Health OS
Your body.
Better observed.
A personal experiment in translating wearable data, clinical research, and nutrition context into a more legible model of one body over time.
View Live DemoDemo password
vital-atlas-52-meridian
The story
It started as a question about interpretation.
I switched from an Apple Watch to a Garmin. The hardware and training signals were useful, but the product made it hard to reason across them.
So I started building a different interface. That small design problem became a research problem: how much of the clinical and exercise science literature can be made practical when it is connected to daily wearable data, food intake, and recovery patterns?
Health OS is my working answer. It does not claim to diagnose anything. It tries to organise the evidence I already have into a clearer model: what changed, what might explain it, and what I should pay attention to next.
The useful question is not whether health software can be personalised. It is whether we any longer need to wait for it.
Most health apps are not designed for people whose bodies, conditions, or goals fall within the average. They work well when your needs fit a predefined category, but they start to break down when your health story is more specific, more chronic, more complex, or simply not commercially common enough to be deeply served.
Health OS is a small counterexample: one person, one data history, one set of goals, and a research layer that tries to make the interpretation explicit for acheiving specific & personalized goals.
The broader idea is that people can now build personal health ecosystems around problems that may never look commercially large enough for a big technology company. Patients and physicians dealing with chronic conditions, including long neglected conditions like PCOS, can create tools that track what matters to their own biology and daily life.
That does not replace clinical judgment. It changes the starting point. Instead of waiting for a generic app to decide a condition is worth serving, people can assemble data, research, and AI into systems that expose reasoning, show uncertainty, and adapt to the biological question in front of them.
AI is useful here only if it improves interpretation. The clinical evidence still matters. The wearable data still needs structure. The product problem is connecting those layers without turning ambiguity into false confidence.
Feature 01
A live map of
your entire body.
Garmin provides fragments: strain, sleep, HRV, training load, and muscle activity. Health OS groups those signals into a body level view so patterns are easier to inspect.
HRV and resting pulse are read as a recovery context, not isolated scores.
VO2 max and oxygen trends sit beside training history.
Nutrition timing is flagged as a hypothesis, not a conclusion.
Recent training volume is separated from readiness language.
Low direct stimulus is treated as missing evidence.
Recovery estimates are shown with the data that produced them.
Feature 02
Nutrition as
structured evidence.
Instead of treating food as a diary entry, the system parses plain language meals into approximate nutritional evidence that can be compared with training, recovery, and micronutrient needs.
Feature 03
The metrics your
apps rarely connect.
The interesting work is not another dashboard. It is deciding which research backed metrics are useful enough to operationalise, how uncertain each signal is, and how they should influence daily decisions.
Healthspan Score
A composite view of biological systems, useful mainly as a way to track direction rather than declare truth.
VO2 Max Trajectory
Current VO2 max is placed inside a longer trend with training load, recovery, and nutrition context.
Muscle Recovery Model
Detected muscle work is translated into a simple recovery ledger so training decisions have memory.
Stress Load Index
HRV, sleep quality, and recovery load are combined into a signal that is meant to prompt investigation.
Micronutrient Tracking
Repeated nutrition gaps become visible over time instead of disappearing inside daily food logs.
AI Analysis Layer
The AI layer is used to summarise competing signals and surface hypotheses, not to pretend certainty.
Try it yourself
The live demo is open.
Built on my own data and shared as a working prototype for people interested in health interfaces, applied AI, and quantified self systems.
Demo password
vital-atlas-52-meridian