AI Systems
AI INTEGRATION
Intelligent Sports Analysis with Language Models
AI COMPONENTS
Transforming biomechanical data
into intelligent insights.
Moriarty integrates cutting-edge AI technologies to enhance sports video analysis through intelligent data processing, natural language interfaces, and automated insights generation. The system combines computer vision outputs with language models to create intuitive, queryable interfaces for biomechanical data.
LLM Training
RAG System
Data Processing
NL Interface
LLM TRAINING SYSTEM
Converting pose data to natural language
Advanced pipeline for training language models
on biomechanical data and sports performance metrics.
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Data-to-Text Conversion
Transform 3D landmarks, confidence scores, and temporal sequences into natural language descriptions. Includes biomechanical metrics to descriptive text conversion with categorization of stride mechanics, joint kinematics, and performance summaries.
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Model Architectures
Support for GPT-3.5/4 fine-tuning, BERT for classification, T5 for text-to-text generation, and custom sports domain models. Includes LoRA/QLoRA for efficient fine-tuning and gradient accumulation strategies.
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Training Pipeline
Four-stage process: Data collection from video analysis, text generation from numerical data, dataset creation with training pairs and prompts, and model fine-tuning with evaluation metrics.
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Evaluation Metrics
BLEU scores for text quality assessment, ROUGE scores for summarization tasks, perplexity for language modeling evaluation, and domain-specific accuracy metrics for sports analysis.
BiomechanicalTextGenerator
class BiomechanicalTextGenerator:
def generate_descriptions(self, analysis_result):
"""Convert biomechanical data to natural language"""
descriptions = []
# Sprint mechanics description
stride_desc = self._describe_stride_mechanics(
analysis_result.stride_analysis
)
descriptions.append(stride_desc)
return " ".join(descriptions)
RAG SYSTEM
Retrieval-Augmented Generation
for intelligent querying
Advanced semantic search and retrieval system that enables natural language querying of biomechanical analysis results with contextual understanding.
Vector Database
Embedding Models
Query Interface
DATA PROCESSING
Intelligent data transformation
and analysis pipeline
Automated processing of biomechanical data for AI model training and inference.
Data VectorizationVector Embeddings
Embedding Generation
Convert biomechanical metrics into high-dimensional vector representations for semantic similarity search and clustering analysis.
Automated InsightsReport Generation
Performance Analysis
Generate comprehensive performance reports with automated recommendations based on biomechanical analysis patterns.
API IntegrationExternal Services
Model APIs
Integration with OpenAI, Anthropic, and other language model APIs for real-time analysis and query processing.
USAGE EXAMPLES
Practical implementation examples
# Initialize LLM training system
from src.ai_systems import LLMTrainingSystem
trainer = LLMTrainingSystem(
model_name="gpt-3.5-turbo",
training_data_path="biomech_data/"
)
# Generate training dataset
trainer.prepare_training_data()
trainer.fine_tune_model()
# Query biomechanical data using natural language
from src.ai_systems import RAGSystem
rag = RAGSystem()
# Ask questions about performance
query = "What is the average stride length for elite sprinters?"
response = rag.query(query)
print(response.answer)
print(f"Confidence: {response.confidence}")
# Integrate with external AI APIs
from src.ai_systems import AIIntegration
ai = AIIntegration(api_key="your_api_key")
# Generate performance insights
analysis_result = process_video("sprint.mp4")
insights = ai.generate_insights(analysis_result)
print(insights.summary)
print(insights.recommendations)
# Full pipeline integration
from src.pipeline import VideoPipeline
from src.ai_systems import AIEnhancedPipeline
pipeline = AIEnhancedPipeline(
enable_llm=True,
enable_rag=True
)
result = pipeline.analyze_with_ai("video.mp4")
print(result.natural_language_summary)