Research Progress on the Application of AI Posture Recognition Technology in Traditional Chinese Medicine Exercise Intervention

Abstract

Traditional Chinese medicine (TCM) traditional exercises serve as an important carrier of the TCM “preventive treatment of disease” system and integrated physical and medical health services. They possess unique advantages in adolescent physical fitness improvement, sub-health regulation, chronic disease prevention and control, as well as rehabilitation medicine. For a long time, traditional Chinese medicine exercises have been plagued by problems such as difficulty in quantifying movement specifications, teaching relying on oral instruction and personal demonstration, lack of real-time feedback in the intervention process, and insufficient personalization, which restrict their modernization, large-scale and standardized popularization, and application. Represented by OpenPose, AI posture recognition technology features non-contact performance, real-time capability, high precision, and low cost, providing key technical support for the digital transformation of traditional Chinese medicine exercises. This paper systematically sorts out the research context and cutting-edge achievements of AI posture recognition in the field of traditional Chinese medicine exercise intervention, summarizes the technical integration paths and application modes, analyzes practical effects, and explores the core bottlenecks, including the transformation of TCM tacit knowledge, algorithm robustness, construction of standard systems, and long-term exercise adherence. It also prospects the future development trend of the integration of AI and traditional Chinese medicine exercises, aiming to provide a theoretical reference and academic basis for subsequent research, technology implementation, and achievement promotion in this field.