sis 24(5):

Research Article

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

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  • @ARTICLE{10.4108/eetsis.5771,
        author={Yujie Guo},
        title={Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={5},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={5},
        keywords={Tai Chi, Teaching Assistance System, Dynamic Time Warping (DTW), Support Vector Machine (SVM), Emotion Recognition},
        doi={10.4108/eetsis.5771}
    }
    
  • Yujie Guo
    Year: 2024
    Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM
    SIS
    EAI
    DOI: 10.4108/eetsis.5771
Yujie Guo1,*
  • 1: Physical Education Department of Science College, North China University of Technology
*Contact email: G1yujie@163.com

Abstract

Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.