Affective state estimation for human-robot interaction
Affective state estimation for human-robot interaction
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DOI：
10.1109/tro.2007.904899
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发表时间：
2007-10-01
影响因子：
7.8
通讯作者：
Croft, Elizabeth A.
中科院分区：
文献类型：
--
作者：
Kulic, Dana;Croft, Elizabeth A.
为了使人类和机器人以有效和直观的方式进行交互，机器人必须获得有关人类情感状态的信息，以响应机器人的动作。这种交互式通信的第二模式被假设为允许更自然的协作，类似于两个合作的人类之间的“肢体语言”交互。本文描述了一个隐马尔可夫模型（HMM）的实现和验证估计人类情感状态的真实的时间，使用机器人运动作为刺激。该系统的输入是生理信号，如心率、出汗率和面部肌肉收缩。情感状态估计使用二维价唤醒表示。机器人操纵器用于产生人机交互期间预期的运动，并要求人类受试者报告他们对这些运动的反应。还测量了人体生理反应。机器人运动产生的名义上的势场规划和最近报道的安全运动规划，最大限度地减少潜在的碰撞力沿着path.The机器人运动进行了测试，与36个科目。这些数据用于训练和验证HMM模型。的HMM情感估计的结果也进行了比较，以前实现的模糊推理引擎。
In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two-dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine.