AthletiQ：AI驱动的板球生物力学分析平台

AthletiQ is a professional cricket performance diagnosis platform developed by milansinghal2004. It integrates cutting-edge AI and computer vision technologies (SAM2, MediaPipe, R3D-18 CNN, Ollama LLM) to convert training videos into actionable biomechanical feedback, aiming to provide elite-level insights for players and coaches.

AthletiQ was created to address gaps in traditional cricket motion analysis—such as insufficient player segmentation accuracy and lack of contextual, coach-like feedback. Its core goal is to offer scientific, data-driven technical guidance to help players refine their skills, from professionals to hobbyists.

Core Components:

1. Meta SAM2: High-precision object tracking for player segmentation in complex scenes.
2. MediaPipe: 12-point key pose extraction (elbow, knee, hip, shoulder etc.) for fine-grained motion analysis.
3. R3D-18 CNN: Classifies over 10 cricket shot types (Cover Drive, Pull, Flick etc.).
4. Segmented DTW: Time alignment of player actions with professional benchmarks for fair comparison.
5. Ollama LLM: Generates context-aware, coach-style technical reports.

System Architecture:

• Frontend: Cyber-Command themed UI with glassmorphism (JS/CSS).
• Orchestration: Node.js (Express) backend for session management and task triggering.
• AI Processing: Python-based engine (Gradio for visualization).
• Persistence: PostgreSQL (Neon Cloud) for data storage and history tracking.

1. Authentication: User logs in to access upload interface.
2. Video Upload: Sent to Node.js Express server.
3. Analysis Trigger: Server starts Python subprocess for analysis.
4. SAM2 Tracking: Object tracking and video clipping.
5. Pose Extraction: MediaPipe extracts key pose data.
6. Time Alignment: Segmented DTW aligns actions with benchmarks.
7. Biomechanical Analysis: Calculate differences from ideal.
8. Report Generation: Ollama LLM produces technical guidance.
9. Result Display: Interactive dashboard shows insights.
10. Data Archiving: Results stored in PostgreSQL for future reference.

1. Multimodal AI Fusion: Seamless integration of computer vision (SAM2), pose estimation (MediaPipe), time series analysis (DTW), and generative AI (Ollama) for end-to-end analysis.
2. Interactive SVG Diagnostics: Real-time, clickable joint analysis with ideal range overlays for intuitive understanding.
3. Scalable Distributed Architecture: Separated frontend, orchestration, and AI layers allow independent scaling for different usage scales.

AthletiQ serves diverse users:

• Professional Players: Quantify and refine technical details.
• Youth Camps: Provide scientific action correction for young players.
• Sports Researchers: Support biomechanics studies with data.
• Hobbyists: Access professional-level analysis for skill improvement.

AthletiQ exemplifies deep AI application in sports science. By combining state-of-the-art computer vision and generative AI, it opens new possibilities for scientific cricket training. For SportsTech developers and researchers, it offers a valuable reference for building AI-powered biomechanics analysis tools.