Body movements carry important information about a person's emotions or mental state and are essential in daily communication. Enhancing the ability of machines to understand emotions expressed through body language can improve the communication of assistive robots with children and elderly users, provide psychiatric professionals with quantitative diagnostic and prognostic assistance, and aid law enforcement in identifying deception. This study develops a high-quality human motor element dataset based on the Laban Movement Analysis movement coding system and utilizes that to jointly learn about motor elements and emotions. Our long-term ambition is to integrate knowledge from computing, psychology, and performing arts to enable automated understanding and analysis of emotion and mental state through body language. This work serves as a launchpad for further research into recognizing emotions through analysis of human movement.

翻译：身体动作承载着个体情绪或心理状态的重要信息，在日常沟通中不可或缺。提升机器理解通过肢体语言表达情感的能力，可以改善辅助机器人与儿童及老年用户的交流，为精神科专业人员提供量化的诊断与预后辅助，并协助执法部门识别欺骗行为。本研究基于拉班动作分析编码系统构建了高质量的人体运动要素数据集，并利用该数据集联合学习运动要素与情绪。我们的长期目标是整合计算科学、心理学与表演艺术领域的知识，实现通过肢体语言自动理解与分析情绪及心理状态。本工作可作为后续通过分析人体动作识别情绪研究的重要起点。