We present textile weaving as a new technique for the manufacture of miniature electrochemical sensors with significant advantages over current fabrication techniques. Biocompatible silk yarn is used as the material for fabrication instead of plastics and ceramics used in commercial sensors. Silk yarns are coated with conducting inks and reagents before being handloom-woven as electrodes into patches of fabric to create arrays of sensors, which are then laminated, cut and packaged into individual sensors. Unlike the conventionally used screen-printing, which results in wastage of reagents, yarn coating uses only as much reagent and ink as required. Hydrophilic and hydrophobic yarns are used for patterning so that sample flow is restricted to a small area of the sensor. This simple fluidic control is achieved with readily available materials. We have fabricated and validated individual sensors for glucose and hemoglobin and a multiplexed sensor, which can detect both analytes. Chronoamperometry and differential pulse voltammetry (DPV) were used to detect glucose and hemoglobin, respectively. Industrial quantities of these sensors can be fabricated at distributed locations in the developing world using existing skills and manufacturing facilities. We believe such sensors could find applications in the emerging area of wearable sensors for chemical testing.

We report a new image processing algorithm that extracts quantitative information about the concentration of human chorionic gonadotropin (hCG), an important early pregnancy marker, from commercially available qualitative home pregnancy kits. The algorithm could potentially be ported onto a simple camera based cell phone making it a low-cost, portable point-of-care device as opposed to costly and time consuming clinical labs for accurate quantitative determination of hCG. The algorithm takes the image of the test result as input, classifies and determines the hCG concentration based on the RGB intensities of the test line. The efficacy of the algorithm is demonstrated using control samples on commercially available strips as well as novel fabric based strips designed for this application.

Home pregnancy kits typically provide a qualitative (yes/no) result based on the concentration of human chorionic gonadotropin (hCG) present in urine samples. We present an algorithm that converts this purely qualitative test into a semiquantitative one by processing digital images of the test kit's output. The algorithm identifies the test and control lines in the image and classifies an input into one of four different hCG concentration levels based on the color of the test line. The proposed algorithm provides significant improvement over a prior method and reduces the maximum false positive rate to less than 5%. This improvement is achieved by a careful choice of the color space so as to maximize the inter-concentration separability. Also, the proposed method increases the utility of the test kits by providing useful diagnostic information. Furthermore, the algorithm could be ported to a mobile platform to make it particularly helpful in remote rural health monitoring.