Affiliations: [a] Sports Medicine Assessment Research and Testing
Laboratory, George Mason University, Manassas, VA, USA | [b] Applied Neuromechanics Research Laboratory, University
of North Carolina at Greensboro, Greensboro, NC, USA
Note: [] Address for correspondence: Jatin P. Ambegaonkar, PhD ATC OT
CSCS, George Mason University, 208-C, Bull Run Hall, MS 4E5, 10900, University
Boulevard, Manassas, VA 20110, USA. Tel.: +1 703 993 2123; Fax: +1 703 993
2025; E-mail: jambegao@gmu.edu
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.
Keywords: Surface electromyography, root mean square, signal processing, drop jumps
