Continuous curve registration as an intertrial gait variability reduction technique
Bibliographic record
Abstract
Timing in peak values shifts slightly between gait trials. When gait data are averaged, part of the standard deviation could be associated with this intertrial variability unless normalization is carried out beforehand. The objective of this study was to determine how continuous curve registration, an alignment technique, can reduce intersubject variability in gait data without altering the original curve characteristics. Gait data were obtained by means of a four-camera high-speed video system synchronized to a force plate. The data for 60 gait trials were collected from 20 young, healthy subjects. Curve registration was applied to hip angular displacement, net moment, and power curves generated in the sagittal plane. Following registration, the peak values increased by an average of 1.2% (0.11 +/- 0.26 degrees) for angular displacement, and by 11.2% (0.11 +/- 0.09 W/kg) for power, while there were no changes for moments. First and second derivatives of the unregistered and registered curves did not display significant differences, and the harmonics were barely affected. Continuous curve registration would thus be an appropriate technique for application prior to any statistical analysis using able-bodied gait patterns.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".