Movement Onset Detection Methods: A Comparison Using Force Plate Recordings
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".