Utilizing emotion recognition technology to enhance user experience in real-time

Abstract

In recent years, advancements in human-computer interaction (HCI) have led to the emergence of emotion recognition technology as a crucial tool for enhancing user engagement and satisfaction. This study investigates the application of emotion recognition technology in real-time environments to monitor and respond to users’ emotional states, creating more personalized and intuitive interactions. The research employs convolutional neural networks (CNN) and long short-term memory networks (LSTM) to analyze facial expressions and voice emotions. The experimental design includes an experimental group that uses an emotion recognition system, which dynamically adjusts learning content based on detected emotional states, and a control group that uses a traditional online learning platform. The results show that real-time emotion monitoring and dynamic content adjustments significantly improve user experiences, with the experimental group demonstrating better engagement, learning outcomes, and overall satisfaction. Quantitative results indicate that the emotion recognition system reduced task completion time by 14.3%, lowered error rates by 50%, and increased user satisfaction by 18.4%. These findings highlight the potential of emotion recognition technology to enhance user experiences. However, challenges such as the complexity of multimodal data integration, real-time processing capabilities, and privacy and data security issues remain. Addressing these challenges is crucial for the successful implementation and widespread adoption of this technology. The paper concludes that emotion recognition technology, by providing personalized and adaptive interactions, holds significant promise for improving user experience and offers valuable insights for future research and practical applications.