Prediction of Arm End-Point Force Using Multi-channel MMG

We investigate the effectiveness of a dual-channel MMG signal recorded from the biceps and triceps brachii as a way to predict the isometric forces produced by flexion and extension of the elbow. We asked 8 subjects to apply a range of isometric force levels for both flexion and extension of the elbow while the activity of the two muscles was captured using custom-built MMG sensors. By extracting two characteristic MMG features, the ´MMG score´ and the root mean square power spectrum (rmsPS), we applied an artificial feed-forward neural network (NN) to generate a mapping between the MMG signals and the actual forces generated. The accuracy of the NN predictor was evaluated using a 10-fold cross validation, achieving an average across subject R2 of 0.76 and a RMSE of 8.6% of the maximum voluntary isometric contraction (MVC).