Is the ai tester accurate?

The accuracy of an ai tester depends on its application scenario and device type. ‌
Accuracy assessment
Medical grade equipment: such as hospital electrocardiographs, blood pressure monitors, etc. These devices require strict certification, have a small range of data errors, are suitable for disease diagnosis, and have high accuracy.
Consumer grade products, such as heart rate bracelets, body fat scales, etc., focus on convenience, and the data is mostly trend reference, which may result in bias in a single measurement.
The impact of technical principles and usage scenarios
Optical sensors: used for heart rate and blood oxygen monitoring, susceptible to interference from ambient light and skin conditions.
Bioelectrical impedance: used for body fat and moisture content detection, the results may vary greatly among different brands of equipment.
Inertial sensors: used for step count and sleep monitoring, and may misjudge complex behaviors.
Data interpretation and individual differences
Algorithm optimization: Device manufacturers train models through big data, but the population coverage is limited. For example, individuals with darker skin or tattoos may experience inaccurate heart rate monitoring due to differences in light absorption.
Individual baseline values: Health indicators vary from person to person, and continuous monitoring is more meaningful than single data.
Usage standards and scenario limitations
Operation requirements: If the position of the blood pressure monitor cuff is incorrect, it may cause errors. The body fat scale should be stood barefoot with dry feet.
Environmental interference: Strong electromagnetic fields may affect sensor signals, and extreme temperatures can lead to a decrease in component performance.