An sEMG-driven musculoskeletal model of shoulder and elbow based on neural networks

In this paper, an sEMG-driven musculoskeletal model of human shoulder and elbow joints is built based on time delay neural network (TDNN). Six principal muscles of the upper arm and forearm are included, and the experiment was conducted under isometric contractions with the aid of a planar haptic interface. Both force amplitude and direction were regulated continuously, and the experiment results proved the effectiveness and performance of this modeling method. The model was proved to have less overfitting risk than the most-used basic multilayer forward networks, and the isometric model was proved to be still effective in estimation of slow movement cases.