This paper introduces a method for estimating the maximum depth (sub-millimeter) of minor cracks on the surface of aluminum plates used in the aeronautical industry. A set of C-scan eddy current (EC) images, including real and imaginary parts of the impedance, is analyzed to extract suitable features after reducing noise effects, such as background noises and edge noises. Based on the obtained features, e.g. maximum impedance, the background feature, background noises, type of sensors, a Multilayer Perceptron (MLP) Neural Network is built to estimate the maximum depth of the cracks. The network is optimized based on loss functions, such as mean absolute error and mean squared error. An optimal network structure with five neurons in the first hidden layer and eight neurons in the second hidden layer is chosen. The obtained result indicated that the relative error of estimations is lower than 10% for almost all experimental tested samples.

Rehabilitation robots have shown a promise in aiding patient recovery by supporting them in repetitive, systematic training sessions. A critical factor in the success of such training is the patient’s recovery progress, which can guide suitable treatment plans and reduce recovery time. In this study, a neural network-based approach is proposed to estimate the patient’s recovery, which can aid in the development of an assist-as-needed training strategy for the gait training system. Experimental results show that the proposed method can accurately estimate the external torques generated by the patient to determine their recovery. The estimated patient recovery is used for an impedance control of a 2-DOF robotic orthosis powered by pneumatic artificial muscles, which improves the robot joint compliance coefficients and makes the patient more comfortable and confident during rehabilitation exercises.