Exploratory Evaluation of the Force Myography (FMG) Signals Usage for Admittance Control of a Linear Actuator

Force Myography (FMG) is a technique involving the use of force sensors on the surface of the limb to detect the volumetric changes in the underlying musculotendinous complex. This paper investigates the feasibility of employing force-sensing resistors (FSRs) worn on the arm that measure the FMG signals for force prediction in dynamic conditions. The predicted force value can be mapped into velocity value to control a linear actuator to track hand movements. Two FMG bands were donned on the participant wrist and forearm muscle belly to measure the FMG signals during force exertion. An accurate force transducer was used for labeling the FM G signals by measuring the exerted force. Three regression algorithms including kernel ridge regression (KRR), support vector regression (SVR), and general regression neural network (G RNN), were used in this study for predicting hand force using the FMG signals. The data were collected for 200 seconds for training the regression model. Then, the trained model was used for online force prediction for 430 seconds. The testing accuracy was 0.92, 0.90 and 0.79, using KRR, SVR and GRNN, respectively. These results will be beneficial for monitoring hand force during human-robot interaction or controlling the robot movement.