用自然语言分析用户对触觉信号的情感描述，实现情感与触觉信号的精准对应。
Grounding Emotional Descriptions to Electrovibration Haptic Signals
- 通过NLP提取用户描述中的感官与情绪关键词并聚类为概念
- 发现关键词簇与触觉信号特征（如脉冲数）存在显著相关性
- 适合人机交互、情感计算和触觉设计领域的研究者参考
在多种应用中，设计具备感官与情感特性的触觉信号可提升用户体验。自由表达的用户语言蕴含丰富的感官与情感信息（如“这个信号感觉平滑且令人兴奋”），但将这些描述与具体触觉信号关联的研究仍较少（即语言接地问题）。为此，我们开展了一项研究，邀请12名用户描述32种在表面触觉（电振动）显示设备上感知到的信号感受。我们构建了一个基于自然语言处理（NLP）的计算流程，采用GPT-3.5 Turbo和词嵌入方法提取感官与情绪关键词，并将其聚类为语义簇（即概念）。通过相关性分析，我们将关键词簇与触觉信号特征（如脉冲数）进行关联。该方法验证了计算手段分析触觉体验的可行性，未来计划建立触觉体验的预测模型。
原文摘要 · Abstract (English)
Designing and displaying haptic signals with sensory and emotional attributes can improve the user experience in various applications. Free-form user language provides rich sensory and emotional information for haptic design (e.g., ``This signal feels smooth and exciting''), but little work exists on linking user descriptions to haptic signals (i.e., language grounding). To address this gap, we conducted a study where 12 users described the feel of 32 signals perceived on a surface haptics (i.e., electrovibration) display. We developed a computational pipeline using natural language processing (NLP) techniques, such as GPT-3.5 Turbo and word embedding methods, to extract sensory and emotional keywords and group them into semantic clusters (i.e., concepts). We linked the keyword clusters to haptic signal features (e.g., pulse count) using correlation analysis. The proposed pipeline demonstrates the viability of a computational approach to analyzing haptic experiences. We discuss our future plans for creating a predictive model of haptic experience.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。