Looking at myself though the eyes of the Garmin
I’ve been a dedicated Garmin Fenix user for over 7 years, starting with the Fenix 3HR and now using the latest Fenix 7.
While the Garmin app provides a clear snapshot of daily and weekly fitness data, there’s a deeper opportunity to explore the raw data and uncover insights that go far beyond app-level summaries.
This ongoing project allows me to analyze and interpret Garmin’s rich dataset, revealing:
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Long-term trends in fitness, recovery, and performance.
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Patterns influenced by sleep, stress, activity levels, and lifestyle factors.
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Anomalies that identify outliers, external influences (like stress or alcohol), or opportunities for improvement.
My goal is to bridge the gap between app overviews and deeper actionable plans—leveraging this data to optimize performance, monitor health trends, and enhance overall well-being.🚀
Think like an Data scientist
Strategy:
Define Objectives: Set measurable goals to drive actionable outcomes.
People Engagement: Collaborate with all stakeholders (just me in this case!).
Set KPIs: Identify and track actionable metrics.
Decision Making: Rely on data-driven insights to inform actions.
Data(The most important and tedious part, garbage in - garbage out) :
Collection: Gather high-quality data from reliable sources.
Preparation: Clean, organize, and structure data for usability.
Analysis: Derive meaningful insights and patterns.
Quality Checks: Ensure data accuracy, completeness, and consistency.
Feature Engineering: Craft tailored features to improve model predictions.
AI-Model:
Development: Build scalable, efficient Artificial Intelligent machine learning solutions.
Training: Use robust algorithms on high-quality data.
Evaluation: Compare model performance against benchmarks.
Validation: Ensure reliability across scenarios and datasets.
A/B Testing: Test variations to optimize performance.
Implementation/Operations ( Get shit done usually the most hard part of the process):
Deployment: Transition models to production smoothly.
Monitoring: Continuously assess performance and reliability.
Reporting: Provide clear, actionable analytics to stakeholders (me).
Model Retraining: Update models to reflect evolving trends.
Documentation: Maintain detailed records for reproducibility and compliance and future Dev.
Garmin Personal health
A data-driven action plan for health optimization
Collect and integrate comprehensive health metrics from Garmin devices and external platforms
Analyze and organize raw health data to uncover meaningful patterns and trends.
Transform raw data into insightful features that reveal deeper relationships.
Use advanced algorithms to discover hidden patterns and group similar data points.
Predictive Insights
Transform data-driven findings into actionable health strategies and recommendations.