Section A: Project Information
This VR-based fencing training platform revolutionizes access to the sport by combining immersive technology with pedagogically training design. At its core, the system leverages Meta Quest 3's hand tracking and haptic feedback to authentically replicate foil techniques—from basic footwork to advanced parry-riposte sequences—while eliminating traditional barriers like high equipment costs and injury risks. The innovation lies in its biomechanically accurate motion capture and adaptive AI opponents that dynamically adjust to user skill levels using probabilistic attack algorithms.
Additionally, the platform integrates dedicated learning and practice modules, enabling students to engage in instructional video study and scenario-based training simulations. When students struggle with specific techniques, they can consult the AI coach for real-time movement analysis and personalized feedback.
Section B: Participant Information
| Title | First Name | Last Name | Organisation/Institution | Faculty/Department/Unit | Phone Number | Current Study Programme | Current Year of Study | Contact Person / Team Leader | |
|---|---|---|---|---|---|---|---|---|---|
| Mr. | Heng Jie | Zhao | EDUHK | MIT | s1157932@s.eduhk.hk | 55148640 | Master's Programme | Year 1 |
|
Section C: Project Details
Emphasis on Fencing: Recognizes fencing as a weapon-based combat sport.
High Costs and Risks: the relatively high costs for equipment and a greater risk of injury compared to many other sports.
Limited Access: limited availability of professional training facilities and qualified coaches.
Project Goal:
Aims to leverage VR technology to enhance accessibility.
Seeks to enable more people to experience the excitement and allure of the dynamic sport of fencing.
Technologies & Resources:
Hardware: Meta Quest 3 for hand tracking and haptic feedback.
Software: Unity with XR Interaction Toolkit for physics-based blade mechanics.
Validation: Pilot testing with 50 users (25 novices, 25 intermediate fencers) to measure engagement and skill improvement.
Core Functionalities:
Real-Time Feedback: Haptic vibrations correct wrist angles during parries.
Adaptive AI: Adjusts opponent difficulty based on user performance.
Performance Metrics: Skill accuracy (mm deviation from ideal form), reaction time (ms), and SUS scores for usability.
User Experience: Gamified tutorials and progress tracking to sustain motivation.
Output Layer: Visual/haptic feedback delivered via headset.
Innovative Features:
Dynamic Parry Trainer: Uses probabilistic algorithms to simulate unpredictable attacks.
Timeline: 6-month development (3 months for core mechanics, 3 for UX polish).
Performance Metrics:
Latency (<20ms for feedback loops).
Accuracy (95% match to real-world blade trajectories).
Novel Solutions:
Cost Reduction: Replaces VR headset.
Safety Innovation: No physical contact reduces injury risk to zero.
Creative Pedagogy: "Virtual Coach" uses generative AI to personalize advice.
Enhanced Effectiveness:
Micro-Learning: Seamless Learning in home
Global Leaderboards: Foster competition among remote users.
AI Motion Detection: Identifies recurring habits and tendencies in your fighting style that may create vulnerabilities
Scalability Strategies:
Cloud-Based Matchmaking: Supports 10,000+ concurrent users.
Modular Design: Easy updates for new weapons (e.g., sabre, epee).
Sustainability:
Hardware Recycling: Partner with Meta for refurbished headsets.
Carbon Neutral: Offset server emissions via AWS’s sustainability program.
Long-Term Engagement: Seasonal tournaments and skill certifications.
Social Issues Addressed:
Equity: Serves Remote area (e.g., there are no fencing venues or qualified coaches) and disabled users (e.g., wheelchair fencing modes).
Inclusion: Gender-neutral avatars combat stereotypes in sports.
Metrics:
% users from underserved regions (target: 40%).
Skill progression rates vs. traditional training.
Community Responsiveness:
Quarterly surveys to adapt to local needs (e.g., adding left-handed tutorials).
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