Emotion-Aware Smart-Home Recommendation and Control System

Abstract

Problem: With the involvement of data science and Internet of Things (IoT) technologies, smart home systems have undergone significant improvements. While existing smart home systems demonstrate satisfactory performance, they lack substantial integration of deep learning technologies. The combination of IoT and deep learning presents opportunities for developing more robust solutions with an enhanced user experience. This project focuses on applying deep learning concepts to the smart home systems domain to improve performance and explore the potential of utilizing computer vision to recognize user emotions and control smart home appliances. Methodology: Deep learning was utilized to develop and train a model to detect user facial expressions and emotions accurately and generate recommendations based on emotions. The system is capable of predicting emotions in real time and suggesting recommendations. Subsequently, based on user acceptance, it can control the appliances connected to the home network.