典型文献
Lighting control with Myo armband based on customized classifier
文献摘要：
This paper focuses on gesture recognition and interactive lighting control.The collection of gesture data adopts the Myo armband to obtain surface electromyography(sEMG).Considering that many factors affect sEMG,a customized classifier based on user calibration data is used for gesture recognition.In this paper,machine learning classifiers k-nearest neighbor(KNN),support vector machines(SVM),and naive Bayesian(NB)classifier,which can be used in small sample sets,are selected to classify four gesture actions.The performance of the three classifiers under different training parameters,different input features,including root mean square(RMS),mean absolute value(MAV),waveform length(WL),slope sign change(SSC)number,zero crossing(ZC)number,and variance(VAR)are tested,and different input channels are also tested.Experimental results show that:The NB classifier,which assumes that the prior probability of features is polynomial distribution,has the best performance,reaching more than 95％accuracy.Finally,an interactive stage lighting control system based on Myo armband gesture recognition is implemented.
文献关键词：
中图分类号：
作者姓名：
Jiang Yujian;Yang Xue;Zhang Junming;Song Yang
作者机构：
Key Laboratory of Acoustic Visual Technology and Intelligent Control System,Communication University of China,Beijing 100024,China;Beijing Key Laboratory of Modern Entertainment Technology,Communication University of China,Beijing 100024,China;School of Information and Communication Engineering,Communication University of China,Beijing 100024,China
文献出处：
引用格式：
[1]Jiang Yujian;Yang Xue;Zhang Junming;Song Yang-.Lighting control with Myo armband based on customized classifier)[J].中国邮电高校学报（英文版）,2022(04):106-116
A类：
armband
B类：
Lighting,control,Myo,customized,This,paper,focuses,gesture,recognition,interactive,lighting,collection,data,adopts,obtain,surface,electromyography,sEMG,Considering,that,many,factors,affect,user,calibration,used,In,this,learning,classifiers,nearest,neighbor,KNN,support,vector,machines,naive,Bayesian,NB,which,can,small,sample,sets,selected,classify,four,actions,performance,three,under,different,training,parameters,input,features,including,root,mean,square,RMS,absolute,value,MAV,waveform,length,WL,slope,sign,change,SSC,number,zero,crossing,ZC,variance,VAR,tested,channels,also,Experimental,results,show,assumes,prior,probability,polynomial,distribution,has,best,reaching,more,than,accuracy,Finally,stage,system,implemented
AB值：
0.602755
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