Lower Limb Joint Torque Estimation by Neural Network and Sparse Gaussian Process with RIO Kernel

Abstract

In this study, joint torques in the sagittal plane are estimated using joint angles and electromyography (EMG) signals during subjects' walk at 7 different speeds. First, a general inter-subject model is built by backpropagation neural network (BPNN) with data from 12 subjects. Then, to improve the estimation performance of the inter-subject for a new subject, sparse gaussian process (SGP) with residual estimation using input and output (RIO) kernel is used to compensate for the model as a transfer learning method. The obtained intra-subject model has superior performance with a relatively small amount of data in the training process. This article can be referenced when you have limited training data to estimate the torques on a new subject.