集成 DTW 算法和 SVM 的太极拳教学辅助系统的改进设计

Yujie Guo
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摘要

太极拳是一种以多种健康益处和冥想功能而闻名的武术,利用技术进行体育教育使太极拳等传统习俗得以设定协调的目标。本研究介绍了一种智能太极拳教学辅助系统,该系统由动态时间扭曲算法和支持向量机整合而成,可为练习者提供实时反馈,以提高太极拳的学习效果和质量。在该系统中,DTWA 动态时间扭曲算法用于将练习者的复杂肢体动作与太极标准动作数据集进行精确比较,同时考虑执行速度偏差等因素。同时,采用 SVM 对动作的质量和正确性进行分类,从而能够提供精确的个性化反馈。这种混合方法既能确保较高的动作识别准确率,又能满足细微的太极拳要求。通过对不同水平的太极经验进行详细测试,对该系统进行了评估。评估结果表明,学生对大多数太极拳动作和相关形体练习的表现和理解能力都有明显提高。这表明该系统对初学者、中级和高级太极拳高手都有实际应用价值。它还显示了结合 DTW 和 SVM 来支持学习者在体能学习环境中的身体运动轨迹的有效性,为他们提供了更多的技术辅助体能训练应用。这为未来体育教育中融入复杂的人工智能技术提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM
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.
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