Optimal Electromyogram Modeling and Processing During Active Contractions and Rest

Wang, Haopeng

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Optimal Electromyogram Modeling and Processing During Active Contractions and Rest

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The standard deviation of surface EMG (EMGσ) is often related to muscle force; the accuracy of EMGσ estimation is valuable for many application areas such as clinical biomechanics, prostheses control and sports medicine. Numerous researchers have developed methods to optimize EMGσ estimation. Whitening the EMG signal has been proved to improve the statistical efficiency of EMGσ estimation, but conventional linear whitening filters fail at low contraction level. An adaptive whitening filter was proposed by Clancy and Farry[14]. This technique has a better performance than prior whitening methods, however, the adaptive whitening filter needs to be calibrated each time electrodes are applied, which increase the complexity of the implementation. We designed a universal whitening filter which can omit most calibration steps for the adaptive whitening filter in future use. We used the ensemble mean of the power spectrum of 512 EMG recordings to form a general shape of a fixed whitening filter that can applied on any EMG signal. The test error on an EMG-torque task based on universal whitening over 512 subjects had a mean error of 4.80% maximum voluntary contraction (MVC) and standard deviation (std) of 2.03% MVC, compared with an original adaptive whitening filter error of 4.84±1.98% MVC.\n\nAdditionally, the rest contraction modeling hasn’t received enough attention. Existing RMS estimates of EMGσ subtract noise in either the amplitude or power domain. These procedures have never been modeled analytically. We show that power domain noise subtraction is optimal. But rest contractions which are processed using power domain noise subtraction only estimate a zero-valued EMGσ approximately 50% of the time, which is undesirable in prosthesis-control. The prosthesis has a 50% possibility to slowly drift based on the current RMS estimator. We propose a new estimator to improve the zero-amplitude estimation probability during rest. We used 512 rest contraction recordings to validate the probability distribution of rest EMG signal showing that it only has 1.6% difference compared with Gaussian distribution. We also evaluated the percent of zero-valued EMGσ estimates using power domain noise subtraction and our new estimator, matching experimental findings to the theoretic predictions.\nAdditionally, the rest contraction modeling hasn't received enough attention. Existing RMS estimates of EMGσ subtract noise in either the amplitude or power domain. These procedures have never been modeled analytically. We show that power domain noise subtraction is optimal. But rest contractions which are processed using power domain noise subtraction only estimate a zero-valued EMGσ approximately 50% of the time, which is undesirable in prosthesis-control. The prosthesis has a 50% possibility to slowly drift based on the current RMS estimator. We propose a new estimator to improve the zero-amplitude estimation probability during rest. We used 512 rest contraction recordings to validate the probability distribution of rest EMG signal showing that it only has 1.6% difference compared with Gaussian distribution. We also evaluated the percent of zero-valued EMGσ estimates using power domain noise subtraction and our new estimator, matching experimental findings to the theoretic predictions.\n

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