MSK Monitoring & Return-to-Play Decision System (Azure AI Prototype)

AI-assisted workflow for tracking musculoskeletal (MSK) injuries and supporting return-to-play decisions using structured performance and recovery data.

Overview

This system captures daily athlete inputs (pain, training load, energy, readiness) and translates them into consistent monitoring, trend analysis, and decision-support outputs.

The goal is to reduce subjective decision-making and provide a more structured, data-informed approach to rehabilitation and performance progression.

🔁 System Flow

Injury Status
→ Daily Monitoring (pain, load, energy, readiness)
→ Trend Analysis
→ AI-assisted Decision Support
→ Return-to-Play Progression

📊 Example

• Input data → /data/sample-inputs.csv
• Decision output → /outputs/sample-decisions.md

Decision Logic (Simplified)

• Pain decreasing + energy stable → Progress
• Pain increasing + energy low → Modify
• Load increasing + energy dropping → Hold

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Decision Output

• Progress → Increase load or complexity
• Hold → Maintain current level
• Modify → Reduce load or adjust exercise

🚀 Next Steps

• Integrating Azure OpenAI for automated insights and summaries
• Expanding into injury risk prediction models
• Building a dashboard for real-time monitoring