A Resistive Network Model for Conductive Knitting Stitches

Wearable electronic knitwear has recently been gaining the attention of both researchers and industrial sectors. Combining knitting technology with electronics may become a dominant trend in the future. There is a need to develop an analytical equation to model the complex resistive network for a given set of conductive stitches in order to meet the high demand for rapid prototype designing of smart knitwear. Currently, a matrix equation with high dimensionality must be solved, which is impractical and requires high computational power, retarding the growing demand for smart knitwear design with resistive routing paths. The routing network embedded into smart knitwear with conductive knitting stitches is a critical element for connecting different electronic devices, such as textile electrodes, sensors, and heaters. The knitting stitch made with conductive yarn is one of the essential building blocks for textile-based circuitry and controls the power distribution in the wearable electronic knitwear. Knitwear exhibits high flexibility and comfort, making it a good candidate for applications in sports, medicine, and other areas that incorporate electronic devices. Textile-based electronic circuits have become a key element in recent developments in intelligent textiles. Different manufacturing processes for textile-based electronic circuits have been reported, such as embroidery, weaving, printing, and coating. However, few studies have given an analytical equation and a systematic approach to obtaining the equivalent resistance of the conductive knitting stitch network. This paper describes work done to derive analytical equations to model a given resistive network of conductive knitting stitches, built with conductive yarn, and based on the common intarsia knitting and jersey knitting techniques. The experimental results revealed that the derived equations could accurately model the equivalent electrical resistance of conductive stitches of knitwear and could greatly simplify existing models.