Analysis of vertical ground reaction force waveforms of trans-tibial prosthesis users
Bibliographic record
Abstract
The development of prosthetic foot components often incorporates mechanical characterization methods that simulate the loading conditions expected in activities of daily living. However, it is recognized that these conditions vary among users and the effect of user variability on mechanical testing results is not currently understood. The objective of this study was to statistically characterize the vertical ground reaction force (vGRF) waveform of prosthesis users to describe its variability in a population by: (1) Applying Principal Component Analysis (PCA) to measured waveforms in a cohort of trans-tibial subjects, and (2) Simulate an expected sample of waveforms for this population using a Monte Carlo method. Phase 1: Three prosthesis users walked on a level walkway at self-selected walking speeds under four prosthetic foot conditions. PCA performed on the vGRF waveforms for affected-limb footsteps resulted in three principal components (PCs) accounting for 91.5% of data variability. Results showed low variability for the same subject using similar designs of prosthetic feet and distinct differences when using a familiar device. Phase 2: Monte Carlo simulation was used to predict a family of 30 vGRF waveforms representative of the sample population. Variability was highest in regions of weight acceptance, mid-stance, and push-off, while lower variability was observed in the transition regions prior to, between, and after these regions. Conclusion: The study supports the use of PCA to describe variability in vGRF waveforms of trans-tibial prosthesis users. The analysis is suitable for Monte Carlo simulation, which showed vGRF waveforms with distinct regions of high and low variability.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".