Keywords
work-life balance, scheduling, productivity

Technologies
flask, scikit-learn, FitbitAPI, python

This project looks at how wearable data can be used to improve the user's work life balance. Stress is a common problem, especially with long working hours, commuting and household responsibilities. While devices like Fitbits can track health data and calendars help organise tasks, they do not work together.

In this project, data from a Fitbit device is combined with tasks entered by the user and their own stress ratings. A machine learning model is used to predict how stressful a task might be based on things like time of day, duration, heart rate, and task type. These predictions are then used to suggest small changes to a user’s schedule.

The system is built as a web application using Flask, with scikit-learn used for the model. The aim is to help users plan their day in a more balanced way and reduce stress.