Artificial Veri Data for Software Development

In this post, we are going deeper into the Dynamic Data project, looking at the Veri dynamic data (library available here on GitHub and at NPM). Opening the library, you’ll see there is one type of data object: Meal Activity.

Learn more about dynamic data generators and the benefits of artificial data in software development.

About the data source

Veri (iOS|Android) is a health tracking company that specializes in tracking how your body responds to different foods. This is achieved by their Continuous Glucose Monitors (CGM) that is applied to the back of the upper arm to measure blood sugar levels. Keeping your blood sugar level within the target range is good to prevent and delay serious health problems. The user can also upload their meal details, sleep and training data. It should be noted however that the CGM made by Veri should be used for medical purposes such as diabetes diagnosis, management or eating disorder diagnosis, etc.

Approach used

Veri provides a data-exporting service that allows the user to export a selection of their glucose values, meals, notes, training, sleep and flow scores. This results in a large CSV file of records, one of which were converted into a JSON object stored in a NPM package. The index file of the package imports this data object and exports it in a collection called “Data”. These mockup files make up the veri-data package

Models Generated With JSON Crack

The veri-mockups package imports the files above and goes through each attribute generating artificial (new) data using proprietary functions, such as those found in the utils package.

For example, with the mealActivity object:

Static MealActivity Object

Artificial MealActivity Object

Use case ideas

• Health/Fitness Apps
• Data Visualization apps
• etc
  

Ideas to combine with some other data sources

• Use with Genetic Data sources (23andMe and Ancestry) to identify DNA strains related to Blood Glucose and if the results could be effected
• Pair with other Fitness sources (Fibit, Strava, etc) to identify glucose levels during activities
  
• Pair with Dietary apps to recommend specific meals and diets to improve glucose levels
  
• Or with some entirely different segment data, like air quality, weather, location or similar to find interesting new applications for the data.
  

Open-source data library

We welcome contributions and forks to this data set, and look forward to seeing what developers build in our Liberty. Equality. Data. Slack channel.

Considerations for next version/improvements

• Wide Selection of Meals and sensible combinations (e.g. Banana and Apple)
• Appropriate Meal Scores for the selected meals (more nutritious meal will have higher scores)
• Meal Tag Faker
• Appropriate Duration for an exercise (more intensive workouts will probably have a lower duration)
  
• Appropriate changes in Glucose levels (reduces the spiking effect that may generate in a visualization of the data)
  

With Prifina developers can use the Dynamic Data Libraries natively in the App Studio to build direct to consumer apps where individuals can run them with their own user-held data.

Join our Slack community; Liberty. Equality. Data. to brainstorm and collaborate with other app developers, designers, and our team.