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Utilizing Depth Based Sensors and Customizable Software Frameworks for Experiential Application

机译:利用基于深度的传感器和可定制的软件框架进行体验式应用

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Using depth sensor cameras such as the Kinect and highly customizable software development frameworks in conjunction with artificial intelligence methodologies offer significant opportunities in a variety of applications, such as undergraduate science, technology, engineering, and math (STEM) education, professional or military training simulation, and individually-tailored cultural and media arts immersion. Designing a participatory educational experience where users are able to actively interface and experience given subject matter in a practical experiential manner can enhance the user's ability to learn and retain presented information. Such natural gesture user interfaces have potential for broad application in disciplines ranging from systems engineering education to process simulation. This paper will discuss progress on the development of testing environments for interactive educational methods in conjunction with artificial intelligent systems that have the ability to adjust the educational user experience based on individual user identification. This will be achieved through depth sensor skeletal tracking, allowing experience adaptation based on the nature and effectiveness of the interactive educational experience.
机译:将深度传感器摄像头(如Kinect)和高度可定制的软件开发框架与人工智能方法结合使用,可为各种应用提供大量机遇,例如本科科学,技术,工程学和数学(STEM)教育,专业或军事训练模拟,以及个性化的文化和媒体艺术沉浸式体验。设计一种参与式教育体验,使用户能够以实际的体验方式主动地交互和体验给定的主题,可以增强用户学习和保留所呈现信息的能力。这样的自然手势用户界面在从系统工程教育到过程仿真的学科中具有广泛的应用潜力。本文将讨论交互式教育方法的测试环境的开发进展,以及结合能够根据个人用户识别来调整教育用户体验的人工智能系统。这将通过深度传感器骨骼跟踪来实现,从而允许根据交互式教育体验的性质和有效性来适应体验。

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