Audience Engagement Monitoring System Using Machine Learning

Effective communication in educational and presentation settings relies heavily on audience engagement. To address this, we propose a novel Real-Time Audience Engagement Monitoring System that leverages computer vision and real-time data analysis techniques. The system employs a multi-step process, beginning with face detection and facial landmark detection to identify audience members and analyze their head movements. Utilizing OpenCV and MediaPipe libraries, the system estimates the pose of audience members' heads, allowing for the calculation of attention scores based on head movements and facial orientation. These attention scores are then streamed in real-time using Socket.IO to a Node.js/Express.js server, which serves as a central hub for data distribution. The server disseminates the attention scores to multiple dashboard applications, where speakers and educators can monitor audience engagement throughout the session. This research presents a comprehensive approach to assessing and enhancing audience engagement in real-time, providing valuable insights for improving communication and learning outcomes.