An ANN-Based Single Calibration Impedance Measurement System for Skin Impedance Range

Impedance measurement has been widely used as an effective indicator for characterizing samples. Traditional high accuracy impedance analyzers are complicated, expensive, and non-portable. Many kinds of research require low cost, portable, and high precision impedance analyzer devices. AD5933 impedance analyzer integrated circuit has been popularly used for fulfilling these requirements. There are many successful applications of the AD5933 integrated circuit; however, the most significant drawback is nonlinear calibration requirements for high precision measurement in a specified range. The calibration and unknown impedances must be close enough to each other for better measurement accuracy. In the literature, calibration impedance arrays increasing the complexity and processing time are commonly used for high accuracy measurements. In this study, an artificial neural network-based signal post-processing algorithm is proposed to overcome the calibration requirements of the AD5933 integrated circuit, which requires different impedances for different ranges. In the literature, a neural network-based solution has not been applied to this phenomenon. An application specific artificial neural network topology is developed and trained for high precision impedance measurement using a fixed calibration impedance. The proposed measurement system is designed for operating in the range of nominal skin impedance. The average mean square error of measurements is obtained as 0.206%. Although a fixed calibration resistance is used, the proposed signal post-processing approach significantly improved the measurement accuracy of the AD5933 integrated circuit. The high accuracy measurement results prove the effectiveness of the proposed measurement system. The developed system offers portable, simple, and low cost high precision impedance analyzer.