Reflects on developments in noninvasive electromyography, and includes advances and applications in signal detection, processing and interpretation Addresses EMG imaging technology together with the issue of decomposition of surface EMG Includes advanced single and multi-channel techniques for information extraction from surface EMG signals Presents the analysis and information extraction of surface EMG at various scales, from motor units to the concept of muscle synergies.

This thesis aims to develop an interfacing mechanism for controlling prosthetic devices using Force Myography signal (FMG) and various hand gesture classifications. The FMG signals have been collected through three piezoelectric sensors banded around the forearm and Omega Data Acquisition (DAQ) System. The recorded data has been imported into Matlab, Simulink software for analysis and classification. The hand motion has been recorded through Virtual Motion Glove (VMG), and utilized in the system identification procedure to find out the dynamic relationship between the hand gesture and the corresponding FMG signals. Several classification and recognition models have been considered. Tree Decision Learning and Support Vector Machine (SVM) showed high accuracy results. Both of these estimated models generate above 82% of accuracy in terms of classification. The feasibility of the FMG signal for the implementation of a control system in the prosthetic hand is also tested. The result shows a high degree of accuracy in replicating the grasping gestures using threshold method. To limit and control, both the position and the amount of force applied at the fingertips of a prosthetic hand, a dynamic relationship has been established with the corresponding FMG signal through system identification method. These relationships will provide a useful foundation for the implementation and utilization of control system in an assistive device. In order to see the performance of FMG over electromyography (EMG), a comparative analysis has been performed by collecting EMG signals from the same groups of muscles. Unlike EMG, FMG signal is not affected by sweat, skin impedance, and doesn't need a reference signal.

Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and successfully implemented in the position control of different prosthetic hands. An estimation of the intended torque of the user could also provide sufficient information for an effective force control of hand prosthesis or an assistive device. This thesis presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control an exoskeleton prototype that can function as an assistive device. Data from eight volunteers was gathered and Support Vector Machines (SVM) was used for classification. An average testing accuracy of 88% was achieved for nineteen classes. The classification and control algorithm implemented was executed in less than 125 ms. The results of this study showed that real-time classification of sEMG using SVM for controlling an exoskeleton is feasible.

This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson’s; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.