Motor Control Information Extracted from Surface EMG as Muscle Force Estimation

The aim of this paper is to introduce an extension to a force estimation technique based on activity index and to compare it to two other muscle force estimation techniques that also use the motor control information on the same set of surface EMG signals. Our new method is called motor unit twitch force technique and the compared methods include motor unit action potential rate and activity index. The main difference of the three compared methods lies in the extraction of the motor control information from multi-channel surface EMG. Motor unit action potential rate and activity index measure global muscle activity as they represent the summation of innervation pulse trains of all active motor units, while twitch force technique decomposes the surface EMG and obtains the activity of all active individual motor units separately. This means a great improvement over activity index and motor unit action potential rate methods as both force regulation principles, i.e. motor unit recruitment and firing rate modulation can be observed. Surface EMG signals used in the experiment were recorded from biceps brachii muscle during elbow flexion on five subjects. Two-dimensional matrix of surface electrodes (13 rows by 5 columns) was applied. Isometric constant force contractions at three different force levels were performed, i.e. at 5, 10 and 30 % of maximal voluntary contraction. Torque produced at the elbow joint was measured simultaneously with surface EMG. The force estimation error of the methods was measured by root mean square error between the recorded and estimated force. Our new motor unit twitch force technique reduced the muscle force estimation error significantly, for 13% when compared to the motor unit action potential rate, and for 2% when compared to the activity index method. Key-Words: surface electromyography, muscle force estimation, EMG force relationship, MUAP rate, activity index, twitch force