Electromyogram amplitude estimation with adaptive smoothing window length

Typical electromyogram (EMG) amplitude estimators use a fixed window length for smoothing the amplitude estimate: When the EMG amplitude is dynamic, p revious research suggests that varying the smoothing length as a function o f time may improve amplitude estimation, This paper develops optimal time-v arying selection of the smoothing window length using a stochastic model of the EMG signal. Optimal selection is a function of the EMG amplitude and i ts derivatives, Simulation studies, in which EMG amplitude was changed rand omly, found that the "best" adaptive filter performed as well as the "best" fixed-length filter. Experimental studies found the advantages of the adap tive processor to be situation dependent Subjects used real-time EMG amplit ude estimates to track a randomly-moving target. Perhaps due to task diffic ulty, no differences in adaptive versus fixed-length processors were observ ed when the target speed was fast, When the target speed was slow, the expe rimental results mere consistent with the simulation predictions. When the target moved between two constant levels, the adaptive processor responded rapidly to the target level transitions and had low variance while the targ et dwelled on a level.