03551nas a2200481   4500000000100000000000100001008004100002653001400043653003300057653003300090653002600123653002600149653003000175653002400205653003400229653003900263653003300302653002100335653001600356653004000372653003300412653003200445653002800477653001200505653002000517653003200537100001400569700001500583700001500598700001500613700001300628700001500641700001000656700001500666700001500681700001700696245010100713856015100814300001400965490000700979520206300986022002003049                                       d10aAnimation10aBiomedical signal processing10aCorrelation between features10adiscriminant analysis10aEMG signal processing10aFrequency domain analysis10aGesture recognition10aIntangible cultural heritages10aLDA (linear discriminant analysis)10aLinear discriminant analysis10aLow pass filters10aMYO armband10aMulti-class support vector machines10aSVM (support vector machine)10aSVM(support vector machine)10aSupport vector machines10aVectors10aVirtual reality10aVirtual-reality environment1 aY.-T. Tan1 aX.-F. Zhou1 aL.-Z. Kong1 aX.-C. Wang1 aZ.-K. Wu1 aW.-Y. Shui1 aY. Fu1 aM.-Q. Zhou1 aV. Korkhov1 aL.P. Gaspary00aResearch on Puppet Animation Controlled by Electromyography (EMG) in Virtual Reality Environment  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075570206&doi=10.13328%2fj.cnki.jos.005786&partnerID=40&md5=da980ea37b6a34033c78c42bc9bdab35  a2964-29850 v303 aQuanzhou puppet is one of the intangible cultural heritages of China. It is the physical embodiment of traditional Chinese culture. However, the large size of the puppet and inconvenience to carry and manipulate directly makes it hard to reach a wider audience. In order to realize the effective inheritance and protection of Quanzhou puppet, this study designs a virtual real-line puppet animation scheme based on gesture recognition, builds a prototype system which uses MYO Armband EMG signal to control the generation of animation, and applies it in user experiment to verify the high accuracy and easy manipulation of the algorithm. Firstly, low-pass filtering and smoothing is used to process the original multi-channel EMG data. Secondly, after eight-channel EMG signal time-domain feature and time-frequency-domain feature extraction, the dimension of the feature vector is reduced to six by linear discriminator to eliminate the correlation between features and enhance the robustness of the algorithm. Thirdly, a multi-class support vector machine is constructed which uses feature vector to determine the result of gesture recognition. Experiments show that the average recognition accuracy of offline action is 95.59\%, the average recognition accuracy of real-time action is 90.75\%, and the gesture recognition is completed within 1.1 s. For the puppet task, two users task is designed: the common users and the expert users. In the common user study, the gestures recognition accuracy is high. In the aspects of user s willingness to use and easiness to learn, the performance of this system is significantly higher than real puppets manipulation. In the expert user study, user s acceptance and usability of the system are also highly evaluated. These two user tasks indicate the system meets the requirements of real-time and accuracy, and has good interactivity and interesting. Relevant research can be widely applied to similar systems, such as computer animation. It has practical significance for experiencing and protecting the puppet.  a10009825 (ISSN)