Facile and direct 3D printing of smart glove for gesture monitoring

Abstract

The distinctive characteristics of electrically conductive fabrics, including their flexibility, breathability, and comfort, have led to their recognition as a viable substitute for silicon wafers in wearable electronics. However, the difficulty of constructing sensors with three-dimensional (3D) structure on woven fabrics significantly limits their sensitivity and sensing range. Layer-by-layer 3D printing of entire smart textile sensing components has enabled the development of high-performance sensors with enhanced sensitivity and sensing range. This research endeavors to produce a smart glove with superior performance by incorporating strain and pressure sensors by 3D printing a composite conductive ink, consisting of multi-walled carbon nanotubes (MWCNTs), graphene nanosheets (GNSs), fumed silica (FSiO₂) and Ecoflex, and encapsulated ink directly onto a commercially available fabric glove. The 3D structure of the sensing layer and the sensing material were intentionally designed to achieve desired performance. The smart glove demonstrates a high gauge factor (GF ∼ 35) and a strain range of 0–50% for strain detection. Additionally, it exhibits a high sensitivity of ∼0.07 kPa⁻¹ and a sensing range of 1000 kPa for pressure examination, which facilitates precise detection of finger bending angles and fingertip contact pressures. The smart glove also shows excellent linearity, repeatable resistance response, favorable cycling characteristics in both strain and pressure detecting, and were unaffected by temperature and humidity. The combination of the smart glove with a Long Short-Term Memory (LSTM) deep learning model achieves a high accuracy (100%) for dynamic gesture recognition and manipulator control, demonstrating their potential for smart wearable electronics and human-computer interaction.