摘 要
随着科技的不断发展,人机交互技术也越来越受到关注。在传统的人机交互系统中,用户通常需要通过键盘、鼠标或触摸屏等输入设备与计算机进行交互,用户不一定能方便使用这些输入设备,比如在进行运动、开车等情况下。为了应对上述问题, 特设计一个基于 MediaPipe 的人机交互系统,利用先进的姿态估计技术和机器学习算法,开发出一种更加高效、准确和自然的人机交互方式,以满足用户在不同场景和应用中的交互需求。通过这种方式,可以在移动设备和 PC 端实现更加直观、人性化和方便的人机交互方式,在日常生活中也提供一个实时 AI 健身监测功能,提升用户体验和工作效率。本文首先分析了研究背景、研究方法等内容,并介绍了相关技术,接着详细阐述了系统的设计和实现,最后进行了系统测试并分析了存在的不足之处,同时提出了今后改进方向。
关键词:人机交互;手势识别;姿态估计;MediaPipe
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
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