Enhancing Biomechanical Analysis and Rehabilitation with Deep Migration Learning: A Professional Dance Movement Gesture Detection Approach

Abstract

Motion recognition technology, particularly in the context of dance, plays a crucial role in the nuanced detection of human body movements, which are essential for conveying complex ideas and emotions. This field has expanded to include significant applications in biotechnology, such as enhancing biomechanical analysis and developing advanced rehabilitation methods. This study focuses on a professional dance movement gesture detection method employing deep migration learning, designed to accurately capture the intricate and variable movements of dancers. By incorporating features expression and attribute mining within the detection system, this approach addresses the challenges posed by the unique characteristics of each dancer, which are vital high-level semantic information for precise motion detection. The development and implementation of this system allow for the detailed analysis of dance movements, facilitating applications in physiotherapy and movement correction therapies. The dataset used in this study demonstrates that our algorithm can successfully identify and enhance the accuracy of dance movement detection. This improvement in detection capabilities supports not only the fine-tuning of dancers' performances but also the potential for corrective biomechanical interventions. The findings suggest significant possibilities for using this technology to assist in rehabilitation programs and to aid in the recovery and enhancement of motor functions, providing a new avenue for biotechnological applications in human movement and health.