Investigating Implicit Cues for User State Estimation in Human-Robot Interaction Using Physiological Measurements
Investigating Implicit Cues for User State Estimation in Human-Robot Interaction Using Physiological Measurements
复制标题
使用生理测量研究人机交互中用户状态估计的隐式线索
DOI：
10.1109/roman.2007.4415249
复制
发表时间：
2007
期刊：
影响因子：
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通讯作者：
Shrikanth S. Narayanan
中科院分区：
文献类型：
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作者：
E. Provost;David Feil;M. Matarić;Shrikanth S. Narayanan
实现并维持用户参与是人机交互的一个关键目标。本文提出了一种根据生理数据（包括皮肤电反应和皮肤温度）确定用户参与状态的方法。在报告的研究中，参与者在玩由模拟机器人或实体机器人主持的金属拼图游戏时测量了生理数据，这两种机器人都有不同的性格。使用 K 最近邻算法根据试验中的位置对所得生理数据进行分段和分类。我们发现可以在不同长度的试验中估计用户的参与状态，准确度为 84.73%。在未来的实验中，这种能力将允许辅助机器人主持人估计用户在交互过程中的任何给定点结束交互的可能性。然后可以使用这些知识来调整机器人的行为，以尝试重新吸引用户。
Achieving and maintaining user engagement is a key goal of human-robot interaction. This paper presents a method for determining user engagement state from physiological data (including galvanic skin response and skin temperature). In the reported study, physiological data were measured while participants played a wire puzzle game moderated by either a simulated or embodied robot, both with varying personalities. The resulting physiological data were segmented and classified based on position within trial using the K-Nearest Neighbors algorithm. We found it was possible to estimate the user's engagement state for trials of variable length with an accuracy of 84.73%. In future experiments, this ability would allow assistive robot moderators to estimate the user's likelihood of ending an interaction at any given point during the interaction. This knowledge could then be used to adapt the behavior of the robot in an attempt to re-engage the user.
DOI：
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发表时间：
1990
期刊：
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影响因子：
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作者：
J. Cacioppo;L. Tassinary
通讯作者：
J. Cacioppo;L. Tassinary