A number of studies have investigated the relationship between surface electromyogram (EMG) and torque exerted about a joint. The standard deviation of the recorded EMG signal is defined as the EMG amplitude. The EMG amplitude estimation technique varies with the study from conventional type of processing (i.e. rectification followed by low pass filtering) to further addition of different noise rejection and signal-to-noise ratio improvement stages. Advanced EMG amplitude processors developed recently that incorporate signal whitening and multiple-channel combination have been shown to significantly improve amplitude estimation. The main contribution of this research is a comparison of the performance of EMG-torque estimators with and without these advanced EMG amplitude processors. The experimental data are taken from fifteen subjects that produced constant-posture, non-fatiguing, force-varying contractions about the elbow while torque and biceps/triceps EMG were recorded. Utilizing system identification techniques, EMG amplitude was related to torque through a zeros-only (finite impulse response, FIR) model. The incorporation of whitening and multiple-channel combination separately reduced EMG-torque errors and their combination provided a cumulative improvement. A 15th-order linear FIR model provided an average estimation error of 6% of maximum voluntary contraction (or 90% of variance accounted for) when EMG amplitudes were obtained using a four-channel, whitened processor. The equivalent single-channel, unwhitened (conventional) processor produced an average error of 8% of maximum voluntary contraction (variance accounted for of 68%). This study also describes the occurrence of spurious peaks in estimated torque when the torque model is created from data with a sampling rate well above the bandwidth of the torque. This problem is anticipated when the torque data are sampled at the same rate as the EMG data. The problem is resolved by decimating the EMG amplitude prior to relating it to joint torque, in this case to an effective sampling rate of 40.96 Hz.
Worcester Polytechnic Institute
Electrical & Computer Engineering
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Bida, Oljeta, "Influence of Electromyogram (EMG) Amplitude Processing in EMG-Torque Estimation" (2005). Masters Theses (All Theses, All Years). 146.
system sdentification, EMG, optimal sampling rate, linear torque model, EMG-torque model, EMG amplitude, torque, Electromyography, Muscle contraction, Measurement