General Perspectives on Electromyography Signal Features and Classifiers Used for Control of Human Arm Prosthetics

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Fundamental Aspects Of Emg

EMG is the electrical activity of skeletal muscles (Basmajian & Deluca, 1985). It represents the summation of the muscle action potentials which cause the contraction of muscle fibers. Recorded EMG data by means of electrodes are amplified and filtered to eliminate the motion artifacts, as well as the environment and device related noises. Rejection of ambient influences on natural muscle activation improves the accuracy and usability of EMG signals. One of the most widely usage of EMG signals is to control the myoelectric-based prosthetics which are used by amputated people. Control scheme for EMG-driven human arm prosthetics includes a sequential series of signal processing (Figure 1).

Figure 1.

Control scheme of multifunctional human arm prosthetics

Key Terms in this Chapter

Rehabilitation: A series of therapy to make injured or amputated people regained lost skills or functions.

Assistive Technology: A branch of technology is used to regain the lost functions of human body parts.

Surface Electromyography: A type of electromyography signal recording method carrying out by means of adhering electrodes to skin surface.

Human Arm Prostheses: Assistive devices which enable to perform lost functions of human arm due to upper or lower arm amputations.

Pattern Recognition: A machine learning process which identifies the pattern of physical systems using data belong to investigated systems.

Feature Classification: A pattern recognition technique that is used to categorize a huge number of data into different classes.

Feature Extraction: A method to obtain meaningful and clear data of a signal.