Nowadays, a great number of articles in the health field use IoT devices in the proposed models, techniques, and approaches. The manufacturers of these devices present different ways to provide the vital signs captured by the equipment. Thus, third-party applications and solutions that require the use of vital signs need to adapt to the different approaches proposed by each manufacturer, which culminates in an increase in the complexity of the development of these applications. In addition, it is noted that the current state-of-the-art does not present, for the theme, an integration solution that analyzes vital signs aiming to describe the user’s health status, establishing a pre-diagnosis of the health status. Given this scenario, this dissertation has the objective of promoting an integration model between IoT devices and health systems, facilitating contact between the parties and ensuring interoperability. This article promotes HealthTranslator, a model that collects the data captured by different IoT devices and provides the collected data in a file with a unique format and type, promoting the use of the data enrichment technique through the analysis of vital signs, aiming precisely to describe the user’s health status. The model consists of promoting 4 microservices, with the objectives of communicating with the user, consulting vital signs collected by APIs from device companies that collect vital signs, defining the periodicity of reading vital signs based on the values of these signs, and generating the final files with a description of the user’s state of health. The output object uses the FHIR standardization, used globally for healthcare solutions. HealthTranslator also features an intelligent collection of users’ vital signs, as it determines a periodicity for the collection based on the individual characteristics of each user’s vital signs. This model has been tested to assess its accuracy based on functional tests and unit tests. These tests presented a good work of the model, generating the description of health and performing the other steps correctly. With the proposed model, numerous healthcare applications around the world will benefit, as they will have a model that facilitates the capture of vital signs, reducing the complexity of implementing the applications themselves and related solutions.