CAJ | 학술논문

为提高头部体态语言表达“同意”与“不同意”态度的识别效果，提出结合贪心遗传算法和Elman神经网络的表面肌电识别方法。通过前导实验分别采集8名被试者点头与摇头时颈部肌肉的表面肌电信号，利用Wilcoxon秩和检验提取具有显著性差异的10个肌电时域特征值，进而基于贪心遗传算法优化的Elman神经网络建立体态语言识别模型。实验结果表明，该模型能成功识别自发表达“同意”与“不同意”的头部体态语言，与标准Elman神经网络和BP神经网络的识别模型相比，相关系数更高、均方误差更小，对测试集的正确识别率提高了3．2％以上，从而验证了该方法的可靠性。
위제고두부체태어언표체“동의”여“불동의”태도적식별효과，제출결합탐심유전산법화Elman신경망락적표면기전식별방법。통과전도실험분별채집8명피시자점두여요두시경부기육적표면기전신호，이용Wilcoxon질화검험제취구유현저성차이적10개기전시역특정치，진이기우탐심유전산법우화적Elman신경망락건입체태어언식별모형。실험결과표명，해모형능성공식별자발표체“동의”여“불동의”적두부체태어언，여표준Elman신경망락화BP신경망락적식별모형상비，상관계수경고、균방오차경소，대측시집적정학식별솔제고료3．2％이상，종이험증료해방법적가고성。
In order to improve the recognition effects of the "agreement"and"disagreement"attitudes expressed by the body language of the head movements , a surface electromyography ( sEMG ) approach in combination with the greedy genetic algorithm ( GGA) and the Elman neural network is proposed .The sEMG signals of the neck muscles were detected while eight participants were nodding and shaking their heads respectively during a pilot experiment . By means of the Wilcoxon ’ s signed-rank test , ten features of the sEMG time domain indices were extracted with significant differences .Furthermore , the body language recognition model was constructed based on the Elman net-work optimized by GGA .Experimental results show that the model can successfully recognize the "agreement and disagreement"attitudes spontaneously expressed by the different body languages of the head .Compared with the recognition models using the standard Elman and BP network , the correlation coefficient of this present model is higher, the mean squared error is less , and the correct recognition rate of the test set is increases by over 3.2%, which demonstrate the reliability of this approach .