A comprehensive data processing tool that integrates multiple data sources to provide personalized performance insights and optimization strategies for endurance athletes.
KineticAI is designed to help athletes optimize their training and recovery by providing deep insights into their performance data. By integrating both quantitative and qualitative data sources, the tool offers a unified platform for analyzing various factors that influence athletic performance.
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Sentiment Analysis of Workout Notes (planned)
- Uses Large Language Models (LLMs) to analyze athletes' workout notes from intervals.icu, extracting sentiments, emotions, and key themes to correlate subjective experiences with performance metrics.
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Multi-Source Data Integration (planned)
- Analyzes data such as sleep duration and quality, heart rate variability (HRV), subjective self-evaluations, and weather conditions to assess their impact on training outcomes.
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Hill Climbing Efficiency Calculation (planned)
- Examines running power versus pace across different gradients to measure hill climbing efficiency, helping athletes understand their strengths and areas for improvement.
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Automated Power-Based Race Predictor (planned)
- Utilizes power data, running effectiveness, and Riegel exponent calculations from all activities to predict race finish times, aiding in goal setting and race preparation.
The tool's architecture includes:
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Data Ingestion
- Collecting data from various sources like intervals.icu, wearable devices, weather APIs, and user inputs.
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Data Processing and Analysis
- Processing quantitative data using statistical methods and machine learning algorithms.
- Analyzing qualitative data using LLMs for sentiment and theme extraction.
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Data Storage
- Storing processed data in a scalable database for efficient retrieval and analysis.
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API Services
- Providing RESTful APIs for accessing data and analysis results.
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User Interface
- Offering dashboards and visualizations for athletes to interact with their data and insights.
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Programming Language
- Python 3.8+ or Java 11+
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Tools
- Nix
- Docker (for containerization)
- Git (for version control)
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API Keys
- intervals.icu API
- OpenAI API (for LLM integration)
- Weather API (e.g., OpenWeatherMap)
Installation instructions will be added once the codebase is established.
Contributions are welcome! Please open an issue or submit a pull request for any improvements, features, or bug fixes.
This project is licensed under the MIT License.
- intervals.icu for providing access to their incredible API.
- The endurance sports community for inspiration and support.