Fingerprint-Mimetic Fibrous Glove via Shear-Induced Alignment and Dynamic Compensation for Multimodal Tactile Sensing

Abstract

Flexible devices are key for human–machine interaction in smart homes and virtual reality. However, existing wearable devices are limited by single-mode sensing, irreversible conductive network damage under deformation, and a lack of tactile enhancement, hindering high sensitivity and multimodal perception. Herein, inspired by the arc-shaped topology of human fingerprints, we propose a fiber-based multimodal tactile smart glove (silver/ionic liquid/TPU glove, named SIT Glove). Using Jeffery orbit dynamics, shear flow during wet-spinning aligned silver nanoflakes (AgNFs) axially, while an ionic liquid served as a dynamic conductive compensation medium, yielding SIT fibers with an “electronic network + ionic polarization” synergy. The fibers exhibit excellent strain, pressure, and temperature sensing properties: conductivity 1.66 × 104 S/m, elongation at break 387.96%, gauge factor 9826.244 (80–100% strain), pressure sensitivity 4.4340 kPa–1 (50–60 kPa), and TCR –0.0453 °C–1 (30–40 °C). Meanwhile, embroidering SIT fibers onto glove fingertips in fingerprint patterns created an 18-channel tactile array. Furthermore, with machine learning for gesture recognition and deep learning (1D-CNN) for fabric classification, the glove achieved 93.98% accuracy for 6 gestures and 86.84% for 4 fabrics and demonstrated body temperature monitoring, hot/cold sensing, virtual game interaction, and fan control. This work offers a bioinspired paradigm for wearable devices with high sensitivity, a wide response range, and multifunctional integration.