Analysis of existing techniques in human emotion and behavioral analysis using deep learning and machine learning models

Artificial intelligence has become a pivotal force in the 21st-century information technology landscape, driving significant advancements in various fields. As AI continues to evolve, its capacity to understand and analyze human emotions and behaviors through machine learning has reached a new height. In practical applications, it is essential to intelligently capture the emotional information. Current research methods for studying human emotions and behaviors can be broadly classified into artificial and machine learning. Predominantly, emotion research leverages machine learning to enable AI to recognize facial expressions and perform emotion detection and feedback tasks. In the realm of human behavior research, vital structural information is extracted from limbs and skeletons for detailed analysis. Significant breakthroughs have been achieved by integrating AI technology with effective algorithms. However, existing detection mechanisms still suffer from varying degrees of error, primarily owing to imperfections in machine learning and algorithmic approaches to human emotions and behaviors, resulting in computational inaccuracies. This review explores the contributions and limitations of both machine and artificial learning in the current research landscape. This review proposes leveraging the working principles of wearable technology to develop a comprehensive detection framework for emotions and behaviors. This integrated approach aims to enhance the accuracy and reliability of AI in detecting human emotions and behaviors, thereby laying a solid foundation for future advancements in this field. By addressing the current challenges and refining methodologies, we can significantly improve AI’s effectiveness of AI in understanding and interacting with human emotional and behavioral patterns.