Empirical modelling and classification of surface electromyogram

This thesis develops an effective feature extraction technique for sEMG signals. Surface electromyography (sEMG) is the recording of a muscle’s electrical activity from the surface of the skin. The signal contains information that is related to the anatomy and physiology of the muscle. In clinical applications, the signal is used for the diagnosis of neuro-muscular diseases and disorders. Another application of sEMG is for device control application where the signal is used for controlling devices such as prosthetic devices, robots, and human – machine interfaces. Signal classification is used to extract relevant information that represent a particular state (or class) of the sEMG signal. This stater (or class) of the sEMG depicts the information about the underlying pathology or is used as control input for other devices. Therefore it is important that the sEMG is classified in to the relevant class with high accuracy to ensure reliable application in a given field. Many researchers have attempted to improve the classification accuracy of the sEMG signal. Generally the number of electrodes attached to the surface of the skin also needs to be increased in order to increase the classification accuracy. In some cases this number becomes prohibitively high. On the other hand, with a decrease in the number of electrodes the classification accuracy has been reported to decrease. In order to overcome these challenges, in this thesis a new feature extraction technique has been developed. As opposed to the established global time or frequency domain analysis of the sEMG signal, the technique developed in this thesis relies on the well established volume conduction model of sEMG generation. Developed feature extraction technique is then applied to sEMG recorded from low level digital contraction with low signal to noise ratio. A high classification rate of approximately 93% in four classes of low level contraction was achieved by using single channel of sEMG recording. It was further established that the placement of electrode did not have significant effect on the accuracy and reliability of the classification. Further developments that may improve on the methods established in this thesis are presented in the end