Education Technology
Construction of evaluation system of 24-style Tai Chi classic technique movements based on computer vision
This paper constructs a 24-type Tai chi classic technique action evaluation system based on computer vision, adopts the literature method and other research methods, constructs a video data set containing high, middle and low quality actions, builds an evaluation system, builds an evaluation model based on human joint points, uses CNN convolutional neural network to extract joint points, classify and score, and encapsulates the model to develop the corresponding system. It has functions such as recording data, reading and analyzing video. The system supports automatic evaluation, avoids the disadvantages of traditional teaching, and promotes teaching interaction and communication.
Executive Impact: Transforming Tai Chi Education with AI
Leveraging computer vision, this research pioneers an automated Tai Chi evaluation system, delivering objective insights and driving efficiency in physical education.
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Deep Analysis & Enterprise Applications
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Improved Pose Estimation Accuracy
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Evaluation System Development Process
Collecting video Data of Tai Chi
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Video data clipping classification
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Self-built Tai Chi technical movement data set
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Taijiquan movement skill annotation
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Student Tai Chi practice video data
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Tai Chi movement skill labeling and recognition
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Evaluation result of Tai Chi technique movement
Building a Specialized Tai Chi Movement Dataset
The study constructed a dataset of 24-type Tai Chi classic technique actions, comprising 982 video clips with a frame rate of 30 fps and 1080x720 resolution. This dataset was meticulously gathered from online teaching resources, sports websites, and manually shot videos. It's structured into three skill levels: professional athletes, municipal Wushu Association members, and beginners. This hierarchical design ensures the dataset reflects diverse movement characteristics across different age and skill levels, promoting robustness and recognition accuracy for the AI model. The process involved cropping video clips to specific technical actions and filtering out poor quality or occluded footage, creating a robust foundation for the evaluation system.
9-Level
Quantitative Scoring System
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Your Implementation Roadmap
Deploying an AI-driven Tai Chi evaluation system involves strategic phases to ensure successful integration and maximum impact within your educational institution.
Phase 1: Data Collection & Model Training (2-4 months)
Gather and preprocess diverse Tai Chi video data, train initial CNN and pose estimation models.
Phase 2: System Integration & Alpha Testing (3-5 months)
Integrate trained models into a client application, conduct internal testing with experts.
Phase 3: Beta Deployment & Refinement (2-3 months)
Deploy to a pilot group of users (students/teachers), collect feedback, and refine the evaluation algorithms.
Phase 4: Full Rollout & Ongoing Support (1-2 months)
Widespread deployment, provide user training and continuous maintenance.
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