Tactile Data Recording System for Clothing with Motion-Controlled Robotic Sliding

TL;DR

Problem: quantify clothing tactile sensation during sliding to reveal fabric properties. Approach: a robotic arm with a simulated fingertip stroking garments under preset force, speed, and direction, recording frictional audio, surface images, and acceleration to build motion-labeled multimodal tactile databases. Key findings: motion parameters (velocity and direction) improve clothing-type identification from audio and acceleration data; audio signals carry strong tactile information; acceleration can be redundant when motion labels are accurate. Significance: provides a scalable, non-destructive method to characterize fabric tactile properties, facilitating quality assessment, material classification, and tactile texture reproduction.

Abstract

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.

Figures (1)

• Figure 1: Machine learning architecture. We extracted features from audio and/or acceleration signals. We also investigated integrating motion-related parameter.