Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework
This study presents the implementation of an AI-based prototype for recognizing Korean traditional dance movements using a metadata-enhanced dataset. The research was conducted in three stages. First, a classification framework was developed to reflect the unique characteristics of Korean traditiona...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/409 |
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| author | Hae Sun Kim |
| author_facet | Hae Sun Kim |
| author_sort | Hae Sun Kim |
| collection | DOAJ |
| description | This study presents the implementation of an AI-based prototype for recognizing Korean traditional dance movements using a metadata-enhanced dataset. The research was conducted in three stages. First, a classification framework was developed to reflect the unique characteristics of Korean traditional dance. Second, video data were collected from existing and newly filmed sources, and a metadata set was created by labeling five fundamental movements for training. Third, the BlazePose model was applied to generate real-time skeletal key points, which were integrated with the metadata-enhanced dataset and processed using a customized approach to recognize dance movements in real time. The developed prototype successfully recognizes five fundamental Korean traditional dance movements and demonstrates the potential of AI technology in analyzing complex motion patterns. By integrating existing AI models with a domain-specific dataset, this study provides a systematic approach to the digital preservation and modern reinterpretation of traditional arts. Furthermore, the methodology can be extended to recognize dance movements from other cultures, offering new possibilities for the preservation and transmission of intangible cultural heritage through digital technology. |
| format | Article |
| id | doaj-art-20197be185cb456a9d58af2ce8794bd6 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-20197be185cb456a9d58af2ce8794bd62025-01-10T13:15:27ZengMDPI AGApplied Sciences2076-34172025-01-0115140910.3390/app15010409Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced FrameworkHae Sun Kim0Korea Culture Technology Institute, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of KoreaThis study presents the implementation of an AI-based prototype for recognizing Korean traditional dance movements using a metadata-enhanced dataset. The research was conducted in three stages. First, a classification framework was developed to reflect the unique characteristics of Korean traditional dance. Second, video data were collected from existing and newly filmed sources, and a metadata set was created by labeling five fundamental movements for training. Third, the BlazePose model was applied to generate real-time skeletal key points, which were integrated with the metadata-enhanced dataset and processed using a customized approach to recognize dance movements in real time. The developed prototype successfully recognizes five fundamental Korean traditional dance movements and demonstrates the potential of AI technology in analyzing complex motion patterns. By integrating existing AI models with a domain-specific dataset, this study provides a systematic approach to the digital preservation and modern reinterpretation of traditional arts. Furthermore, the methodology can be extended to recognize dance movements from other cultures, offering new possibilities for the preservation and transmission of intangible cultural heritage through digital technology.https://www.mdpi.com/2076-3417/15/1/409Korean traditional dance classification frameworkKorean traditional dance AIautomatic dance recognition of dance movementsAI dance recognitioncultural heritage digitization |
| spellingShingle | Hae Sun Kim Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework Applied Sciences Korean traditional dance classification framework Korean traditional dance AI automatic dance recognition of dance movements AI dance recognition cultural heritage digitization |
| title | Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework |
| title_full | Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework |
| title_fullStr | Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework |
| title_full_unstemmed | Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework |
| title_short | Real-Time Recognition of Korean Traditional Dance Movements Using BlazePose and a Metadata-Enhanced Framework |
| title_sort | real time recognition of korean traditional dance movements using blazepose and a metadata enhanced framework |
| topic | Korean traditional dance classification framework Korean traditional dance AI automatic dance recognition of dance movements AI dance recognition cultural heritage digitization |
| url | https://www.mdpi.com/2076-3417/15/1/409 |
| work_keys_str_mv | AT haesunkim realtimerecognitionofkoreantraditionaldancemovementsusingblazeposeandametadataenhancedframework |