人类机器人混:用于建模和检测位于人机交互中的用户混的多式数据集
Na Li1, Jane Courtney2, Robert Ross1
1School of Computer Science, Technological University Dublin, Dublin, Ireland.
Data in brief
|October 1, 2025
概括
这项研究引入了用于人机交互 (HRI) 研究的新数据集,通过同步视频和语音捕捉用户混乱状态. 这些数据有助于在HRI任务中建模社会行为和认知状态.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 了解用户状态对于有效的人机交互 (HRI) 至关重要.
- 现有的数据集往往缺乏捕捉任务导向交互期间细微的用户行为多式联络数据.
- 检测用户的混乱是提高HRI系统适应性的关键.
研究的目的:
- 呈现一种新的多式联运数据集,用于在面向任务的HRI中建模和检测用户困惑状态.
- 提供与机器人互动的参与者的同步视频 (面部和身体) 和语音记录.
- 促进对用户在HRI和HCI中的认知和心理状态的研究.
主要方法:
- 在三个实验任务中收集了28名参与者的数据.
- 录制高清RGB视频 (面部和身体) 与语音同步.
- 分段数据成分片,代表不同的混状态 (一般,生产性,非生产性,非混).
主要成果:
- 数据集包括789个视频片段 (392个身体,397个面部) 与相应的语音.
- 数据捕捉了用户在特定HRI场景中的面部表情和肢体语言.
- 该数据集能够详细分析用户的社交行为和混乱状态.
结论:
- 这一多式联运数据集是推动HRI和HCI研究的宝贵资源.
- 它支持开发更直观,更响应的人机器人系统.
- 未来的工作可以利用这些数据来提高AI对人类认知状态的理解.
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