How We Calculate Your Metrics
Transparency is important. This page explains how key metrics are computed and how to interpret them.
Heart Rate Variability (HRV, SDNN)
Measures variation in time between heartbeats. We use SDNN (in milliseconds) as provided by your wearable.
Formula
Variables Explained
Normal-to-normal heartbeat intervals (in milliseconds)
Standard deviation of NN intervals (in milliseconds)
How to Interpret
Often associated with better stress resilience
A typical range for many adults during daily activities
May indicate elevated stress or reduced recovery capacity
Context & Application
HRV (SDNN) is sampled periodically by the wearable (often every 30–60 seconds). It can be sparse compared to heart rate, so gaps are expected.
Respiratory Rate
Breaths per minute from your wearable when available.
Formula
Variables Explained
Respiratory rate (breaths per minute)
How to Interpret
Common resting or light activity breathing rate
May correlate with stress, anxiety, or physical exertion
May indicate a strong stress response or hyperventilation
Context & Application
Respiratory rate is typically sampled periodically (often every 1–2 minutes) and may be missing depending on device support and signal quality.
Stress Level (1-5)
Derived from your average HRV (SDNN) when HRV data is available.
Formula
Variables Explained
Average HRV SDNN (milliseconds) over the selected period
Limits x to the 1–5 range
How to Interpret
Lower stress estimate based on higher HRV
Typical range for many drives
Higher stress estimate based on lower HRV
Context & Application
This is a reflection aid and not medical advice. If HRV is missing, stress may be unavailable or shown as a gap.
Driving Events
Detection of sudden changes in motion indicating driving maneuvers.
Formula
Variables Explained
Change in acceleration (m/s²)
Velocity at time i
Velocity at previous time point
Time interval between measurements
Event thresholds vary by type
How to Interpret
Higher deceleration beyond a threshold
Higher acceleration beyond a threshold
Higher lateral acceleration beyond a threshold
Context & Application
Events are detected using device sensors. We apply smoothing filters to reduce false positives while capturing meaningful changes.
Correlation Analysis
Statistical analysis revealing relationships between driving behavior and biometrics.
Formula
Variables Explained
Pearson correlation coefficient (-1 to +1)
Individual driving metric values (e.g., speed, events)
Individual biometric values (e.g., heart rate, HRV)
Mean values of x and y datasets
How to Interpret
As one increases, the other tends to increase
Some relationship exists, but other factors may contribute
Little to no direct linear relationship
Context & Application
Correlations are most useful for patterns across time. They help answer questions like whether specific driving conditions coincide with physiological changes.
Questions?
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