Smart Sofa: An IoT-Based Posture Detection and Visualization System

This project presents the design and implementation of an intelligent sofa system for real-time posture detection. The system is engineered using a flexible pressure sensor array and an ESP32 microcontroller, which captures detailed pressure data and transmits it wirelessly to a computer. Leveraging machine learning algorithms, the system classifies this data to accurately determine the number of occupants on the furniture, their specific positions, and their postures. Cross-validation techniques were employed to ensure the model's reliability. A key focus of this project is to enhance affective computing capabilities by providing critical data visualizations for user interaction, thereby improving overall system responsiveness. To support this, dedicated web and iOS applications were designed and developed to visualize the sofa's pressure data in real-time.