ArtCognition：一种多模态AI框架，用于从视觉和运动学绘画线索中感知情绪状态
ArtCognition: A Multimodal AI Framework for Affective State Sensing from Visual and Kinematic Drawing Cues
AI总结：
ArtCognition通过融合视觉和运动学绘画线索，提供非侵入性情绪状态评估的新方法，验证了其在心理测量中的潜力。
摘要：
人类情绪和心理状态的客观评估是一个重大挑战，尤其是在非语言渠道中。本文引入了数字绘画作为一种丰富且未被充分探索的模态，用于情绪感知。我们提出了一种新的多模态框架，名为ArtCognition，用于自动分析House-Tree-Person（HTP）测试，这是一种广泛使用的心理仪器。ArtCognition独特地融合了两种不同的数据流：静态视觉特征来自最终作品，通过计算机视觉模型捕获，以及动态行为运动学线索，这些线索来自绘画过程本身，例如笔触速度、暂停和流畅度。为了弥合低级特征和高级心理解释之间的差距，我们采用了检索增强生成（RAG）架构。这使分析建立在已确立的心理学知识上，增强了可解释性并减少了模型幻觉的可能性。我们的结果表明，视觉和行为运动学线索的融合比单独的模态提供了更细致的评估。我们展示了提取的多模态特征与标准化心理指标之间有显著的相关性，验证了该框架作为可扩展工具支持临床医生的潜力。这项工作贡献了一种非侵入性情绪状态评估的新方法，并为技术辅助心理健康护理开辟了新途径。
英文摘要：
The objective assessment of human affective and psychological states presents a significant challenge, particularly through non-verbal channels. This paper introduces digital drawing as a rich and underexplored modality for affective sensing. We present a novel multimodal framework, named ArtCognition, for the automated analysis of the House-Tree-Person (HTP) test, a widely used psychological instrument. ArtCognition uniquely fuses two distinct data streams: static visual features from the final artwork, captured by computer vision models, and dynamic behavioral kinematic cues derived from the drawing process itself, such as stroke speed, pauses, and smoothness. To bridge the gap between low-level features and high-level psychological interpretation, we employ a Retrieval-Augmented Generation (RAG) architecture. This grounds the analysis in established psychological knowledge, enhancing explainability and reducing the potential for model hallucination. Our results demonstrate that the fusion of visual and behavioral kinematic cues provides a more nuanced assessment than either modality alone. We show significant correlations between the extracted multimodal features and standardized psychological metrics, validating the framework's potential as a scalable tool to support clinicians. This work contributes a new methodology for non-intrusive affective state assessment and opens new avenues for technology-assisted mental healthcare.
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。
