{"title":"计算机辅助舞蹈教学资源管理系统的设计与实现","authors":"Peng Huang","doi":"10.3233/JIFS-219026","DOIUrl":null,"url":null,"abstract":"Traditional teaching methods are limited to time and place, and the performance of dance teaching resources management is poor. Design a computer-assisted dance teaching resource management system. The functional structure of the system includes core computer-assisted teaching and teaching management applications. The data management module is used to store the processed data in data files, and the dance teaching content release module retrieves requests and multimedia. The remote image resource location request of the management module responds to the feedback. In order to improve the management of computer-aided dance teaching resources, this article takes dance robots as the research object, takes dance video information as input, uses deep learning methods to estimate the human body posture in the video, and obtains the key point position coordinates of the human body; The inverse kinematics calculation of the robot obtains the angle values of each joint of the robot, and the angle values of the lower body joints are adjusted to maintain the balance of the robot. In addition, this paper also proposes a method to automatically generate robot dance sequence. Gated cyclic unit (GRU) network is used to learn the correlation between the global characteristics of music and dance gesture relationship characteristics, the correlation between music local characteristics and dance movement density characteristics, and then combine the dance movement graphs to sample and plan Robot dance moves synchronized with the beat. Experimental results show that whether it is robot dance movement imitation or dance movement generation, it can improve the computer-aided management of dance teaching.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design and implementation of computer aided resource management system for dance teaching\",\"authors\":\"Peng Huang\",\"doi\":\"10.3233/JIFS-219026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional teaching methods are limited to time and place, and the performance of dance teaching resources management is poor. Design a computer-assisted dance teaching resource management system. The functional structure of the system includes core computer-assisted teaching and teaching management applications. The data management module is used to store the processed data in data files, and the dance teaching content release module retrieves requests and multimedia. The remote image resource location request of the management module responds to the feedback. In order to improve the management of computer-aided dance teaching resources, this article takes dance robots as the research object, takes dance video information as input, uses deep learning methods to estimate the human body posture in the video, and obtains the key point position coordinates of the human body; The inverse kinematics calculation of the robot obtains the angle values of each joint of the robot, and the angle values of the lower body joints are adjusted to maintain the balance of the robot. In addition, this paper also proposes a method to automatically generate robot dance sequence. Gated cyclic unit (GRU) network is used to learn the correlation between the global characteristics of music and dance gesture relationship characteristics, the correlation between music local characteristics and dance movement density characteristics, and then combine the dance movement graphs to sample and plan Robot dance moves synchronized with the beat. Experimental results show that whether it is robot dance movement imitation or dance movement generation, it can improve the computer-aided management of dance teaching.\",\"PeriodicalId\":44705,\"journal\":{\"name\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/JIFS-219026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fuzzy Logic and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JIFS-219026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Design and implementation of computer aided resource management system for dance teaching
Traditional teaching methods are limited to time and place, and the performance of dance teaching resources management is poor. Design a computer-assisted dance teaching resource management system. The functional structure of the system includes core computer-assisted teaching and teaching management applications. The data management module is used to store the processed data in data files, and the dance teaching content release module retrieves requests and multimedia. The remote image resource location request of the management module responds to the feedback. In order to improve the management of computer-aided dance teaching resources, this article takes dance robots as the research object, takes dance video information as input, uses deep learning methods to estimate the human body posture in the video, and obtains the key point position coordinates of the human body; The inverse kinematics calculation of the robot obtains the angle values of each joint of the robot, and the angle values of the lower body joints are adjusted to maintain the balance of the robot. In addition, this paper also proposes a method to automatically generate robot dance sequence. Gated cyclic unit (GRU) network is used to learn the correlation between the global characteristics of music and dance gesture relationship characteristics, the correlation between music local characteristics and dance movement density characteristics, and then combine the dance movement graphs to sample and plan Robot dance moves synchronized with the beat. Experimental results show that whether it is robot dance movement imitation or dance movement generation, it can improve the computer-aided management of dance teaching.
期刊介绍:
The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.