Flexible and self-adhesive strain sensor based on GNSs/MWCNTs coated stretchable fabric for gesture monitoring and recognition

Abstract

Fabrics are considered to be promising alternatives to silicon wafers in wearable electronics, and the strain sensors developed based on them have attracted widespread attentions for their promising applications in health and motion monitoring, soft robotics and human-computer interaction, etc . However, existing fabric-based strain sensors (FSS) are usually prepared by complicated methods which greatly limit their practical applicability. Herein, high-performance FSSs were prepared by transferring graphene nanosheets (GNSs)/multi-walled carbon nanotubes (MWCNTs) mixing ink onto stretchable cloth adhesive tape via a simple one-step screen-printing method. The sensing range and sensitivity of the prepared FSSs were adjustable by designing different sensing patterns, and the FSS with a high GF (40) and a suitable strain range (30%) was used for detection of finger bending angles. The optimized FSS features excellent linearity (0.99), outstanding durability (>5,000), superior breathability, good adhesion and ease of storage and use. Combined with morphological characterization and tensile tests, the sensing principle of the FSS was explained. Furthermore, a FSS-based sensing glove was integrated, and by combined with LSTM deep learning model; it achieves a high accuracy (95%) of dynamic gesture recognition and can be used to control manipulator which demonstrates its promising applications in the field of smart wearable electronics and human-computer interaction. • This work adopts a low-cost and simple screen-printing method to fabricate high-performance GNSs/MWCNTs-based strain sensors on 3 M stretchable tape. • The morphology, principle, and performance of the sensor s are described and characterized. • The sensing glove were made with prepared sensors and combined with the LSTM deep learning model to recognize dynamic gestures.