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

TL;DR

This paper introduces a robotic system for collecting detailed tactile data from clothing during sliding motions, facilitating better understanding of fabric comfort through multimodal data and machine learning analysis.

Contribution

A novel robotic arm-based system that systematically records tactile data from garments with precise motion control, enabling creation of labeled tactile databases for clothing analysis.

Findings

Including motion parameters improves tactile data classification accuracy.

The system enables scalable, non-destructive tactile data collection.

Machine learning confirms the importance of motion-related labels.

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.