Revolutionary AI platform that teaches physical skills using real-time computer vision, physics-based simulation, and predictive feedback
CueAI is building the future of physical skills education through AI-native technology. We combine real-time computer vision, advanced physics simulation, and predictive AI to create an intelligent platform that can teach any physical skill with unprecedented precision and personalization.
Democratizing physical skills mastery through AI-powered instruction that adapts to every learner's unique needs.
- Ultra-precise motion tracking with sub-millimeter accuracy
- Multi-angle analysis for complete movement understanding
- Adaptive lighting compensation for any environment
- Real-time object detection and classification
- Advanced physics engines for realistic movement prediction
- Collision detection and response modeling
- Force and momentum analysis for technique optimization
- 3D trajectory reconstruction for spatial understanding
- Personalized learning algorithms that adapt to individual progress
- Predictive error detection before mistakes happen
- Real-time technique optimization suggestions
- Performance trend analysis and improvement forecasting
Our first application demonstrates the platform's capabilities through pool/billiards analysis, showcasing:
- Individual ball-by-ball analysis with precise calibration
- Spin prediction using advanced trajectory analysis
- Strategic scanning phases (rack → break → individual shots)
- Real-time performance optimization with smart frame skipping
The core AI engine that implements:
- Multi-skill detection with adaptive algorithms
- Context-aware analysis based on skill progression
- Individual technique analysis with pre-execution, execution, and post-execution phases
- Performance prediction using trajectory analysis
- Real-time optimization with adaptive quality adjustment
@dataclass
class LearningContext:
skill_level: str
current_phase: str
technique_focus: str
improvement_goals: List[str]
adaptive_difficulty: float@dataclass
class TechniqueAnalysis:
technique_id: int
frame_start: int
frame_end: int
initial_position: Tuple[float, float]
target_position: Tuple[float, float]
execution_angle: float
execution_distance: float
predicted_outcome: str
confidence_score: float
technique_quality: str
improvement_score: float- Skill Assessment (0-10s): Analyzes current skill level and technique
- Technique Breakdown (10-20s): Identifies specific areas for improvement
- Personalized Instruction (20s+):
- Preparation Phase: Guides optimal positioning and setup
- Execution Phase: Provides real-time feedback during movement
- Analysis Phase: Evaluates results and suggests improvements
- Multi-skill recognition: Adaptable to any physical skill
- Real-time tracking: Sub-millisecond response times
- Environmental adaptation: Works in any lighting condition
- Multi-camera support: Complete 360° movement analysis
- Realistic modeling: Accurate physics for any skill domain
- Collision prediction: Anticipates outcomes before execution
- Force analysis: Optimizes technique efficiency
- 3D reconstruction: Full spatial understanding
- Personalized algorithms: Adapts to individual learning styles
- Predictive feedback: Anticipates and prevents errors
- Progress tracking: Continuous improvement monitoring
- Skill transfer: Applies learning across related skills
- Python 3.8+
- OpenCV 4.8+
- NumPy
- yt-dlp
# Clone the repository
git clone https://github.com/ab0626/CueAI.git
cd CueAI
# Install dependencies
pip install -r requirements.txtopencv-python>=4.8.0
numpy>=1.21.0
yt-dlp>=2023.0.0
matplotlib>=3.5.0
from video_pool_analyzer import UltraFocusedPoolAnalyzer
# Initialize AI learning platform
platform = UltraFocusedPoolAnalyzer()
# Analyze skill performance
video_url = "https://www.youtube.com/watch?v=-fCIN8RQp9s"
platform.analyze_skill_performance(video_url, start_time=0.32)# Run the AI learning platform
python test_video_analyzer.py "https://www.youtube.com/watch?v=-fCIN8RQp9s"- Adaptable algorithms: Works with any physical skill
- Skill-specific analysis: Tailored to each domain's requirements
- Cross-skill learning: Identifies transferable techniques
- Progressive difficulty: Scales with skill development
- Instant analysis: Sub-second response times
- Predictive guidance: Anticipates optimal technique
- Error prevention: Identifies issues before they occur
- Success prediction: Forecasts likely outcomes
- Individual adaptation: Customizes to each learner's needs
- Progress tracking: Monitors improvement over time
- Goal setting: Establishes and tracks learning objectives
- Motivation optimization: Maintains engagement and progress
- Performance metrics: Comprehensive skill assessment
- Trend analysis: Identifies improvement patterns
- Comparative analysis: Benchmarks against standards
- Predictive modeling: Forecasts future performance
platform = UltraFocusedPoolAnalyzer(
analysis_fps=25, # Real-time analysis rate
technique_preparation_frames=30, # Setup analysis
execution_tracking_frames=60, # Movement tracking
feedback_analysis_frames=90 # Outcome evaluation
)- Skill detection: Adaptive algorithms for any physical skill
- Performance analysis: Real-time technique evaluation
- Feedback generation: Personalized improvement suggestions
- Real-time overlays: Live technique visualization
- Trajectory paths: Movement optimization suggestions
- Performance indicators: Success probability markers
- Improvement guides: Step-by-step technique refinement
- Skill assessments: Comprehensive performance breakdowns
- Progress metrics: Improvement tracking and forecasting
- Learning insights: Personalized development recommendations
- Adaptive processing: Scales with skill complexity
- Smart caching: Optimizes repeated analysis
- Memory optimization: Efficient data handling
- GPU acceleration: Optional hardware optimization
- Multi-threaded analysis: Parallel processing capabilities
- Optimized algorithms: Streamlined for speed and accuracy
- Athletic performance: Technique optimization for any sport
- Skill development: Progressive learning for beginners to experts
- Competition preparation: Performance optimization for events
- Injury prevention: Technique analysis to reduce risk
- Rehabilitation tracking: Progress monitoring for recovery
- Movement analysis: Technique correction for healing
- Prevention programs: Risk assessment and mitigation
- Outcome prediction: Recovery timeline forecasting
- Physical education: Enhanced learning in schools
- Research applications: Data collection for studies
- Skill transfer: Cross-domain learning optimization
- Performance science: Advanced analytics for research
- Skill recognition: Identifies and classifies physical movements
- Performance analysis: Evaluates technique quality and efficiency
- Predictive modeling: Forecasts outcomes and suggests improvements
- Personalized feedback: Generates customized learning recommendations
- Progress tracking: Monitors improvement and adjusts difficulty
- Skill classification: Deep learning for movement recognition
- Performance prediction: Regression models for outcome forecasting
- Personalization: Adaptive algorithms for individual learning
- Continuous improvement: Self-optimizing AI systems
- Multi-skill support: Beyond pool to any physical skill
- AR/VR integration: Immersive learning experiences
- Mobile applications: On-the-go skill development
- Cloud platform: Scalable learning infrastructure
- Advanced AI models: Next-generation learning algorithms
- Biometric integration: Heart rate, muscle activity analysis
- Social learning: Collaborative skill development
- Global accessibility: Democratizing physical skills education
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
python -m pytest tests/
# Run linting
flake8 python/This project is licensed under the MIT License - see the LICENSE file for details.
- AI Research Community: For the foundational algorithms and techniques
- Computer Vision Pioneers: For the real-time analysis capabilities
- Physics Simulation Experts: For the realistic modeling systems
- Open Source Community: For the tools and libraries that made this possible
- GitHub: https://github.com/ab0626/CueAI
- Issues: GitHub Issues
Building the future of physical skills education through AI
CueAI - Where artificial intelligence meets human potential