Wellness Monitoring: Metrics, Baselines, and Personalized Insights
Description
The provided text discusses integrating subjective wellness questions with objective health metrics like steps per day, resting heart rate (RHR), heart rate variability (HRV), and heart rate zones to gain deeper insights into an individual's well-being and physical state. It emphasizes the importance of establishing personalized baselines and thresholds for these metrics and using machine learning techniques to detect anomalies, identify patterns, and combine subjective user input with objective data for tailored recommendations regarding stress, recovery, and training intensity. The ultimate goal is to create a system that adapts to individual needs and provides more accurate, actionable health and fitness guidance than relying on objective data alone.
United States
