Anxolotl, an Anxiety Companion App -- Stress Detection
This is an incremental solution for monitoring stress in real-life applications, specifically for integration into the Anxolotl anxiety companion app.
The paper tackled stress detection by developing a supervised learning model for classification using the SMILE dataset, achieving 64.1% accuracy and an F1-score of 54.96%.
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.