Influence of the feature space on the estimation of hand grasping force from intramuscular EMG
The study compares the performance of different combinations of nine features extracted from intramuscular electromyogram (EMG) recordings for the estimation of grasping force within the range 0100% maximum voluntary contraction (MVC). Single-channel intramuscular EMG was recorded from the flexor digitorum profundus (FDP) muscle from 11 subjects who exerted three force profiles during power grasping. The ability of the features to estimate force with a 1st order polynomial (poly1) and an artificial neural network (ANN) model was assessed using the adjusted coefficient of determination (R2). Willison amplitude (WAMP) and root mean square (RMS) showed the highest R2 (?0.88) values for poly1. The performance of all the features to predict force significantly increased (P<0.01) when an ANN was applied. In this case, the Modified Mean Absolute Value (MMAV) demonstrated the best performance (?0.91). The results showed that a single channel intramuscular EMG recording represents the entire grasping force range (0100% MVC) measured from the FDP muscle. The association between EMG and force depends on the features extracted and on the model.
grasping force intramuscular emg artificial neural network emg features proportional control hysteresis