A Study on the Effects of Cognitive Overloading and Distractions on Human Movement During Robot-Assisted Dressing.
A Study on the Effects of Cognitive Overloading and Distractions on Human Movement During Robot-Assisted Dressing.
复制标题
DOI：
10.3389/frobt.2022.815871
复制
发表时间：
2022
影响因子：
3.4
通讯作者：
Caleb-Solly, Praminda
中科院分区：
文献类型：
--
作者：
Camilleri, Antonella;Dogramadzi, Sanja;Caleb-Solly, Praminda
对于能够提供物理帮助的机器人来说，保持机器人和人类运动的同步性是交互安全的前提。现有的协作 HRI 研究并未考虑如果人类在密切的身体互动过程中遭受认知超载和干扰，同步性会受到怎样的影响。认知神经科学表明，互动过程中的意外事件不仅会影响动作认知，还会影响人类运动控制。如果机器人要安全地调整其轨迹以适应分散注意力的人类运动，则应评估人类运动的定量变化。这项研究的主要贡献是在涉及机器人辅助穿衣的物理协作任务中对人类运动中断的分析和量化。量化中断的运动是保持人机交互同步性的第一步。从一系列实验中收集的人体运动数据，在这些实验中，参与者在人机交互过程中受到认知负荷和干扰，这些数据被投影到一个二维潜在空间中，该空间有效地表示了数据的高维性和非线性。定量数据分析得到用户体验定性研究的支持，使用 NASA 任务负荷指数来衡量感知的工作量，并使用 PerDITA 问卷来代表这些交互过程中的人类心理状态。此外，我们提出了一种实验方法来收集这种类型的人机协作中的交互数据，该方法提供了场景中人机交互的真实性、实验严谨性和高保真度。
For robots that can provide physical assistance, maintaining synchronicity of the robot and human movement is a precursor for interaction safety. Existing research on collaborative HRI does not consider how synchronicity can be affected if humans are subjected to cognitive overloading and distractions during close physical interaction. Cognitive neuroscience has shown that unexpected events during interactions not only affect action cognition but also human motor control. If the robot is to safely adapt its trajectory to distracted human motion, quantitative changes in the human movement should be evaluated. The main contribution of this study is the analysis and quantification of disrupted human movement during a physical collaborative task that involves robot-assisted dressing. Quantifying disrupted movement is the first step in maintaining the synchronicity of the human-robot interaction. The human movement data collected from a series of experiments where participants are subjected to cognitive loading and distractions during the human-robot interaction, are projected in a 2-D latent space that efficiently represents the high-dimensionality and non-linearity of the data. The quantitative data analysis is supported by a qualitative study of user experience, using the NASA Task Load Index to measure perceived workload, and the PeRDITA questionnaire to represent the human psychological state during these interactions. In addition, we present an experimental methodology to collect interaction data in this type of human-robot collaboration that provides realism, experimental rigour and high fidelity of the human-robot interaction in the scenarios.