Res. Assist. İsmail Kayadibi and Assoc. Prof. Dr. Osman Uslu, members of our department, have developed an artificial intelligence–based model capable of detecting individuals’ stress levels from physical activity data.
The study introduces a lightweight deep learning architecture called St-CNN, which analyzes physical activity data to determine stress levels with high accuracy and low computational cost. Thanks to these features, the proposed model enables real-time stress monitoring on wearable devices.
The research makes a significant contribution to the use of artificial intelligence and deep learning techniques in healthcare. The developed system offers new opportunities for early warning systems in psychological health monitoring, stress management, and workplace productivity.
In this respect, the study directly contributes to the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being) and SDG 9 (Industry, Innovation, and Infrastructure).