Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force-varying contractions

Numerous studies have investigated the relationship between surface electromyogram (EMG) and torque exerted about a joint. Most studies have used conventional EMG amplitude (EMGamp) processing, such as rectification followed by low-pass filtering, to pre-process the EMG before relating it to torque. Recently, advanced EMGamp processors that incorporate signal whitening and multiple-channel combination have been shown to significantly improve EMGamp processing. In this study, we compared the performance of EMGamp–torque estimators with and without these advanced EMGamp processors. Fifteen subjects produced constant-posture, non-fatiguing, force-varying contractions about the elbow while torque and biceps/triceps EMG were recorded. EMGamp was related to torque using a linear FIR model. Both whitening and multiple-channel combination reduced EMG–torque errors and their combination provided an additive benefit. Using a 15th-order linear FIR model, EMG–torque errors with a four-channel, whitened processor averaged 7.3% of maximum voluntary contraction (MVC) (or 78% of variance accounted for). By comparison, the equivalent single-channel, unwhitened (conventional) processor produced an average error of 9.9% of MVC (variance accounted for of 55%). In addition, the study 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 occurs when the torque data are sampled at the same rate as the EMG data. The problem is corrected by decimating the EMGamp prior to relating it to joint torque, in our case to an effective sampling rate of 40.96 Hz.