayData;?
??3?參考文獻
文章中一些內(nèi)容引自網(wǎng)絡，會注明出處或引用為參考文獻，難免有未盡之處，如有不妥，請隨時聯(lián)系刪除。文章來源：http://www.zghlxwxcb.cn/news/detail-670487.html
[1]Too, Jingwei, et al. “Featureless EMG Pattern Recognition Based on Convolutional Neural Network.” Indonesian Journal of Electrical Engineering and Computer Science, vol. 14, no. 3, Institute of Advanced Engineering and Science, June 2019, p. 1291, doi:10.11591/ijeecs.v14.i3.pp1291-1297.文章來源地址http://www.zghlxwxcb.cn/news/detail-670487.html
??4 Matlab代碼實現(xiàn)
到了這里，關于【使用 k 折疊交叉驗證的卷積神經(jīng)網(wǎng)絡(CNN)】基于卷積神經(jīng)網(wǎng)絡的無特征EMG模式識別研究（Matlab代碼實現(xiàn)）的文章就介紹完了。如果您還想了解更多內(nèi)容，請在右上角搜索TOY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關文章，希望大家以后多多支持TOY模板網(wǎng)！
