Ai fitness coach Strava data analysis and personalized training insights

Detailed Title "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n"

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Description This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp.

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Workflow Details

Trigger: Strava Activity Updates - Node: Strava Trigger - Purpose: Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more. - Integration: Uses Strava API for real-time synchronization.

Step 1: Data Preprocessing - Node: Code - Purpose: Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes. - Logic: A recursive function flattens JSON input to create a clean and readable structure.

Step 2: AI Analysis with Google Gemini - Node: Google Gemini Chat Model - Purpose: Leverages Google Gemini's advanced language model to analyze the activity data. - Functionality: - Identifies key performance metrics. - Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling). - Offers tailored recommendations and motivational advice.