Optical pressure sensors based plantar image segmenting using an improved fully convolutional network

Abstract

Optical pressure sensors in foot scanner system (FSS) based imaging systems can directly and accurately reflect the elastic changes through the plantar tissue to form different pressure values in different regions. In this work, the computational complexity of the sensor dataset from FSS was reduced using an improved full convolution network (FCN) through the AlexNet platform (FCN-AlexNet-8 s). Initially, plantar pressure imaging pre-process and segmentation techniques were developed to extract the region of interests (ROIs) in the captured images from the optical pressure sensors. The experiment established superior performance in the index evaluation system, including mean square error (MSE), and peak signal to noise ratio (PSNR) compared to the previous related studies. Furthermore, the proposed method was compared with region based convolutional neural network (R-CNN) and fast R-CNN separately in terms of the layers indices, maximum stride and time consuming. Accordingly, the proposed method is beneficial to decrease the computation complexity of plantar pressure sensor datasets and has potential application on shoe-last customization.