Dataset for "Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms"

Published: 22 January 2021| Version 1 | DOI: 10.17632/5hbfnnjfff.1


The simulated SEMG(i) presents variable quantities of events, smooth changes in amplitude with marked on-off timing, and variable SNR. Globally, 1.200 SEMG were synthesized using sets of uniformly distributed random values for σn (single events durations in range 50 to 150 ms), αn (1 to 2.5), and SNR (0 to 39 dB). All simulated components – r(i), gn(i), sn(i), and e(i) – were stored as ASCII files in a database and are available upon request. Notice that the SNR does not depend on the duration of the simulated contraction event. The SNR ratio per event was calculated as the 10log10 (σ2y /σ2e), where σ2y and σ2e represent calculated variances of y(i) and e(i), respectively. All signals were simulated with a sampling frequency of 1.0 kHz. No additional signal processing was performed in the SEMG(i).


Steps to reproduce

Details: y(i)= noiseless SEMG for the ith sample (i = 1,2,…,2000) e(i) = background noise modeled as a bandlimited (80-120 Hz, 1st order Butterworth filter) pseudorandom pattern r(i) = isometric contraction also modeled as a bandlimited Gaussian-distributed pseudorandom pattern with standard deviation σr gn(I) = profiles of muscle activity modeled as n = 1, 2, 3 Gaussian functions with standard deviations σn and random amplitude factors in range [0.1; 1.0] sn(i) = on-off periods modeled as n square patterns with time support αn and unitary amplitude.


Centro Universitario Augusto Motta


Biomechanics, Electromyography, Biomedical Signal Processing