首页> 外文期刊>International Journal of Social Robotics >A Methodological Outline and Utility Assessment of Sensor-based Biosignal Measurement in Human-Robot Interaction A System for Determining Correlations Between Robot Sensor Data and Subjective Human Data in HRI
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A Methodological Outline and Utility Assessment of Sensor-based Biosignal Measurement in Human-Robot Interaction A System for Determining Correlations Between Robot Sensor Data and Subjective Human Data in HRI

机译:基于传感器的生物信息测量在人机交互中的方法论概述和实用性评估,用于确定HRI中机器人传感器数据与主观人体数据之间的相关性的系统

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摘要

Sensor data taken during a human-robot interaction (HRI) have high potential for usage as new, objective measures of an interaction, either replacing or supplementing survey techniques that are currently most common in HRI research. Sensor data can be taken in large quantities quickly, naturally, and discreetly. They also have the potential to reflect a user's biosignals-information about the user's inner state (such as stress and attention) when interacting with the robot. We previously conducted three studies attempting to use sensor data as a measurement in HRI, with methodological differences in three different experimental environments. In this paper, we reanalyze and add new data to the previous findings under a consistent methodology, consolidate what correlations we find, and can conclude that sensor data is a useful metric in HRI across a wide range of experimental setups and subject pools. We fully describe the methodology we determined to be most effective, from selection of sensors to data analysis techniques to HRI experiment setup, as a basis for how this methodology can be used in other HRI studies. We describe necessary steps in the analysis of a large amount of sensor data (over 100,000 sets) and how sensor data can be compared with survey and behavioral data. Based on these correlations, we find that the most effective sensors are temperature sensors, tactile sensors, and face distance measurements. We also find that higher measurements across all of these sensors are more correlated with both survey and behavioral measurements reflecting positive thinking towards a robot (including non-technophobia, reciprocal behaviors, and positive ratings of the robot) during an interaction. Based on these results, we argue that robot sensor usage is an important and objective metric for HRI research.
机译:在人机相互作用(HRI)期间采取的传感器数据具有高潜力,作为新的,客观措施的互动,替代或补充目前在HRI研究中最常见的调查技术。传感器数据可以快速,自然和谨慎地占据大量。它们还具有在与机器人交互时反映有关用户内状态(例如压力和注意)的用户的生物信息信息。我们以前进行了三项研究试图在HRI中使用传感器数据作为测量,三种不同的实验环境中的方法论差异。在本文中,我们根据一致的方法进行重新分析并将新数据添加到以前的发现,巩固我们发现的相关相关情况,并且可以得出结论,传感器数据在各种实验设置和主题池中的HRI是一个有用的度量。我们完全描述了我们确定最有效的方法,从选择传感器到数据分析技术到HRI实验设置,作为如何在其他HRI研究中使用该方法的基础。我们描述了在分析大量传感器数据(超过100,000套)以及如何将传感器数据与调查和行为数据进行分析的必要步骤。基于这些相关性,我们发现最有效的传感器是温度传感器,触觉传感器和面距离测量。我们还发现,在相互作用期间,对所有这些传感器的所有这些传感器的测量都与反映了对机器人(包括非技术恐惧症,互惠性行为和机器人的积极额定值)的调查和行为测量更加相关。基于这些结果,我们认为机器人传感器使用是HRI研究的重要和客观度量。

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