用机械臂模拟手指滑动，采集衣物触感数据。
Tactile Data Recording System for Clothing with Motion-Controlled Robotic Sliding
- 用机械臂控制滑动速度方向，精准采集触觉数据。
- 加入运动参数后，识别准确率提升，验证标签有效性。
- 非破坏性采集，适合研究服装触感与复现。
衣物的触感对穿着舒适度至关重要。为揭示影响舒适性的物理特性，需系统采集滑动过程中的触觉数据。本文提出一种基于机械臂的完整衣物触觉数据采集系统，通过模拟指尖滑动并精确控制速度与方向，生成带运动标签的多模态触觉数据库。机器学习评估表明，引入运动相关参数可提升音频与加速度数据的识别准确率，证明了运动标签在表征衣物触感方面的有效性。该系统提供了一种可扩展、非破坏性的衣物触觉数据采集方法，有助于未来织物感知与再现的研究。
原文摘要 · Abstract (English)
The tactile sensation of clothing is critical to wearer comfort. To reveal physical properties that make clothing comfortable, systematic collection of tactile data during sliding motion is required. We propose a robotic arm-based system for collecting tactile data from intact garments. The system performs stroking measurements with a simulated fingertip while precisely controlling speed and direction, enabling creation of motion-labeled, multimodal tactile databases. Machine learning evaluation showed that including motion-related parameters improved identification accuracy for audio and acceleration data, demonstrating the efficacy of motion-related labels for characterizing clothing tactile sensation. This system provides a scalable, non-destructive method for capturing tactile data of clothing, contributing to future studies on fabric perception and reproduction.
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