Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation
This addresses the challenge of evaluating textile comfort in remote settings, such as online shopping, by enabling tactile transmission, though it appears incremental as it builds on existing sensing and actuation concepts with a novel learning approach.
The paper tackles the problem of remotely transmitting tactile sensations of textiles for applications like online shopping, where users cannot physically touch fabrics, by proposing Telextiles, an interface that uses contrastive self-supervised learning to create a latent space for textiles, enabling the transmission of unknown tactile sensations with a method that organizes 16 textile samples based on 1D distance for selection.
The tactile sensation of textiles is critical in determining the comfort of clothing. For remote use, such as online shopping, users cannot physically touch the textile of clothes, making it difficult to evaluate its tactile sensation. Tactile sensing and actuation devices are required to transmit the tactile sensation of textiles. The sensing device needs to recognize different garments, even with hand-held sensors. In addition, the existing actuation device can only present a limited number of known patterns and cannot transmit unknown tactile sensations of textiles. To address these issues, we propose Telextiles, an interface that can remotely transmit tactile sensations of textiles by creating a latent space that reflects the proximity of textiles through contrastive self-supervised learning. We confirm that textiles with similar tactile features are located close to each other in the latent space through a two-dimensional plot. We then compress the latent features for known textile samples into the 1D distance and apply the 16 textile samples to the rollers in the order of the distance. The roller is rotated to select the textile with the closest feature if an unknown textile is detected.