Measuring muscle activity using surface electrodes, also called surface EMG or sEMG, is a popular method of acquiring signals that reflect the intent of a user to control muscles and connected limbs. This signal can be used as a human-machine interface to control exo-aids. There are many different ways of processing the sEMG signal, but a paper or report describing the difference in the performance of these techniques in different scenarios is lacking.
This thesis aims to fill the gap and provide a general overview of the effectiveness of pre-whitening, different filtering techniques to improve SNR, and different amplitude estimation techniques, with the aim of accurately estimating the force that is applied during an isometric contraction.