Tactile Data Recording System for Clothing with Motion-Controlled Robotic Sliding
This work addresses the need for scalable, non-destructive tactile data collection in fabric perception studies, though it is incremental as it builds on existing robotic and sensing methods.
The researchers tackled the problem of systematically collecting tactile data from clothing to understand wearer comfort by developing a robotic arm-based system that performs stroking measurements with precise motion control, and they found that including motion-related parameters improved identification accuracy for audio and acceleration data in machine learning evaluations.
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.