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Preliminary study on the badminton backhand skill analysis based on the tactile glove
Summary
This study introduces an AI-powered tactile glove for analyzing badminton backhand strokes. The system accurately classifies skills, offering insights to improve amateur performance and prevent injuries.
Area of Science:
- Sports Science
- Artificial Intelligence
- Biomechanics
Background:
- Badminton amateurs often lack scientific training guidance.
- Technological advancements enable intelligent, quantitative approaches in sports training.
Purpose of the Study:
- To develop an AI-based method for analyzing badminton backhand skills using a tactile glove.
- To classify different backhand strokes and analyze finger contributions.
- To provide insights for improving amateur performance and injury prevention.
Main Methods:
- A dataset of stroke tactile data was created from four high-level players performing four backhand skills.
- A neural network model was developed to classify these backhand strokes.
- Finger contribution analysis was performed for different backhand techniques.
Main Results:
- The neural network achieved high classification accuracies for individual skills (98.87%-99.87%) and overall (99.46%).
- Analysis revealed significant finger contributions, e.g., thumb and index finger in active straight-shot.
- Results highlight the importance of specific finger actions for skill execution.
Conclusions:
- AI-based analysis using tactile gloves shows significant potential for enhancing badminton training.
- This technology can guide amateurs in skill improvement and injury prevention.
- The study paves the way for intelligent sports training systems.
