A Machine Learning-Driven Online Mobile Application for Personalized Student Stress Management
Project Description
In response to a rise in student suicides in Hong Kong, we propose a mobile app designed to help students manage stress and enhance emotional resilience.
Highlighted Feature
- Initial Stress Review
- Personalised Tips of the Day and other Miscellaneous features
- Stress Log and Tracking
- Thought Sharing Platform
- Stress Warning System
Main Features
Initial Stress Review
An initial quiz to determine users’ stress level.
Personalised Tips of the Day
Offer daily personalized tips and relaxation techniques based on user preferences and feedback.
Stress Log and Tracking
Users are prompted to log their stress levels daily upon login, providing a continuous record of their emotional state. This visualizing stress trends over time, helping users identify patterns and triggers.
Thought Sharing Platform
An online peer forum, enabling users to share their stress management strategies, initiate discussions about their challenges and receive peer inputs.
Stress Warning System
A machine learning-based early warning system will be used to identify students experiencing severe stress or suicidal ideation.
Project Schedule
| Period | Work Description | Progress |
| Sep~early Oct | -Research -Architectural specifications | Ongoing |
| Oct | -Augment and filter test data (if necessary) -Build and train initial models -Construct initial mobile application (Front end) | Not Started |
| Nov~early Dec | -Construct mobile application (Back end) -tune the hyperparameters of the models | Not Started |
| Dec | -Prepare interim report and presentation | Not Started |
| Jan~Mar | -Debug and improve mobile app based on feedback -Fine tune the models -User testing | Not Started |
| Apr | -Prepare for final report and presentation -Source code clean up -App deployment | Not Started |