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在工业场景中,人机协作流程,多模式分析和感知挑战检测在工业场景中

Pooja Prajod¹, Matteo Lavit Nicora^2,3, Marta Mondellini^2,4

¹Human-Centered Artificial Intelligence, University of Augsburg, Augsburg, Germany.

Frontiers in robotics and AI

|June 14, 2024

概括

这项研究探讨了工业任务中的流动状态,发现在低挑战 (无聊) 和高挑战条件之间,兴奋和心率存在显著差异. 开发了一个基于HRV的模型来检测适应性工作环境的挑战水平.

科学领域:

人与计算机的交互
职业健康心理学职业健康心理学
情感计算是一种情感计算.

背景情况:

流动状态,一种最佳体验,在工业环境中没有得到充分的研究,特别是在非心理任务中.
现有的关于流动的研究主要集中在精神要求较高的活动上,在理解工业任务参与方面留下了一个差距.
关于在特定行业任务中对不同挑战水平的情绪和生理反应的知识有限.

研究的目的:

研究在工业装配任务中感知挑战水平和情感/生理反应之间的关系.
在模拟的工业人机器人合作期间探索面部情绪估计 (价值,兴奋) 和心率变量 (HRV).
开发一种检测感知挑战水平的方法,以促进工业工作的流动.

主要方法:

模拟了一个工业人机协作组装任务,具有三个挑战级别.
收集了37名参与者的视频,心电图 (ECG) 和NASA-TLX数据.
分析了与感知挑战相关的面部情绪估计和HRV特征.

主要成果:

在低挑战 (无聊) 和其他条件之间发现了平均兴奋和心率的显著差异.
心率和HRV特征的趋势级差异 (平均NN,三角指数) 在Flow和焦虑条件之间出现.
开发了一个基于HRV的机器学习模型,以区分低和高的感知挑战水平.

结论:

在工业环境中,情绪和生理反应因感知到的挑战而有所不同.
人力资源价值分析提供了一种可行的方法,用于检测工业任务中感知到的挑战水平.
研究结果支持设计适应性工作环境,以优化流动和工人福祉.

关键词:

合作合作合作合作情绪估计情绪估计流动流动流动流动心率变化的心率变化.人与机器人的交互工业 5.0 工业 5.0 工业机器学习是机器学习.任务负载任务负载

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