Artificial Peloton Data for Software Development

In this post, we are going deeper into the Dynamic Data project, looking at the Peloton dynamic data (library available here on GitHub and at NPM). Opening the library, you’ll see there are fourteen types of data objects: AchievementTemplate, AuthDetails, FacebookUserProfile, Instructor, Relationship, Reservation, RideDetails, RideFilter, RideSorts, UserData, UserOverview, UserProfile, UserSettings and WorkoutDetails

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

About the data source

Peloton is a fitness company that makes exercise equipment, has an exercise app (IOS|Android) and creates workout videos. The types of equipment sold by Peloton include: Bikes, Treadmills, Rowing Machines, and a AI-powered Guide. Within the app, there are several classes that have workout videos that can be viewed:

• Strength
• Yoga
• Cardio
• Meditation
• Running
• Outdoor
• Cycling
• Stretching
• Tread Bootcamp
• Bike Bootcamp
• Walking

During these classes, the user can view their heart rate (if synced with a compatible device), the time left and the current exercise in the workout (e.g., 5 min stretching). After the class, and activity summary page is displayed with the calories burned, achievements earned and heart rate performance.

Example Peloton Product: Peloton Bike+

Approach used

Peloton has an unoffical API that allows for the retrieval of workout and personal info. Using the API reference, several JSON objects were created and stored from the data types recorded. The index file of the package imports these data objects and exports them as a collection called “Data”. These mockup files make up the peloton-data package

AchievementTemplate

AuthDetails

FacebookUserProfile

Instructor

Relationship

Reservation

RideDetails

RideFilter

RideSorts

UserData

UserOverview

UserProfile

UserSettings

WorkoutDetails

Models Generated With JSON Crack

The peloton-mockup package imports the file 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 Reservation object:

Static Reservation Object

Artifical Reservation Object

Use case ideas

• Health/Fitness Apps
  
• Data Visualization apps
• etc

Ideas to combine with some other data sources

• Combing with other Health/Fitness apps to provide greater context towards some activities e.g. Fitbit, Garmin, Strava, Whoop.
  
• 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

• General Changes to the Data Fakers as currently they mostly return the static 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.