Abstract
The article presents a proposal to use linear prediction method for a quick analysis of surface myoelectric (EMG) signals. The spectra obtained with the linear prediction (LP) and Fourier methods were compared. The LP method allows for a precise determination of the location and amplitude of the spectrum maximum and observation of changes in muscle tension and contraction phases. EMG spectra of brachial biceps during flexion and extension of the forearm by four adults were analyzed. The optimal width of the time window for the averaging of motor unit action potentials that allows for the observation of changes during contraction was established. It has been found that maximum spectrum during flexion has a significantly higher frequency and amplitude than during the extension of the forearm.
Highlights
The human movement is a result of skeletal muscle contractions under the influence of coordinated nerve impulses coming from the central nervous system
Studies on motor unit action potentials (MUAPs) are performed with the use of needle electrodes in order to learn about the motor unit structure and diagnose neuromuscular diseases [12-14, 1719]
What is interesting from the point of view of diagnostic and rehabilitation activities, such as stimulation with the use of external stimuli, is the averaged spectra from which you can determine the peak frequency of the power spectrum
Summary
The human movement is a result of skeletal muscle contractions under the influence of coordinated nerve impulses coming from the central nervous system. The primary control element of muscle contraction is known as motor unit (MU). It consists of many muscle fibers, and under the influence of neurons excitation motor unit action potentials (MUAPs) are generated. Surface EMG studies have diagnostic significance in medical problems of the musculoskeletal system, in the evaluation of load and muscle fatigue [9] and in sports medicine. They are used in the studies of prosthetic control [8, 12], and even for speech recognition [1]. The authors intend to make use of parameterized EMG signals to stimulate the movements of a humanoid robot as well as for the recognition of muscle dysfunction in pathological speech articulation and for the application of biofeedback to correct speech defects
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