 |
We developed a real-time emotion recognition system based on physiological signals utilizing fog computing architecture. The system is suited for environments where the emotional data of users can be processed in places such as educational institutions, workplaces, smart homes, and healthcare facilities. Random forest machine learning model was used on the DREAMER dataset. 84.21% accuracy was achieved with 104 features but considering resource utilization and time performance, the system has been configured to calculate 24 features in 43 ms with 79.70% accuracy.
|
IEEE/ICACT20250190 Slide.38
[Big Slide]