Application of Human Posture Recognition and Classification in Performing Arts Education

This review explores the integration of human posture recognition and classification technologies in performing arts education, focusing on the advancements in deep learning, neural networks, and computer vision. It traces the evolution of these technologies and highlights their significance in developing personalized teaching methods for performing arts. Following this, we examine various posture recognition technologies, emphasizing data acquisition and processing methods while revealing their strengths and limitations, particularly in their adaptability to performing arts. Moreover, the review discusses posture classification methods, addressing the processing and interpretation of data, and the challenges in achieving classification accuracy and efficiency. Illustrative case studies demonstrate the application of these technologies in enhancing teaching and creativity in performing arts, significantly contributing to the skill development and expressiveness of artists and students. Concluding with a critical assessment of the current feasibility of these technologies in performing arts education, the review suggests potential improvements and future directions. It emphasizes the indispensable role of human posture recognition in advancing performing arts education and underscores the need for continued interdisciplinary research in this evolving field.