从动作到身势学：通过身体运动提取人类心理状态
From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
AI总结：
本文提出一种基于3D骨骼数据的身势学识别框架，结合ST-GCN与CNN及迁移学习，在DUET数据集上实现可扩展、准确且保护隐私的人类心理状态推断，用于增强RL模拟。
摘要：
理解人类与建筑环境之间的动态关系是环境心理学到强化学习（RL）等多个学科中的关键挑战。建模这些交互的一个核心障碍是，无法以既具有泛化性又保护隐私的方式捕捉人类心理状态。传统方法依赖于理论模型或问卷，这些方法范围有限、静态且劳动密集。我们提出了一种身势学识别框架，直接从3D骨骼关节数据中推断人类活动的交际功能——即身势学。该框架结合了时空图卷积网络（ST-GCN）与卷积神经网络（CNN），利用迁移学习绕过了手动定义物理动作与心理类别之间映射的需求。该方法在保留用户匿名性的同时，揭示了身体运动中反映认知和情感状态的潜在结构。我们在双人用户参与（DUET）数据集上的结果表明，该方法能够实现可扩展、准确且以人为中心的行为建模，为增强基于RL的人-环境交互模拟提供了新途径。
英文摘要：
Understanding the dynamic relationship between humans and the built environment is a key challenge in disciplines ranging from environmental psychology to reinforcement learning (RL). A central obstacle in modeling these interactions is the inability to capture human psychological states in a way that is both generalizable and privacy preserving. Traditional methods rely on theoretical models or questionnaires, which are limited in scope, static, and labor intensive. We present a kinesics recognition framework that infers the communicative functions of human activity -- known as kinesics -- directly from 3D skeleton joint data. Combining a spatial-temporal graph convolutional network (ST-GCN) with a convolutional neural network (CNN), the framework leverages transfer learning to bypass the need for manually defined mappings between physical actions and psychological categories. The approach preserves user anonymity while uncovering latent structures in bodily movements that reflect cognitive and emotional states. Our results on the Dyadic User EngagemenT (DUET) dataset demonstrate that this method enables scalable, accurate, and human-centered modeling of behavior, offering a new pathway for enhancing RL-driven simulations of human-environment interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
