Gait Analysis Intelligence Backend
A multi-agent, AI-powered backend built using n8n that transforms raw gait analysis PDF reports into structured data, clinical interpretations, and advanced myofascial insights — delivered as a single API response to a ReactJS application.
System Overview
This n8n automation functions as the **entire backend layer** for a frontend application built using ReactJS. The frontend is responsible only for UI and visualization. All intelligence, reasoning, validation, and orchestration happen inside this workflow.
The system accepts a gait analysis PDF, extracts biomechanical data, interprets the data from multiple expert perspectives using AI agents, and returns a single unified JSON response that the frontend can render immediately.
Webhook Entry Point (ReactJS Backend)
The workflow begins with a Webhook node, which acts as an HTTP API endpoint. The ReactJS application sends a POST request containing a URL to a gait analysis PDF report.
- ReactJS sends a POST request with a PDF URL
- n8n receives the request as a stateless backend
- No authentication or parsing logic exists in the frontend
This design keeps the frontend lightweight and allows the backend to evolve independently without breaking the UI.
PDF Retrieval and Text Extraction
After receiving the request, the workflow downloads the PDF using an HTTP Request node and extracts raw text using a PDF extraction node.
- PDF is fetched dynamically at runtime
- Text is extracted regardless of report formatting
- Frontend never handles file parsing complexity
Structured Gait Data Extraction
The extracted text is passed into specialized Information Extractor nodes powered by large language models constrained with strict schemas.
- Patient and study metadata
- Spatiotemporal gait parameters
- Joint-level kinematics (trunk, pelvis, hip, knee, ankle, foot)
- Left/right asymmetries
All extracted data is normalized into predictable JSON structures, ensuring frontend safety and consistency.
AI Agent 1: Clinical Interpretation Layer
The first AI agent acts as a movement specialist or sports medicine clinician. It interprets numeric gait metrics against established biomechanical norms.
- Explains deviations from normal ranges
- Provides clinician-friendly interpretations
- Generates simplified explanations for patients
- No treatment or exercise recommendations
AI Agent 2: Myofascial & Risk Analysis
The second AI agent performs higher-order reasoning based on anatomy trains and functional movement patterns.
- Identifies dominant myofascial meridians
- Infers compensation patterns
- Highlights short-term and long-term risk factors
- Structures Restore → Retrain → Reinforce phases conceptually
Normalization, Safety, and Merge Logic
Multiple Code nodes validate, parse, and normalize AI outputs to prevent malformed responses and ensure downstream stability.
- JSON parsing and error handling
- Key normalization and structure enforcement
- Safe merging of multiple intelligence streams
Unified API Response
All intelligence layers are merged into a single response object and returned synchronously to the ReactJS frontend using a Respond to Webhook node.
- Structured gait data
- Clinical interpretations
- Myofascial and risk insights
- Frontend-ready JSON contract
Key Benefits of This Automation
1. Complete Backend for ReactJS
The frontend handles only presentation. All logic, AI, and orchestration live in n8n.
2. Multi-Agent Intelligence
Separate AI agents with defined responsibilities improve accuracy, explainability, and maintainability.
3. Frontend-Safe JSON Contracts
Predictable structures eliminate fragile UI logic.
4. Clinical-Grade Explainability
Supports both clinicians and end-users with layered explanations.
5. Scalable Architecture
New agents and analysis layers can be added without breaking the system.
Final Summary
This automation demonstrates how n8n can be used as a true AI backend — orchestrating multiple intelligence layers, enforcing data integrity, and powering modern applications with complex reasoning pipelines.