Abstract

With the continuous development of technology, human-computer interaction technology is also receiving increasing attention. In traditional human-computer interaction systems, users usually need to interact with computers through input devices such as keyboards, mice, or touch screens. Users may not be able to easily use these input devices, such as when exercising or driving. To address the above issues, a MediaPipe based human-machine interaction system is designed, utilizing advanced pose estimation technology and machine learning algorithms to develop a more efficient, accurate, and natural human-machine interaction method to meet the interaction needs of users in different scenarios and applications. Through this approach, a more intuitive, user-friendly, and convenient human- computer interaction can be achieved on mobile devices and PCs. It also provides a real-time AI fitness monitoring function in daily life, improving user experience and work efficiency. This paper first analyzes the research background, research methods, and introduces relevant technologies, Subsequently, the design and implementation of the system were elaborated in detail. Finally, system testing was conducted and the existing shortcomings were analyzed. At the same time, future improvement directions were proposed.

Key words: Human machine interaction; Gesture recognition; Attitude estimation; MediaPipe