title: Estimation and application of EMG amplitude during dynamic contractions.
abstract: The sections above have described an EMG amplitude estimator and an initial application of this estimator to the EMG-torque problem. The amplitude estimator consists of six stages. In the first stage, motion artifact and power-line interference are attenuated. Motion artifact is typically removed with a highpass filter. Elimination of power-line noise is more difficult. Commercial systems tend to use notch filters, accepting the concomitant loss of "true" signal power in exchange for simplicity and robustness. Adaptive methods may be preferable, however, to preserve more "true" signal power. In stage two, the signal is whitened. One fixed whitening technique and two adaptive whitening methods were described. For low-amplitude levels, the adaptive whitening technique that includes adaptive noise cancellation may be necessary. In stage three, multiple EMG channels (all overlying the same muscle) are combined. For most applications, simple gain normalization is all that is required. Stage four rectifies the signal and then applies the power law required to demodulate the signal. In stage six, the inverse of the power law is applied to relinearize the signal. Direct comparison of MAV (first power) to RMS (second power) processing demonstrates little difference between the two. Therefore, unless there is reason to believe that the EMG density departs strongly from that found in the existing studies, RMS and MAV processing are essentially identical. In stage five, the demodulated samples are averaged across all channels and then smoothed (time averaged) to reduce the variance of the amplitude estimate, but at the expense of increasing the bias. For best performance, the window length that best trades off variance and bias error is selected. The advanced EMG processing was next applied to dynamic EMG-torque estimation about the elbow joint. Results showed that improved EMG amplitude estimates led to improved EMG-torque estimates. An initial comparison of different system-identification techniques and model orders was reported. It is expected that these advanced processing and identification algorithms will also improve performance in other EMG applications, including myoelectrically controlled prostheses, biofeedback, and ergonomic assessment.
authors.affiliation: Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Worcester Polytechnic Institute. ted@wpi.edu
authors.forename: E A
authors.initials: EA
authors.lastname: Clancy
authors.forename: S
authors.initials: S
authors.lastname: Bouchard
authors.forename: D
authors.initials: D
authors.lastname: Rancourt
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001676:Biofeedback, Psychology / Q000502:physiology; D004550:Elbow / Q000502:physiology; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D019415:Torque