Machine learning
Why Machine Learning?
Machine learning finds patterns in the activity and health data that are difficult to spot manually. It allows for personalized, actionable insights
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Personalization: By analyzing your individual historical data, machine learning models can suggest:
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The ideal exercise intensity for the fitness level
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Daily calorie intake tailored to your activity patterns.
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Rest days optimized for recovery and performance improvement.
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Prediction: Machine learning can forecast recovery needs by examining metrics such as heart rate variability, calorie expenditure, and activity intensity. This ensures you can plan your routine for peak performance and avoid burnout.
Why Clustering?
Clustering is a machine learning technique that identifies natural patterns in your activity data. It helps you make sense of your fitness journey by grouping similar behaviors and trends like Behavioral Grouping and Optimizing Balance
How Many Clusters Do We Need?
Now that we’ve decided to cluster our data, the next critical question is: How many clusters should we create? This decision is essential to ensure the clusters are both meaningful and actionable.
Approach to Determining Optimal Clusters
To determine the optimal number of clusters, I’ll leverage two widely used methods from machine learning theory in unsupervised learning:
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Elbow Method:
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This method examines the rate of decrease in inertia (within-cluster sum of squares).
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The "elbow point" on the curve represents the point where adding more clusters provides diminishing returns.
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Davies-Bouldin Score:
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This metric measures the average similarity between each cluster and its most similar cluster.
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A lower Davies-Bouldin score indicates better-defined and more compact clusters.
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Why These Methods?
Both the Elbow Curve and the Davies-Bouldin score provide complementary perspectives:
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The Elbow Method focuses on minimizing internal variance within clusters.
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The Davies-Bouldin score emphasizes the separation between clusters while maintaining compactness.
Elbow Method:
The Elbow Method shows a clear "elbow" or bend in the curve around k=4, where the rate of decrease in inertia (within-cluster sum of squares) slows down significantly. This indicates that adding more clusters beyond this point provides diminishing returns.
Davies-Bouldin Method:
In this analysis, lower Davies-Bouldin scores indicate better-defined clusters. There is a notable local minimum around k=4, which aligns with the elbow point, and another dip at k=10. These points suggest well-defined cluster separations at these values.
Conclusion
Taking these results into consideration:
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I will proceed with 4 clusters, as this choice strikes a balance between interpretability and cluster quality.
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This aligns well with typical patterns observed in fitness tracking, such as categorizing days into exercise, recovery, rest, and mixed-activity patterns.
This approach ensures that the clustering is both actionable and meaningful, supporting better insights into activity and recovery patterns.
So our final clusters would be High Intensity, Moderate-High Mix, Low Intensity and Rest/Recovery days
Rest Days (Grey)
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Clustered around low heart rates (50-70 bpm) and low step counts (<10,000 steps).
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Some rest days still show higher step counts, indicating active recovery through light movement.
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Incorporating low-intensity sessions between intense training blocks can reduce fatigue while maintaining movement.
Low-Intensity Days (Blue)
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Fall within the 70-80 bpm range and 5,000 to 15,000 steps, typically steady-state cardio, walks, or light recovery workouts.
Moderate-High Mix (Green)
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Occurs between 70-85 bpm with higher variability in step count (10,000 - 25,000 steps), represents mixed-intensity training days, balancing endurance and strength components.
High-Intensity Days (Red)
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Clearly distinct, with higher heart rates (80-95 bpm) and elevated step counts (>15,000 steps). Represents peak workout sessions, interval training, and long-duration endurance runs.
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Given their lower frequency (17.6%), strategically placing them when HRV, sleep, and recovery metrics align well can maximize performance gains.
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Rest Days (~30%) of the days are dedicated to rest, ensuring proper recovery and minimizing injury risks.
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Low-Intensity Days (22.1%): These sessions help maintain consistency without adding excessive fatigue.
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Moderate to High Mix (31.0%): The largest portion, representing structured training sessions that build endurance and fitness.
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High-Intensity Days (17.6%): Focused on performance gains, strength, and cardiovascular improvements.
Key Takeaways:
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The distribution shows a well-balanced approach, ensuring both progression and recovery.
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The mix of high, moderate, and low-intensity training allows for sustainable performance improvements.
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The rest-to-intensity ratio aligns with best practices for long-term fitness and injury prevention.
Next Steps
I've already explored commercial CGMs to analyze daily glucose fluctuations in relation to my health data, alongside tracking blood pressure baselines and conducting periodic cortisol tests. Additionally, I've monitored lipid profiles, ApoB, HbA1c, triglycerides, and other key biomarkers through lab work.
What's truly fascinating is the deep correlation between these physiological markers and my stress-recovery cycles—how fluctuations in one metric can ripple across others, influencing overall health and performance.
Looking ahead, I plan to take this further with advanced epigenetic testing, DNA methylation analysis, hormonal profiling, and DEXA scans.
These will provide deeper insights into longevity, metabolic efficiency, and body composition,
Next Steps on the Artificial Intelligence Roadmap in Fitness & Recovery
1. Classification
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Sleep Pattern Recognition: Identify restful vs. disturbed nights using HRV, sleep quality, and previous day’s activity levels.
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Optimal Activity Intensity: Classify ideal workout intensity based on past performance, sleep data, and recovery metrics using Random Forests & Decision Trees.
2. Anomaly Detection
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Use deep learning models to detect irregular trends in fitness, training strain, and rest status.
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Flag potential overtraining, fatigue, or unexpected HRV deviations.
3. Predictive Modeling
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Forecast Fitness & Recovery: Predict continuous outcomes like fitness levels, recovery times, or calories burned using ensemble models (stacking, bagging, boosting).
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HRV & Training Load Trends: Model long-term changes in fitness, fatigue, and recovery using Base Fitness, Fatigue, and HRV metrics.
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LSTM-Based Predictions: Utilize Long Short-Term Memory (LSTM) Networks to capture sequential dependencies in HRV, sleep, and activity data, enabling personalized training adjustments.
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Finally Develop an adaptive training system that adjusts intensity based on HRV, recovery, and fatigue metrics.
Use Reinforcement Learning (RL) to continuously refine workout recommendations based on past performance.
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