Hack Your Health and Get 300 Health Metrics with AI

For the past decade, wearable health devices have been limited to measuring 20 or so metrics - heart rate, blood pressure, blood oxygen saturation, and steps. Steps, in particular, have become a kind of health ritual: walk more, count them carefully, and all will be fine. Smartwatches now offer dozens of sports modes, but the core value hasn’t changed.

Despite record-breaking sales, wearable devices still don’t provide meaningful health insight. What are users really paying for, and what does a subscription deliver? The real breakthrough comes from turning a wearable into a full laboratory on the wrist - and AI is already making this possible.

By early 2026, our team trained AI to measure more than 300 health parameters from a single wrist pulse signal. The data is sent to an AI-powered cloud, where it is transformed into a full health profile. Users access insights across metabolism and endocrinology, blood and microcirculation, cardiovascular and respiratory function, liver and kidney health, brain and cerebral circulation, as well as lifestyle factors like sleep, activity, stress, emotions, nutrition, and medication adherence. The result is a system-level view rather than isolated numbers.

Continuous measurement during sleep is particularly valuable. Nighttime data is cleaner, more stable, and far more predictive than daytime readings. Our “Digital Sleep” mode enables early risk detection and long-term trend analysis that would otherwise be impossible.

We go beyond tracking. Abnormal readings already signal potential problems, but our AI predicts disease risks before symptoms appear. The Health Assistant evaluates risks, connects physiological data with lifestyle patterns, and generates personalized recommendations. The most important advice remains - consult a physician and pursue further evaluation when necessary.

Our sensor architecture is simple - devices only need to collect a pulse signal. This allows integration into third-party devices and large-scale remote health monitoring. Organizations can track employee or patient health, improve safety, and make decisions based on real physiological data. Enterprise use cases include remote medical screenings, restricting vehicle operation if a driver is unwell, or temporarily removing an employee from duty due to stress - even when no external signs are visible.

The key transformation in health monitoring is the shift from measurement to prediction. AI turns wearables from fitness gadgets into preventive and predictive health tools. Cloud intelligence makes any device a full monitoring platform, while sleep-based continuous data provides the most reliable foundation for long-term risk assessment.

Soon, the first question a doctor asks may no longer be “What are your symptoms?” but “Please share your health data from the past week.” Don’t explain - just share.