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Intelligent Triathlon Coach (AI Triathlon Coach)

This workflow automatically captures users' running, swimming, and cycling activities by real-time monitoring of Strava's sports data, and conducts in-depth analysis using advanced AI models. It provides users with personalized training feedback and improvement suggestions, helping athletes accurately identify their strengths and weaknesses and develop scientific training plans. Ultimately, the analysis results are sent in a structured HTML format via email or WhatsApp, ensuring that users receive efficient exercise guidance in a timely manner, enhancing their training effectiveness and motivation.

Workflow Diagram
Intelligent Triathlon Coach (AI Triathlon Coach) Workflow diagram

Workflow Name

Intelligent Triathlon Coach (AI Triathlon Coach)

Key Features and Highlights

This workflow continuously monitors real-time updates of Strava activity data, automatically capturing users’ running, swimming, and cycling metrics. Leveraging the advanced language capabilities of Google Gemini 2.0 combined with a custom-built intelligent triathlon coaching agent, it performs in-depth analysis of athletic performance and generates personalized, data-driven training feedback and improvement suggestions. The structured analytical results are then converted into HTML format and delivered to users via email or WhatsApp, enabling intelligent and customized sports guidance.

Core Problems Addressed

  • Automates the acquisition and processing of multi-sport activity data, eliminating the tedious manual compilation of training logs.
  • Conducts deep analysis integrating multi-dimensional performance metrics (distance, pace, heart rate, power, swim stroke, etc.) to accurately identify strengths, weaknesses, and potential areas for improvement.
  • Provides tailored training plans, technical guidance, and motivational strategies specific to each sport and training environment, helping users continuously enhance their performance.
  • Ensures timely delivery of scientifically sound feedback through multiple channels, improving training efficiency and athlete engagement.

Application Scenarios

  • Individual triathletes aiming to optimize training outcomes through data science.
  • Coaching teams managing and monitoring the training progress and status of multiple athletes.
  • Sports rehabilitation specialists tracking recovery and adjusting training intensity.
  • Sports technology companies developing intelligent athletic analysis and feedback systems.
  • Fitness platforms offering customized triathlon coaching services to users.

Main Workflow Steps

  1. Strava Trigger: Listens for and captures activity update events from the Strava platform.
  2. Code Node (Data Preprocessing): Preprocesses and formats the raw activity data received.
  3. Combine Everything: Recursively flattens and consolidates multiple JSON-formatted activity records for streamlined analysis.
  4. Fitness Coach Intelligent Agent: Utilizes the Google Gemini model to deeply interpret activity data, factoring in sport-specific characteristics and user goals to generate professional training advice and feedback.
  5. Structure Output: Structures the intelligent agent’s textual output by distinguishing titles, lists, paragraphs, and other elements.
  6. Convert to HTML: Transforms the structured content into HTML format, enhancing presentation for email or message delivery.
  7. Send Email / WhatsApp Business Cloud: Sends personalized training feedback to users via email or WhatsApp channels.

Involved Systems and Services

  • Strava: Primary source of athletic data, providing real-time activity updates.
  • Google Gemini (PaLM) 2.0: Powerful natural language processing model used to generate intelligent analysis and personalized recommendations.
  • Gmail: Utilized for sending personalized email feedback.
  • WhatsApp Business Cloud API: Supports dispatching training feedback messages via WhatsApp.
  • n8n Workflow Automation Platform: Manages data flow and logic control between workflow nodes.
  • Custom JavaScript code nodes for data processing and format conversion.

Target Users and Value Proposition

  • Triathletes: Receive expert, personalized training feedback and guidance to scientifically improve running, swimming, and cycling performance.
  • Coaches and Trainers: Automate the analysis of athletic data to assist in creating more precise training plans.
  • Sports Data Analysts: Rapidly build data-driven performance analysis tools.
  • Fitness Enthusiasts and Rehabilitation Clients: Obtain customized training advice to promote health and continuous performance improvement.
  • Sports Tech Product Developers: Develop intelligent coaching solutions leveraging powerful language models and automation platforms.

Developed by Amjid Ali, this workflow integrates cutting-edge AI language models with sports data platforms to provide triathletes with scientific, precise, and motivational training guidance, empowering users to unlock their potential and achieve higher levels of athletic performance.