在不同心理工作负载条件下的意外机器人行为期间预测认知冲突在物理人机器人协作中的不同心理工作负载条件
Alka Rachel John1, Avinash K Singh1, Klaus Gramann2
1Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
Journal of neural engineering
|January 31, 2024
概括
较高的心理工作量减少了人机协作中的错误意识. 这会影响用于安全的脑信号,但预测模型可以警告潜在的问题.
科学领域:
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
背景情况:
- 脑机接口 (BCI) 技术对于安全的人机协作 (pHRC) 至关重要.
- 来自电脑电图 (EEG) 的与错误相关的潜力 (ERP) 信号,用于BCI适应的人类错误感知.
- 心理工作负担会损害错误意识的认知资源,在pHRC中构成安全风险.
研究的目的:
- 调查认知工作负载在物理人机协作 (pHRC) 期间对人类错误意识的影响.
- 评估心理工作负载的变化如何影响EEG信号中的与错误相关的潜力 (ERP).
- 开发一个预测模型,用于BCI控制的pHRC中减少错误意识.
主要方法:
- 使用工业机器人设计了一项爆炸任务,通过二次算术任务操纵心理工作负载.
- 从24名参与者那里收集了EEG,感知工作量和绩效数据.
- 在33%的试验中,机器人错误是由意想不到的停止引起的.
主要成果:
- 增加心理工作量显著降低了预测错误负面性 (PEN) 和错误正面性 (Pe) 的幅度,表明错误意识下降.
- 与正确停止相比,意想不到的机器人停止引起了额头和中央α/β功率的增加.
- 一个卷积神经网络 (EEGNet) 从错误前的EEG数据中成功预测了PEN和Pe幅度.
结论:
- 认知工作负载直接减少pHRC中的错误意识,影响BCI安全机制.
- 基于EEG的预测模型可以识别用户错误意识的减少,从而实现主动安全措施.
- 这项研究支持将自适应BCI系统集成到pHRC中,以提高安全性和个性化用户体验.
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