The Effects of Filter Cutoff Frequency on Musculoskeletal Simulations of High-Impact Movements
Bibliographic record
Abstract
Estimation of muscle forces through musculoskeletal simulation is important in understanding human movement and injury. Unmatched filter frequencies used to low-pass filter marker and force platform data can create artifacts during inverse dynamics analysis, but their effects on muscle force calculations are unknown. The objective of this study was to determine the effects of filter cutoff frequency on simulation parameters and magnitudes of lower-extremity muscle and resultant joint contact forces during a high-impact maneuver. Eight participants performed a single-leg jump landing. Kinematics was captured with a 3D motion capture system, and ground reaction forces were recorded with a force platform. The marker and force platform data were filtered using 2 matched filter frequencies (10-10 Hz and 15-15 Hz) and 2 unmatched filter frequencies (10-50 Hz and 15-50 Hz). Musculoskeletal simulations using computed muscle control were performed in OpenSim. The results revealed significantly higher peak quadriceps (13%), hamstrings (48%), and gastrocnemius forces (69%) in the unmatched (10-50 Hz and 15-50 Hz) conditions than in the matched (10-10 Hz and 15-15 Hz) conditions (P < .05). Resultant joint contact forces and reserve (nonphysiologic) moments were similarly larger in the unmatched filter categories (P < .05). This study demonstrated that artifacts created from filtering with unmatched filter cutoffs result in altered muscle forces and dynamics that are not physiologic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".