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Investigation of variables for monitoring muscle fatigue in EMG recordings - Essay Example

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Estimation of EMG Variables during Voluntary Isometric Contractions List of Symbols  Deterministic signal  Fourier Transform  Number of symbols  Energy Spectral Density xx Autocorrelation Function  Time Constant  Periodogram (Power Spectral Density) BT Blackman Turkey (Power Spectral Density)  i Signal Samples n Noise Input Sequence  ,  Past Outputs, Past Inputs  Power Spectrum  i Instantaneous Frequency  Signal’s Phase  Autoregressive Model Order Abstract: Many techniques have been developed in spectral analysis and their application in different fields including detection EMG signals…
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Investigation of variables for monitoring muscle fatigue in EMG recordings
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Download file to see previous pages Here the assessments are based on the analysis of signals produced during the activity of the muscle; contraction or relaxation. There are many algorithms used available for estimating the amplitude, frequency variables and conduction velocity of the surface EMG signal detected during voluntary contractions. Here the most widely accepted algorithms are studied and its advantages and drawbacks are outlined. Here the focus is made on the frequency analysis of surface EMG signal. The results obtained during the frequency analysis of surface EMG signifies the behavior of test signals based on mean and median frequency variables acquired using PSD estimation methods, namely Autoregressive and Periodogram. Here an electromyograph is record the signals generated during the electrical or neurological excitation of the muscle cells and these recorded signals are then subjected to spectral analysis. The frequency responses of the signals are considered assessments are made accordingly. “Surface EMG signals are decomposed into 32-subbands by using a cosine modulated filter bank. Both the instantaneous mean frequency (IMF) and the instantaneous amplitude (IA) are estimated from the sub bands and are used as indicators of muscle fatigue” (McGoron, et al, 2009, P. 267). Table of Contents 1. Introduction 7 1.1. Power spectral density (PSD) 7 1.2. Spectrum estimation techniques 8 1.2.1. Fast Fourier transforms 9 1.2.2. Blackman turkey approach 10 1.2.3. Autoregressive method 11 1.2.4. Auto regressive moving average model 12 1.3 EMG 13 1.4. Application of PSD in EMG 13 2. Literature on Application 14 3. Discussion 17 3.1 deterministic function 18 3.2. Stochastic function 18 3.3 induction of autoregressive approach 19 4. Results 31 4.1. Deterministic 31 4.1.1. Test signal 1 31 4.1.2 Test signal 2 37 4.2. Stochastic 46 4.2.1. Test signal 4 46 4.2.2 Test signal 5 53 4.2.3 Test signal 6 57 5. Advantages of EMG and PSD 63 5. Conclusion 64 1. Introduction: At the present era medical literature considers human muscle fatigue as a physical phenomenon that starts during the onset of a muscle contraction and develops progressively until the muscle cannot generate force, the maximum voluntary contraction (MVC) reduces during muscle fatigue. Here spectral analysis is used to examine the nature of signals recorded in the electromyograph due the electrical activity of the muscle fibers. 1.1 power spectral density (PSD): Power spectral density (PSD) is the frequency response of a random or periodic signal and indicates where power is distributed as a function of frequency. PSD is deterministic and for certain types of random signals independent of time. It shows the strength and weakness of the signals at different frequency levels. The frequency level of the signal is drawn against time to get the spectra. Waveform can be represented by a plot of amplitude versus frequency together with a plot of phase versus frequency, respectively known as the amplitude and phase spectra. Amplitude and phase ...Download file to see next pagesRead More
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