An improved incremental online training algorithm for reducing the influence of muscle fatigue in sEMG based HMI

Considering the problem that stability of surface Electromyographic Signal (sEMG) based human-machine interface (HMI) gradually declines as fatigue takes place in muscles, we propose a novel method for updating samples to improve incremental online training algorithm for support vector machine (SVM). We study the changes of sEMG when muscle fatigue occurs using a method based on continuous wavelet transform, and then applies the improved incremental online SVM for sEMG classification. Experiment results show that the proposed algorithm can be used to improve the classification accuracy and training speed significantly. Furthermore, this method effectively diminish the influence of muscle fatigue during long-term operation of sEMG based HMI. © 2012 IEEE

Applications of the electric potential sensor for healthcare and assistive technologies

The work discussed in this thesis explores the possibility of employing the Electric Potential Sensor for use in healthcare and assistive technology applications with the same and in some cases better degrees of accuracy than those of conventional technologies. The Electric Potential Sensor is a generic and versatile sensing technology capable of working in both contact and non-contact (remote) modes. New versions of the active sensor were developed for specific surface electrophysiological signal measurements. The requirements in terms of frequency range, electrode size and gain varied with the type of signal measured for each application. Real-time applications based on electrooculography, electroretinography and electromyography are discussed, as well as an application based on human movement. A three sensor electrooculography eye tracking system was developed which is of interest to eye controlled assistive technologies. The system described achieved an accuracy at least as good as conventional wet gel electrodes for both horizontal and vertical eye movements. Surface recording of the electroretinogram, used to monitor eye health and diagnose degenerative diseases of the retina, was achieved and correlated with both corneal fibre and wet gel surface electrodes. The main signal components of electromyography lie in a higher bandwidth and surface signals of the deltoid muscle were recorded over the course of rehabilitation of a subject with an injured arm. Surface electromyography signals of the bicep were also recorded and correlated with the joint dynamics of the elbow. A related non-contact application of interest to assistive technologies was also developed. Hand movement within a defined area was mapped and used to control a mouse cursor and a predictive text interface