Movement Onset Detection Methods: A Comparison Using Force Plate Recordings

Abstract

Computational approaches for movement onset detection can standardize and automate analyses to improve repeatability, accessibility, and time efficiency. With the increasing interest in assessing time-varying biomechanical signals such as force–time recordings, there remains a need to investigate the recently adopted 5 times the standard deviation (5 × SD) threshold method. In addition, other employed methods and their variations such as the reverse scanning and first derivative methods have been scarcely evaluated. The aim of this study was to compare the 5 × SD threshold method, 3 variations of the reverse scanning method, and 5 variations of the first derivative method against manually selected onsets, in the countermovement jump and squat. Limits of agreement with respect to onsets, manually selected from unfiltered data, were best for the first derivative method using a 10-Hz low-pass filter (limits of agreement: −0.02 to 0.05 s and −0.07 to 0.11 s for the countermovement jump and squat, respectively). Thus, even when the onset of unfiltered data is of primary interest, filtering before calculating the first derivative is necessary as it reduces the amplification of high frequencies. The first derivative approach is also less susceptible to inherent variation during the quiet phase prior to the onset compared to the other approaches investigated.