Stretchable Capacitive Tactile Sensor Array for Accurate Distributed Pressure Recognition

Abstract

Soft capacitive sensors, mainly attributing to their structural simplicity, fast response, and high spatial resolution, have drawn great attention for possible use in many kinds of human–machine interactions. Nevertheless, mechanical coupling and pressure-induced continuous deformation between the contacted areas and other adjacent units would bring unexpected crosstalk and thus vague spatial resolution during distributed pressure recognition. Herein, a stretchable $16\times 16$ capacitive tactile sensor array of minimum proximate crosstalk for distributed pressure recognition is proposed. Benefiting from the introduction of serpentine island bridge structure, the sensor array has displayed excellent stretchability (over 30%) as well as low crosstalk between adjacent units (8.53%) in a wide measuring range (265 kPa) and still maintaining high sensitivity up to 5.40 kPa $^{-{1}}$ , low limit of detection (2 Pa), and fast response time (44 ms) as well as long-term stable working durability for over 1000 cycles. An improved bilinear convolutional neural network (BCNN) integrated with deep residual shrinkage network (DRSN) is proposed to actually heighten the feature extraction capability and thus precise distributed pressure recognition. Cataloged pressure images of capital letter shapes from A to Z in different letter patterns, random angles, and uncertain positions are collected to validate the proposed models. The test results reveal that the recognition accuracy is up to 97.70% in this work and thus provide a more detailed pressure distribution in activated sensing areas.