title: A dynamic EMG-torque model of elbow based on neural networks.
abstract: In this paper, a dynamic EMG-torque model of the elbow joint is developed based on ANN, and two novel test methods are proposed to validate its generalization performance. A time-delay neural network (TDNN) model is built and proved to have less risk of overfitting than the most-used multilayer feedfoward neural network (MFNN) model for dynamic EMG-torque modeling. Both EMG and kinematic features are included in the input of ANN, but the zero-EMG test shows that the trained ANN is part of the inverse joint dynamics rather than the EMG-torque model, and some random samples for ANN training are added to overcome this problem. The single-muscle test shows that an inappropriate choice of the motion type may cause the model to estimate wrong torque directions. After tuning and testing, the root mean square error (RMSE) across all subjects is 0.60±0.20 N.m.
authors.lastname: Liang Peng
authors.lastname: Zeng-Guang Hou
authors.lastname: Weiqun Wang
mesh_terms: D004550:Elbow; D004551:Elbow Joint; D004576:Electromyography; D006801:Humans; D008954:Models, Biological; D018482:Muscle, Skeletal; D016571:Neural Networks, Computer; D019415:Torque