Milliseconds matter: Biomechanical inverse dynamics analysis is highly sensitive to imperfect data synchronization
Bibliographic record
Abstract
Abstract Inverse dynamics analysis is the primary means of quantifying joint moments and powers from biomechanical data. The data are often combined from force plates, motion capture cameras, and perhaps body-worn inertial sensors, and must be temporally synchronized to avoid potentially large inverse dynamics errors. The principles behind the errors, and the sensitivities for movements such as human walking, have yet to be demonstrated. Here we quantify how inverse dynamics computations of joint moments, powers, and work are highly sensitive to temporal mis-synchronization. We do this with (1) a theoretical examination of inverse dynamics, supported by (2) a simulated multi-body jumping movement, and (3) experimental human walking data. The theoretical analysis shows that root-mean-square errors in joint powers increase linearly with temporal mis-synchronization, and increase more for faster movements. With the other analyses we quantify the specific amount. For example, for human walking at 1.25 m/s, an artificially induced 5 ms lag of force relative to motion resulted in a 29% root-mean-square error of the ankle joint moment. The corresponding error in ankle joint power was 58%, and five times as much for walking at 2.2 m/s. The residual force, a measure of internal inconsistency in the data, increased by almost 1% body weight for each millisecond of mis-synchronization for walking. These sensitivities are relevant because standard experimental equipment usually synchronizes only the recording of data, but not processing latencies internal to equipment, which can and do cause mis-synchronization on the order of ten milliseconds. Biomechanical data should be synchronized to within a few milliseconds, and with respect to physical stimulus, to yield accurate inverse dynamics analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".