EMG-Based Neural Network Model of Human Arm Dynamics in a Haptic Training Simulator of Sinus Endoscopy *

This paper proposes an EMG-dependant neural network-based model of human forearm during interaction with a haptic training simulator of sinus endoscopy. We used a conventional lumped mass-spring-damper model as a base model, beside which we took effects of muscle activation level, using surface electromyography (EMG) signals, into consideration. Unknown parameters of a five-parameter mass-spring-damper model are optimised using experimental force and position data with a Levenberg–Marquardt (LM) algorithm. In the training phase, parallel to this lumped model, a neural network (NN) structure is trained to learn the nonlinear mapping between the EMG signals (a way of measuring the muscles activation level that can be interpreted as muscle stiffness) and the parameters of the lumped model. In prediction (operational) phase, the trained neural network makes an estimate of the lumped parameters, using EMG and position data. Therefore, as apposed to conventional constant-parameter (CP) models, the parameters of the lumped model are not fixed in this method and are dependent to the muscle stiffness. Eight trials were performed while the operator was asked to to hold one’s arm in a vertical plane such that their elbow had a right angle keep exerting a quasi-static and also reciprocating force in one direction–a linear motion coaxial to their forearm. Haptic interface was programmed in a way to mimic the impedance model of sinus tissue, a nonlinear viscoelastic Kelvin-Voigt model previously developed by the authors. The estimated forces and the experimental forces are compared for two scenarios: once for the proposed EMG-dependant NN-based model and once again for the constant-parameter lumped model. Results demonstrate the precision improvement on the estimation of the exerted force from human hand to the haptic interface in the proposed model.