This project builds upon SeWol, where I previously explored how emotions accumulate within indoor spaces. In this continuation, the focus shifts toward the algorithmic dimensions of affective computing. By leveraging biofeedback data, such as facial expressions and ECG readings, this project aims to develop real-time emotion adaptation within large language models (LLMs). Through this project, I sought to explore a core question: