Exoplan energy algorithm
Goal
My original desire was to plan my days as efficiently as possible to achieve maximum productivity.
Research foundation
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Focus has peak hours
Attention and memory change through the day. Chronotype shifts the timing.
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Alertness has two drivers
Alertness depends on the body clock and time awake.
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Heart rate follows the body clock
Measured around the clock in a lab, heart rate peaked in the afternoon.
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Wearables can read your body clock
In more than 130,000 days of data from doctors in training, people’s rhythms varied a lot.
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Sleepiness shows up in heartbeats
On real roads, heart rate variability sorted drivers into sleepiness levels with 61% accuracy.
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Heart data helps predict fatigue
In a 27-person pilot, wearable data predicted physical fatigue with about 70% precision.
High-level overview
1. Collect
For each day: heart rate, active energy, and when you fell asleep and woke up. Time counts from wake-up, so early and late mornings line up.
2. Clean
Days with an unusual wake-up time are dropped. So are the first hour after waking, the last hour before bed and any time you were moving. Odd readings go too, and the rest is averaged in 20-minute steps.
3. Fit
Each day is shifted to the same average, and the same weekdays from every week are pooled. A smooth curve runs through each pool, and the hours above a sloped line become the peaks.
4. Output
Your Exo Score each morning sets how long the peaks are. The app then lines them up with when you actually woke up. Until your watch data comes in, Exoplan places the peaks from your chronotype.
Caveats
The curve shows a typical Monday, so a hard Monday and an easy one start from the same peaks. Heart rate also rises with stress, and the model reads that as energy. Days with an unusual wake-up time are dropped, so travel leaves gaps.
I started with 12 weeks of data and moved to 8. I also tried lining days up by shifting them in time. That one stayed an experiment.
Exoplan Planning, tasks, AI assistant