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Wearable Real-Time Skateboard Trick Visualization and Its Community Perception
IEEE Computer Graphics and Applications
|January 24, 2017
Summary
This study introduces a real-time skateboarding trick classification system using a 9D inertial-magnetic measurement unit (IMMU). Skateboarders found the motion visualization technology useful and accepted its potential for sports analysis and engagement.
Area of Science:
- Sports Technology
- Motion Analysis
- Human-Computer Interaction
Background:
- Motion visualization enhances understanding in sports.
- Sensor technology can capture athlete movements for analysis.
- Skateboarding lacks advanced real-time motion feedback systems.
Purpose of the Study:
- To propose and evaluate a real-time trick classification and visualization system for skateboarding.
- To assess skateboarders' perceptions of such a technology's usefulness and acceptance.
- To gather insights for future development of sports motion analysis tools.
Main Methods:
- Development of a real-time system utilizing a 9D inertial-magnetic measurement unit (IMMU).
- Implementation of algorithms for trick classification and motion visualization.
- Conducting a survey with skateboarders to gather feedback on the system.
Main Results:
- The proposed IMMU-based system enables real-time classification and visualization of skateboarding tricks.
- Survey results indicate positive usefulness and acceptance of the technology among skateboarders.
- Skateboarders provided valuable suggestions for future system enhancements.
Conclusions:
- The IMMU-based system offers a viable solution for real-time skateboarding motion analysis.
- The technology has the potential to benefit athletes, judges, and spectators.
- Further development based on user feedback can enhance the system's practical application in sports.
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