Developed a sophisticated system capable of discerning a range of user emotions in real-time, utilising a pre-trained TensorFlow Lite model that processes live video feeds to accurately assess facial expressions and affective states, providing valuable input for dynamic game adjustments.

Engineered a highly responsive and adaptive control system that dynamically modifies the pace of the Pac-Man game, reacting to the nuances of emotional states detected through a bespoke socket communication protocol, resulting in a more personalised and immersive gaming experience.

Constructed a fully modular system architecture for seamless integration of all its component parts (emotion recognition, video frame analysis, and in-game control), while also guaranteeing scalability and facilitating rapid development cycles and future upgrades.