An approach to noise reduction in human skin admittance measurements

This paper presents the development of a signal averaging algorithm for recovering excitation responses contaminated by overwhelming amount of various types of interference in skin admittance measurements. The algorithm is designed to eliminate Gaussian-distributed noise by use of a recursive approach. The process of recovering low magnitude voltage responses from highly noise-contaminated waveforms is a CPU-intensive task. In real-time measurements, iterative reconstruction algorithm is inefficient and time consuming when slow varying input waveforms are present. To increase the quality of the reconstruction a considerably large number of recursions is required. Increasing the number of recursions is appropriate for batch processing of measurement data. However, the algorithm considers measurements in real-time, whereas required quality of signal reconstruction should be kept independent from the number of recursions.