Nonverbal Leadership in Joint Full-Body Improvisation

IF 9.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Affective Computing Pub Date : 2024-12-11 DOI:10.1109/TAFFC.2024.3514933
Radoslaw Niewiadomski;Léa Chauvigné;Maurizio Mancini;Gualtiero Volpe;Antonio Camurri
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Abstract

In this work, we investigate nonverbal leadership and address two research questions: 1) is it possible to perceive leadership from nonverbal cues in an unstructured joint full-body activity with no designated leader? 2) what are its nonverbal indicators? To address these questions, we propose eight cues of nonverbal leadership and conduct a two-step validation study on a novel dataset (video, MoCap) of dance improvisation. To explore various leadership strategies, we introduce constraints on how dancers communicate by manipulating their shared sensory channels. In the first stage, 27 persons carried out continuous annotation of leadership in the recorded videos; in the second stage, 92 persons watched 25 short segments indicating who the leader was and reported perceived leadership cues. The results indicate 1) a high consensus among observers regarding nonverbal leadership, but only for certain video segments, and 2) that five leadership cues were frequently observed in our dataset. In the final part, we explore the feasibility of automatically detecting nonverbal leadership using hand-crafted cues and standard machine learning techniques.
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关节全身即兴表演中的非语言领导
在这项工作中,我们研究了非语言领导力,并解决了两个研究问题:1)在没有指定领导者的非结构化联合全身活动中,是否有可能从非语言线索感知领导力?它的非语言信号是什么?为了解决这些问题,我们提出了八种非语言领导的线索,并在一个新的舞蹈即兴数据集(视频,动作捕捉)上进行了两步验证研究。为了探索不同的领导策略,我们引入了舞者如何通过操纵他们共同的感觉通道进行交流的约束。第一阶段,27人在录制的视频中对领导力进行连续标注;在第二阶段,92人观看了25个简短的片段,说明谁是领导者,并报告他们感知到的领导线索。结果表明:1)观察者对非语言领导有高度的共识,但仅限于某些视频片段;2)我们的数据集中经常观察到五种领导线索。在最后一部分,我们探讨了使用手工制作的线索和标准机器学习技术自动检测非语言领导的可行性。
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来源期刊
IEEE Transactions on Affective Computing
IEEE Transactions on Affective Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
15.00
自引率
6.20%
发文量
174
期刊介绍: The IEEE Transactions on Affective Computing is an international and interdisciplinary journal. Its primary goal is to share research findings on the development of systems capable of recognizing, interpreting, and simulating human emotions and related affective phenomena. The journal publishes original research on the underlying principles and theories that explain how and why affective factors shape human-technology interactions. It also focuses on how techniques for sensing and simulating affect can enhance our understanding of human emotions and processes. Additionally, the journal explores the design, implementation, and evaluation of systems that prioritize the consideration of affect in their usability. We also welcome surveys of existing work that provide new perspectives on the historical and future directions of this field.
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