Skip to content

Using the SWELL dataset from Kaggle, we've built 2 machine learning models to predict whether or not a person is under stress using Heart Rate Variability(HRV) which can be collected from modern wearables such as fitbit devices and apple watches.

realmichaelye/Stress-Prediction-Using-HRV

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 

Repository files navigation

Stress-Prediction-Using-HRV

Using the SWELL dataset from Kaggle, we've built 2 machine learning models to predict whether or not a person is under stress using Heart Rate Variability(HRV) which can be collected from modern wearables such as fitbit devices and apple watches.

Data

https://www.kaggle.com/qiriro/swell-heart-rate-variability-hrv

Models

  • Standard Feed-forward Neural Network: Input Layer(34 neurons, ReLU activation), Hidden Layer(10 neurons, ReLU activation), Output Layer(3 neurons, softmax actionation)
  • KNeighbors Classifer

Applications

The goal is to provide a realtime biofeedback from the wearable when a person undergoes stress. This can be in the form of a notification on the iPhone to prompt the user to use a meditation app, or play a calm song through google home automatically. The data can also be recorded and be displayed using an app.

About

Using the SWELL dataset from Kaggle, we've built 2 machine learning models to predict whether or not a person is under stress using Heart Rate Variability(HRV) which can be collected from modern wearables such as fitbit devices and apple watches.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published