论文标题
在机器操作任务中为自适应指导建模用户行为
Modeling User Behaviors in Machine Operation Tasks for Adaptive Guidance
论文作者
论文摘要
支持设备操作员的自适应引导系统需要一个全面的模型，该模型涉及各种考虑不同技能和知识水平以及快速改变的任务情况的用户行为。在本文中，我们介绍了一种用于建模操作任务的新方法，旨在整合具有各种经验水平和个人特征的用户提供的视觉操作记录。为此，我们研究了可以在机器操作条件下可以视觉观察到的用户行为模式之间的关系。我们考虑了12个操作员使用头部安装的RGB-D摄像头和一个静态视线跟踪器执行的两个缝纫任务的144个样本。观察到行为特征，例如操作员的凝视和头部运动，手相互作用和热点，并由不断的用户技能改进而产生的重大行为趋势。我们使用了两步方法来模拟用户行为的多样性：基于技能排名的原型选择和体验集成。实验结果表明，几个功能可以作为用户技能评估的适当指数，并为揭示个人行为特征提供了宝贵的线索。用户记录具有不同技能和运营习惯的集成，可以开发一种丰富的包容性任务模型，可以灵活地使用该模型，以适应各种特定于用户的需求。
An adaptive guidance system that supports equipment operators requires a comprehensive model, which involves a variety of user behaviors that considers different skill and knowledge levels, as well as rapid-changing task situations. In the present paper, we introduced a novel method for modeling operational tasks, aiming to integrate visual operation records provided by users with diverse experience levels and personal characteristics. For this purpose, we investigated the relationships between user behavior patterns that could be visually observed and their skill levels under machine operation conditions. We considered 144 samples of two sewing tasks performed by 12 operators using a head-mounted RGB-D camera and a static gaze tracker. Behavioral features, such as the operator's gaze and head movements, hand interactions, and hotspots, were observed with significant behavioral trends resulting from continuous user skill improvement. We used a two-step method to model the diversity of user behavior: prototype selection and experience integration based on skill ranking. The experimental results showed that several features could serve as appropriate indices for user skill evaluation, as well as providing valuable clues for revealing personal behavioral characteristics. The integration of user records with different skills and operational habits allowed developing a rich, inclusive task model that could be used flexibly to adapt to diverse user-specific needs.
