Identification of the human arm kinetics using dynamic recurrent neural networks

摘要

Aritificial neural networks offer an exciting alternative for modeling and identifying complex non-linear systems. This paper investigates the identification of discrete-time non-linear systems using dynamic recurrent neural networks. We use this kind of networks to efficiently identify the complex temporal relationship between the patterns of muscle activation represented by the electromyography signal (EMG) and their mechanical actions in three-dimensional space. The results show that dynamic neural networks provide a successful platform for biomechanical modeling and simulation including complex temporal relationships.