Gold Sentinel aims to enhance in-home activity recognition by developing an AI model that effectively fuses data from multiple radar sensors. The challenge lies in integrating data from different radar sources to create a cohesive understanding of various in-home activities. This project will allow learners to apply their knowledge of AI and sensor technology to solve real-world problems. The goal is to create a model that accurately interprets and predicts household activities, improving the efficiency and reliability of smart home systems. By working on this project, learners will gain hands-on experience in AI model development, data fusion techniques, and sensor data analysis, all within a controlled and manageable scope.

The project deliverables include a functional AI model capable of fusing data from multiple radar sensors to recognize in-home activities accurately. Additionally, a comprehensive report detailing the model's development process, data fusion techniques, and performance evaluation will be required. The team will also prepare a presentation to demonstrate the model's capabilities and potential applications in smart home systems. These deliverables will showcase the learners' ability to apply theoretical knowledge to practical challenges.