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Hotspots Integrating of Expert and Beginner Experiences of Machine Operations through Egocentric Vision

机译:通过自我中心视野整合专家和初学者的机器操作经验的热点

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摘要

Beginners can often provide useful information regarding machine operations, e.g., how difficult a specific operation is or how beginners deal with difficulties. However, their experiences oftentimes widely vary, and it is difficult to summarize these diverse experiences in an extensive way. To try and solve this problem, this study focused on developing a framework for integrating beginners' and experts' experiences into a unified operation model. A baseline model was first obtained based on hand-machine interactions automatically extracted from experts' egocentric vision records. Then, the beginners' behaviors were integrated by dynamically aligning them to the baseline model. Through this process, an integrated model based on the experiences of a wide range of users was achieved. We applied our method to the operation experiences of two tabletop devices, an IH heater and a sewing machine. The results show good potentials in modeling the common and different behaviors among experts and beginners.
机译:初学者通常可以提供有关机器操作的有用信息,例如,特定操作的难度或初学者如何处理困难。但是,他们的经验常常差异很大,因此很难以广泛的方式总结这些多样的经验。为了解决这个问题,本研究着重于开发一个框架,将初学者和专家的经验整合到统一的运营模型中。首先根据从专家的以自我为中心的视力记录中自动提取的人机交互来获得基线模型。然后,通过使初学者的行为与基准模型动态匹配来整合他们的行为。通过此过程,获得了基于广泛用户经验的集成模型。我们将我们的方法应用于两个台式设备(IH加热器和缝纫机)的操作经验。结果显示出在建模专家和初学者之间常见和不同行为方面的良好潜力。

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