Emotion Analysis and Expression Algorithm of Dance Action Based on Machine Learning

Emotion analysis and expression algorithms represent a pivotal frontier in the intersection of artificial intelligence and humancomputer interaction. These algorithms aim to decode and understand human emotions from various modalities such as text, speech, facial expressions, and physiological signals. This paper introduces the Context -Based Rough Sugeno Fuzzy (CBRSF) model tailored for emotion analysis and expression algorithms in the context of dance actions. With machine learning techniques, the CBRSF model integrates contextual information, rough set theory, and Sugeno fuzzy logic to accurately analyze and express emotions conveyed through dance movements. the power of machine learning techniques, the CBRSF model integrates various components, including contextual information, rough set theory, and Sugeno fuzzy logic, to provide a comprehensive framework for emotion analysis and expression. One of the key strengths of the CBRSF model lies in its ability to incorporate contextual information surrounding dance movements. Emotions conveyed through dance are often influenced by factors such as choreographic context, music, and cultural background. By integrating contextual cues into the analysis process, the CBRSF model can better capture the nuanced emotional nuances embedded within dance performances. The CBRSF model utilizes rough set theory to handle uncertainty and imprecision inherent in emotion analysis. Dance movements can be inherently ambiguous, making it challenging to accurately categorize the associated emotions. Rough set theory provides a principled framework for managing this uncertainty, allowing the CBRSF model to make informed decisions even in situations where data may be incomplete or inconsistent. Through comprehensive experimentation and evaluation, our proposed model achieves an emotion recognition accuracy of 98% across diverse dance action datasets, surpassing existing methods by 10.2%. Moreover, the CBRSF model enables nuanced emotion expression by dynamically adjusting dance movements based on real-time emotional cues.