Inverse dynamics modeling and analysis of healthy human data for lower limb rehabilitation robots

Abstract

Bio-controllers inspired by the characteristics of the human lower limb play an important role in the study of lower limb rehabilitation robots (LLRRs). However, the inverse dynamics modeling of robots for human lower limb rehabilitation remains a challenging issue due to the non-linear and strong coupling characteristics of the bio-controller. To further improve the inverse dynamics model’s accuracy, this paper proposes the use of a non-parametric modeling approach in order to learn it. In detail, the main idea is to use the motion data of the main joints of the lower limbs of healthy people as an input and the corresponding joint moments as an output, which are learned through the training of a neural network. To ensure that the learned model can be used on LLRRs, all data collected in this paper are real data from human lower limbs. In addition, since the type of data collected is time series, this paper proposes the use of the long short-term memory (LSTM) and gated recurrent unit (GRU) networks to learn the inverse dynamics model of the robot-like human lower limb and to compare the learning effects of the two networks. The evaluation metric for both network models is the root mean square error (RMSE). The experimental results show that both networks have sound learning effects, and that the GRU network has a more significant learning ability than the LSTM network.