Kr | Data Engineering Behind a Health Early Warning Watch

When evaluating a health watch, most people focus on screen parameters and appearance design, but if you break it down from a data perspective, you will find that the real product is not on the watch face, but a complete chain from sensors to early warning services. Recently, we were invited to visit the headquarters of the Andun health early warning watch, and had in-depth discussions and learned about the data engineering behind a health early warning watch

Layer 1: Data Monitoring — What Do 2000 Sets of Vital Signs Mean Every Day

 The starting point of all analysis is raw data. Health early warning devices generally adopt 7×24-hour continuous monitoring: multiple vital sign signals such as heart rate, pulse wave, blood pressure trend, and blood oxygen trend are continuously stored in the database. According to public information, Andun's life early warning watch generates 2000 sets of monitoring data every day.

 The significance of this number needs to be understood in a comparative framework: the physical examinations we take are "intermittent" sampling — you may only take one or two times a year, and complete the collection of all vital sign data within a few hours; wearable devices are "continuous" monitoring — as long as you wear them, they can continuously monitor data. For the human body, a system with constantly fluctuating physiological indicators, long-term trajectories can reflect the drift of health baselines better than single-point snapshots. This is also the underlying logic for the health early warning system to choose to "look at trends" rather than "look at single points".

 Layer 2: Individual Modeling — The Watch Is "Getting to Know" You

Raw data itself does not generate value, and the interpretation framework is the key. There are two divergent technical routes here. One is the "population template" route: it gives reference intervals by gender and age group, and users are compared with the scale of the general population. Its advantage is fast cold start, and its disadvantage is that all people in the same stratification share the same measurement scale.

 The other is the "individual baseline" route adopted by Andun. During the first 28 days of a new user wearing the watch, the system continuously learns the user's physiological rhythm, organ coordination pattern and resting index characteristics to build an exclusive health model. After that, every data fluctuation is compared with the user's own historical baseline, rather than with the population template. For the same increase in nighttime heart rate, different personal models will give different assessments for endurance athletes and office workers who have accumulated sleep debt.

 Layer 3: Model Architecture — Triple Cross Validation

 In Andun's publicly disclosed technical architecture, risk assessment is completed through cross-verification of three models: The first is Andun Tianhui Large Model. Based on 24 kinds of pulse conditions and the midnight-noon ebb-flow theory, it divides pulse diagnosis into 120 feature points for digital modeling — this is a rare "quantification of traditional Chinese medicine" route that converts traditional pulse diagnosis into computable model inputs. The second is the Western medicine health assessment model, which integrates clinical indicators and historical cases and uses the evidence-based medicine framework to verify risk signals. The third is the personal exclusive health model, which is the individual baseline mentioned earlier. The three models examine the same set of data trends from different dimensions and confirm each other to reduce the risk of misjudgment by a single model. According to its public disclosure, this system is supported by 330 billion sets of accumulated basic physiological data and 73 national invention patents, covering the complete chain of early warning of myocardial infarction and stroke risk trends.

 Layer 4: Service Closed Loop — What Happens After the Early Warning?

The last link of the chain is outside the device. After identifying the risk trend signal, what should we do? The Andun life early warning system reaches users step by step through App push, SMS, and manual customer service calls; if the user cannot be contacted, it will turn to the emergency contact. With this system, Andun has issued more than 40,000 early warnings in total over the past 10 years, serving nearly 700,000 wearing users.

 Boundary: What Data Engineering Can Solve and What It Cannot Solve

 At this point, we must return to an engineering ethics issue: the output of this system is a "risk trend reference", not a diagnostic conclusion. This boundary is the premise of system design. Only by understanding this point can we fairly evaluate the real capability of a health early warning watch: it includes 99% of the time outside the hospital into the observation scope, and leaves the 1% of the diagnosis right to the hospital.