Estimation of Hip Joint Torque By Using Parallel Fusion Neural Dynamics Model

Lei Liu, Jiaxin Wang, Qian Xiang, Zhendong Zhao, Yong Liu, Shijie Guo

Abstract: Estimation of hip joint torque is of great significance for exoskeleton assist torque planning. However, traditional single neural network models are difficult to reliably estimate human joint torque and the dynamic models based on physical theorem are limited by the measurement technology of ground reaction force. Therefore, this paper proposes a parallel fusion neural dynamic model that incorporates LSTM, NTM, and Newton-Euler dynamical equation. The model only needs human kinematic parameters as inputs to complete the estimation of human hip joint torque. To evaluate the estimation performance, this paper introduces relative accuracy as an evaluation standard. The experimental result shows that the estimation performance of the fusion model is greatly improved compared with the traditional single neural network models. The fusion model proposed in this study can be used to estimate the torque of the hip joint. It can be integrated into the exoskeleton control system and used as the basis for planning the assisting torque of the exoskeleton.