Title : AI-based skin pigmentation training simulator system for standardized PMU education
Abstract:
The rapid growth of the semi-permanent makeup (PMU) industry has increased the demand for standardized education and safety-oriented practical training systems. However, existing PMU education still relies heavily on instructor experience and repetitive manual practice, resulting in inconsistent educational quality and procedural competency. This study proposes an AI-based skin pigmentation training simulator system designed to support standardized PMU education through digital simulation and integrated practical training.
The proposed system consists of a training operation server, smart simulation modules, AI-assisted feedback functions, and digital educational management components. The system enables trainees to learn skin pigmentation procedures through step-by-step theoretical and practical training while receiving real-time feedback related to procedural stability, pigmentation patterns, and safetyoriented practice environments. In addition, the proposed platform aims to improve educational reproducibility and training efficiency by integrating digital learning records and simulation-based evaluation systems.
This research focuses on the educational architecture and conceptual framework of a standardized PMU training ecosystem rather than clinical validation. The study is based on patented technologies and a government-supported startup project in South Korea. The proposed platform may contribute to the future development of digital beauty education, AI-assisted procedural training, and standardized competency-based PMU education systems in the global beauty and aesthetic industry.
