Artificial Polar Data for Software Development

In this post, we are going deeper into the Dynamic Data project, looking at the Polar dynamic data (library available here on GitHub and at NPM). Opening the library, you’ll see there are twenty one types of data objects: getActivitySummary, getAvailableSamples, getAvailableSleepTimes, getExercise, getExerciseSummary, getHeartRateZones, getNightlyRecharge, getPhysicalInfo, getSamples, getSleep, getStepSample, getUserInfo, getWebhook, getZoneSample, listActivities, listExercises, listNightlyRecharge, listNotifications, listPhysicalInfo, listSleep, and listTrainingExercises.

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

About the data source

Polar (iOS|Android) is a fitness-tracking company offering various models of several fitness watches and sensors. The data traccked from these devices include:

• Active Time
• Calories Burnt
• Steps and Distance
• Heart Rate
• Sleep
  

Polar offers a free app, Polar Flow, on the app stores that analyze the data retrieved from their products. The main features offered by the app are Training and Activity tracking. Training tracking allows the user to create a workout reigment with set goals and targets and analyze their performnce of their training session. Activity tracking shows an overview of the data (calories burnt, heart rate, steps, etc) captured over any 24-hour period.

Approach used

Polar has an API that grants access to the user's dataset. These requests generate JSON objects that are then stored in an 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 polar-data package.

getActivitySummary

getAvailableSamples

getAvailableSleepTimes

getExercise

getExerciseSummary

getHeartRateZones

getNightlyRecharge

getPhysicalInfo

getSamples

getSleep

getStepSample

getUserInfo

getWebhook

getZoneSample

listActivities

listExercises

listNightlyRecharge

listNotifications

listPhysicalInfo

listSleep

listTrainingExercises

Models Generated With JSON Crack

The polar-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 getSamples object:

Static getSamples Object

Artifical getSamples Object

Use case ideas

• Health/Fitness Apps
• Data Visualization apps
• etc
  

Ideas to combine with some other data sources

• Combining with other Health devices to provide greater context towards some data e.g. Fitbit, Garmin, Strava, Oura.
  
• Motivational quotes to your screen depending on if the user meets their goals
• 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

• Change the format into the standard format for the dynamic data fakers
  
• Improve the values generated to be sensible, e.g. Heart Rates are more consistant and realistic
  

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.