On the estimation of isometric wrist/forearm torque about three axes using Force Myography

Abstract

Much of the morbidity and disability associated with industrial work settings arise from accidents involving humans and robots. Force Myography (FMG) is a potential technique to be used as an additional control measure for safer human-robot interaction without the need for robot hardware modification or replacement. The FMG signals represent the volumetric changes in the forearm due to muscle contraction, which were acquired using a Force Sensitive Resistor strap. A 1DOF torque sensor was used to model the point of interaction between a robot and a human. The following isolated upper extremity movements were considered: forearm pronation-supination, wrist flexion-extension and wrist radial-ulnar deviation. Torque regression models based on FMG data were created with two machine learning methods: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Performance indices were defined and used for the comparative study between the two learning methods. The results demonstrated the feasibility of using FMG to estimate torque with accuracies around 90%. Both methods also demonstrated strong intra- and inter- participant consistency of FMG signals. The results will be beneficial for measuring the contact force between human and robot during their interaction.