How Sarenica works
Every core metric has a documented method. This page explains the parts that matter most — how signals are collected, how patterns are derived, and when advanced insights unlock.
Four principles behind every metric
Everything else follows from these. They are also what separates Sarenica from black-box scoring tools.
Reproducible results using documented algorithms.
Detects patterns in your own behaviour, not generic benchmarks.
Sensor confidence penalties for more accurate readings.
Compare you to yourself over time, not population averages.
Fatigue state detection
The base signal most other metrics are derived from. Classified from four input streams with a confidence score attached to every reading.
- • Blink rate from the camera (blinks per minute)
- • Microsleep events (eyes closed more than 500 ms while active)
- • Typing rhythm (KPM, WPM, error-rate variability)
- • Mouse movement patterns (speed, distance, idle time)
Each reading carries a confidence percentage based on sensor quality. Camera FPS above 15 and face-detection ratio above 80% score normally; below those thresholds a 0–50% penalty is applied, and readings below 50% confidence are flagged unreliable.
Burnout risk scoring
A weighted combination of seven factors Hydrogen computes from your own history — never a comparison to other users.
Your fatigue state right now (Low / Normal / High / Very High).
Direction of fatigue change in the last hour.
Total minutes at High or Very High fatigue today.
Continuous work time without breaks (over two hours is a risk.)
Post-lunch dip (1–3 PM) and late evening (after 9 PM).
Consecutive days with elevated fatigue burden.
Reduces score reliability when sensor confidence is low.
Best work hours detection
Ranks 3-hour windows by lowest fatigue and highest concentration, weighted toward recent days.
- 1. Analyse hourly fatigue averages across 7–14 days of data.
- 2. Calculate concentration scores (inverse of fatigue plus productivity).
- 3. Identify 3-hour windows with lowest fatigue and highest concentration.
- 4. Weight recent days more heavily (exponential decay, 0.95^days_ago).
- 5. Rank windows by combined score.
- • Minimum 240 minutes tracked (maturity Level 2)
- • At least 2 different days of data
- • Coverage across multiple hours of the day
Personal factor correlations
Pearson correlation with significance testing across your tracked factors. Reports confounders so you can interpret the number honestly.
- • Sleep hours (from wearable) vs. next-day fatigue
- • Keyboard activity intensity vs. fatigue accumulation
- • Mouse activity patterns vs. concentration
- • Session length vs. post-session fatigue
- • Time-of-day effects on your specific patterns
Pearson correlation coefficient (r) with significance testing:
- • r > 0.5: strong positive correlation
- • r < −0.5: strong negative correlation
- • p < 0.05: statistically significant
- • Minimum 7 days of data required for reliability
- • Weekend vs. weekday differences
- • Day-of-week effects
- • Seasonal variations (when enough data exists)
Data maturity levels
Advanced insights only appear when there is enough data to make them statistically valid. Showing best-hours from one hour of tracking would be misleading.
Just started. Basic tracking only: /now, /today.
Learning your patterns. Unlocks /why_fatigue.
Patterns emerging. Unlocks /best_window.
Solid data. Unlocks /patterns, /report, /compare.
Expert mode. Unlocks /correlations and full research capabilities.
Our transparency promise
Every Hydrogen command accepts a /methodology flag that returns the coverage, confidence, and technical details used to produce that answer.
- All algorithms are deterministic and reproducible
- Quality adjustments are transparent and documented
- Statistical methods include confidence intervals and significance tests
- Confounding factors are identified and reported
- Updates to methodologies are versioned and announced
Methodology FAQ
Quick answers to the questions we hear most.
No. Core fatigue and pattern metrics are calculated using deterministic, documented methods. The AI layer helps with interpretation, question understanding, and response presentation, not raw metric calculation.
Sarenica uses a data maturity system so advanced insights are only shown when there is enough data to reduce misleading conclusions. This improves reliability for comparisons and correlations.
Sarenica applies confidence scoring and quality penalties to sensor-derived signals. Low-confidence readings are flagged and can be excluded from reliable-minute calculations and advanced analysis.
Yes. Sarenica provides methodology explanations, coverage details, confidence cues, and technical details on demand so you can understand how a result was produced.