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.

翻译：服装的触觉感知对穿着舒适度至关重要。为揭示影响服装舒适度的物理特性，需要在滑动过程中系统性地采集触觉数据。我们提出了一种基于机械臂的系统，用于从完整服装上采集触觉数据。该系统通过模拟指尖进行滑动测量，同时精确控制速度与方向，从而构建带运动标签的多模态触觉数据库。机器学习评估表明，引入运动相关参数提升了音频与加速度数据的识别准确率，证实了运动标签在表征服装触觉感知方面的有效性。该系统提供了一种可扩展、非破坏性的服装触觉数据采集方法，为未来织物感知与复现研究提供了支持。