TY - JOUR
KW - Animation
KW - Biomedical signal processing
KW - Correlation between features
KW - discriminant analysis
KW - EMG signal processing
KW - Frequency domain analysis
KW - Gesture recognition
KW - Intangible cultural heritages
KW - LDA (linear discriminant analysis)
KW - Linear discriminant analysis
KW - Low pass filters
KW - MYO armband
KW - Multi-class support vector machines
KW - SVM (support vector machine)
KW - SVM(support vector machine)
KW - Support vector machines
KW - Vectors
KW - Virtual reality
KW - Virtual-reality environment
AU - Y.-T. Tan
AU - X.-F. Zhou
AU - L.-Z. Kong
AU - X.-C. Wang
AU - Z.-K. Wu
AU - W.-Y. Shui
AU - Y. Fu
AU - M.-Q. Zhou
AU - V. Korkhov
AU - L.P. Gaspary
AB - Quanzhou 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.
DO - 10.13328/j.cnki.jos.005786
M1 - 10
N1 - Publisher: Chinese Academy of Sciences
N2 - Quanzhou 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.
SP - 2964
EP - 2985
TI - Research on Puppet Animation Controlled by Electromyography (EMG) in Virtual Reality Environment
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075570206&doi=10.13328%2fj.cnki.jos.005786&partnerID=40&md5=da980ea37b6a34033c78c42bc9bdab35
VL - 30
SN - 10009825 (ISSN)
ER -