Changing filtering parameters affects lower extremity pre-landing muscle activation onset times

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Sandra J. Shultz, Professor and Chair (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Surface electromyography(sEMG) is extensively used to examine muscle activation. Although raw sEMG signals are often filtered using Root-Mean-Square(RMS) algorithms, little agreement exists as to the time window over which signals should be processed. We examined the effects of differing RMS filtering windows on muscle onset times. Fifty-five participants performed 5 drop jumps from a 45 cm box and lateral gastrocnemius(LG), medial and lateral hamstring(MH, LH) and lateral quadriceps(LQ) muscle activity were acquired. Signals were collected at 1000 Hz and RMS filtered using 3 ms, 10 ms, 20 ms and 25 ms windows. Muscle onset times differed by RMS windows for the LG(p= 0.01), MH(p= 0.002), and LH(p= 0.000), but not for the LQ(p=0.14). Pairwise comparisons indicated that LG onsets were earlier with the 3 ms vs. 20 ms window, MH onsets were earlier with the 3 ms vs. 20 ms and 25 ms windows, and LH onsets were earlier with the 3 ms, 10 ms, and 20 ms windows than the 25 ms window. Gastrocnemius and hamstring muscle onset times were substantially earlier when filtering raw sEMG data with 3 ms versus wider RMS windows(> 20 ms) during landing. Changing filtering parameters affects data interpretation when analyzing sEMG data using differing window widths. Additional research should determine optimal RMS window widths that maximize signal fidelity but still retain meaningful time differences.

Additional Information

Isokinetics & Exercise Science, 18(3), 125-132
Language: English
Date: 2010
Surface electromyography, root mean square, signal processing, drop jumps

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