Anxolotl, an Anxiety Companion App -- Stress Detection
Nuno Gomes, Matilde Pato, Pedro Santos, Andr\'e Louren\c{c}o,, Louren\c{c}o Rodrigues

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.
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Taxonomy
TopicsDigital Mental Health Interventions
