Consumer wearables are very good at counting things that are easy to count. Steps, hours asleep, a heart rate at rest. None of those tell you much you did not already know, and the leap from more sensors to more insight is not automatic — it is the part nobody has solved well.
What the sensor array is for
The architecture we are exploring combines dry-electrode ECG, multi-wavelength PPG, and high-precision thermistors. Together those can surface vascular, autonomic, and metabolic signals that a single-sensor device cannot see at all.
The sensors are the easy half. The research problem is fusion: turning three noisy, drifting, individually-unreliable streams into something whose changes mean something.
Trends over readings
The output we care about is not a number at a moment. It is a direction over weeks — metabolic response trends, deviations from your own baseline temperature, autonomic load that accumulates before you notice it.
That framing is also what keeps this honest. A single reading from a wrist sensor invites a diagnostic interpretation it cannot support. A trend against your own established baseline is a genuinely different claim, and a defensible one.
Why this is a research note and not a product page
There is no Lunas wearable to buy, pre-order, or join a list for. This describes the architecture and the algorithmic problem we are working on, published while it is still a question rather than after it becomes an announcement.
What this deliberately does not do
- No hardware has shipped — this is an architecture and a research direction, not a product
- Not a medical device and not diagnostic. It has no regulatory clearance and is not seeking to replace clinical measurement
- Wellness indicators and trends only; nothing here detects, diagnoses, or monitors a disease
- Sensor specifics may change — an exploratory architecture is expected to