连贯的机器人行为和体现在人机交互期间的情感感知和识别中的作用:实验研究
Laura Fiorini1,2, Grazia D'Onofrio3, Alessandra Sorrentino1
1Department of Industrial Engineering, University of Florence, Firenze, Italy.
JMIR human factors
|January 26, 2024
概括
具有连贯的语音手势的社交机器人增强了人类的情感感知和识别. K-最近邻近算法在识别用户情绪方面表现出高准确性,特别是在体内机器人中.
科学领域:
- 人与机器人的交互
- 情感计算是一种情感计算.
- 社会机器人社会机器人社会机器人
背景情况:
- 社会机器人越来越多地融入日常生活,需要类似人类的交互模型.
- 用户期望社交机器人利用共同言语手势,反映人与人之间的沟通.
- 在理解机器人体现和行为对情感感知的影响方面仍然存在挑战.
研究的目的:
- 评估机器人语音手势和体现对感知到的情感价值,兴奋和主导性的影响.
- 评估机器学习算法 (SVM,随机森林,KNN) 在识别人类情绪方面的准确性.
- 为了比较Pepper机器人 (静态和连贯的行为) 和PC接口之间的情绪诱导和识别.
主要方法:
- 利用Pepper机器人,在静态和连贯的 (COH) 共话行为条件下,使用标准化图像引起情绪.
- 包括第三个条件,PC接口显示相同图像以隔离机器人实施例的影响.
- 招募了60名参与者,每个条件有20人,共3600次互动.
主要成果:
- 在连贯机器人和PC条件 (P<.05) 之间观察到价值,兴奋和主导的显著差异.
- 与PC和静态机器人条件相比,连贯机器人的条件导致了更高的价值得分.
- 与连贯机器人一起,K-最近邻居实现了最高的情感识别精度 (0.97),超过了静态 (0.88) 和PC (0.94) 条件.
结论:
- 多式联络,包括共话和视觉通道 (COH模式),增强了情感识别准确性和感知到的情感强化.
- 机器人体现和非语言沟通对于有效的人机器人情感互动至关重要.
- 未来的研究应该探索影响人机交互情感感知和识别的人口和认知因素.
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