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This MUAP can be recorded by using electrodes placed on the surface of the skin above the muscle. The electrical potential due to contraction of all fibers in a motor unit during a single activation is referred to as the Motor Unit Action Potential (MUAP). Because each motor unit contains a number of muscle fibers that are attached to the motor neuron at various points, the electrical signal of a motor unit is the summation of the action potential of each muscle fiber, which may be phase shifted from the other muscle fibers in that unit ( Perry & Bekey,1981). These motor units are defined as the fundamental unit of contraction and can range from a few muscle fibers for small muscles such as those in the hand and fingers, to thousands of muscle fibers in large muscles such as those in skeletal muscle. Muscle fibers are excited by nerve branches by one motoneuron in groups known as motor units. 1 depicts the generation of electric fields in muscle fibers. This waveform travels the length of the muscle fiber and is known as the Action Potential (AP). This electrical potential causes depolarization of the muscle fiber tissue and a following depolarization waveform. To instantiate a contraction, a neuron generates a small electrical potential on the surface of the muscle fiber.
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The nervous system controls the voluntary movement of various body parts in humans by contracting and relaxing various skeletal muscles. The EMG signal origin and character is necessary background to understand the difficulty of establishing a relationship between surface EMG and torque. However, the complexity of the EMG signal origin has been a barrier for developing a quantitative description of this relation. The relation of surface EMG to torque makes EMG an attractive alternative to direct muscle tension measurements, necessary in many physical assessments.
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The extraction of accurate features from the EMG signals is the main kernel of classification systems and is essential to the motion command identification ( Park &Lee, 1998).ĮMG is the recording of the electrical activity produced within the muscle fibers. In order to achieve this, a feature set must be chosen which maximally separates the desired output classes. Information extracted from EMG signals, represented in a feature vector, is chosen to minimize the control error. It has been proposed that the EMG signals from the body’s intact musculature can be used to identify motion commands for the control of an externally powered prosthesis. Commands for device control are then generated from the classified motions ( Bu et al., 2009). The core part of these human–robot interfaces is a pattern classification process, where motions or intentions of motions are classified according to features extracted from EMG signals. Up to the present, a number of EMG-based human interfaces have been proposed as a means for elderly people and the disabled to control powered prosthetic limbs, wheelchairs, teleoperated robots, and so on. It has been well recognized as an effective tool to generate control commands for prosthetic devices and human-assisting manipulators. It provides an important access to the human neuromuscular system. EMG signals, which are measured at the skin surface, are the electrical manifestations of the activity of muscles. EMG signals have been used as control signals for robotics devices in the past. By a decoding procedure the muscular activity is transformed to kinematic variables that are used to control the robot arm. While the user moves his arm, (EMG) activity is recorded from selected muscles, using surface EMG electrodes. Robot arms are versatile tools found in a wide range of applications.