Pseudo-Domain Adversarial Networks with Electrical Impedance Tomography for Electrode Offset Error

This paper propose a novel transfer learning approach, Pseudo-Domain Adversarial Network (PDAN), to tackle the issue of electrode displacement in Electrical Impedance Tomography (EIT). Electrode displacement, caused by human movement or improper operation, significantly affects the accuracy of EIT by introducing data errors. Existing solutions either modify the electrode assembly at a high cost or employ recognition algorithms that require retraining from scratch. To overcome these limitations, our work leverages the power of transfer learning to enhance model performance in the target domain by utilizing knowledge from a related task in the source domain. PDAN extends the capabilities of deep adversarial learning by incorporating noisy images to simulate post-electrode rotation scenarios, aiding in the reduction of negative impacts caused by minor electrode displacements. Our method demonstrates superior performance in classifying leg posture data, achieving around 90% accuracy, and proving robust against sensor electrode offset. Experimental results across various datasets validate the effectiveness of PDAN, indicating its potential in addressing complex real-world situations with improved generalization capabilities.