使用数据驱动方法对人际同步状态进行分类:对社会互动理解的影响
Roi Yozevitch1, Anat Dahan2, Talia Seada3
1Department of Computer Science, Ariel University, Ariel, 40700, Israel. roiyo@ariel.ac.il.
Scientific reports
|July 10, 2023
概括
研究人员分析了手的动作,以确定人际运动同步状态. 较慢的运动速度与更高的同步相关,特别是在增加的认知负载下,有助于社会互动分析.
科学领域:
- 人与计算机的互动.
- 社会机器人社会机器人
- 认知科学是一种认知科学.
背景情况:
- 人际运动同步对于社会互动至关重要.
- 对于实时同步检测,存在有限的算法方法.
- 了解同步动态可以为社会互动分析提供信息.
研究的目的:
- 开发一种数据驱动的方法来识别人际运动同步状态.
- 要区分自发和故意的同步模式.
- 探索运动速度,认知负载和同步之间的关系.
主要方法:
- 使用3D深度摄像头数据分析手部运动.
- 在单个实验框架上应用XGBoost机器学习模型.
- 量化运动速度和同步模式跨主题的量化.
主要成果:
- 精确区分自发和故意的同步模式 (接近[公式:见文本]的准确性).
- 在不同受试者的同步模式中一致发现较慢的运动速度.
- 证据表明,较高的认知负载任务会导致运动速度减慢和同步性增加.
结论:
- 运动速度是人际同步状态的关键指标.
- 认知负载调节了速度与同步关系.
- 在实时社会互动评估和诊断社会缺陷 (如自闭症谱系障碍) 中的潜在应用.
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