Anxolotl, an Anxiety Companion App -- Stress Detection

TL;DR

This paper presents the development of Anxolotl, an anxiety companion app that uses a supervised learning model to monitor stress levels with 64.1% accuracy, aiming to support users in managing stress effectively.

Contribution

It introduces a robust stress detection model integrated into a mobile app, utilizing the SMILE dataset and demonstrating consistent performance for real-life application.

Findings

Achieved 64.1% accuracy in stress classification

Model showed low variation across multiple runs

Potential for integration into a real-time stress management app

Abstract

Stress has a great effect on people's lives that can not be understated. While it can be good, since it helps humans to adapt to new and different situations, it can also be harmful when not dealt with properly, leading to chronic stress. The objective of this paper is developing a stress monitoring solution, that can be used in real life, while being able to tackle this challenge in a positive way. The SMILE data set was provided to team Anxolotl, and all it was needed was to develop a robust model. We developed a supervised learning model for classification in Python, presenting the final result of 64.1% in accuracy and a f1-score of 54.96%. The resulting solution stood the robustness test, presenting low variation between runs, which was a major point for it's possible integration in the Anxolotl app in the future.