Detection of Stride Time and Stance Phase Ratio from Accelerometer Data for Gait Analysis
Bibliographic record
Abstract
Stride time and stance phase ratio are supportive biomarkers used in the diagnosis and treatment of gait disorders and are currently frequently used in research studies. In this study, the 3-axis accelerometer signal, taken from the foot, was denoised by a low-pass FIR (finite impulse response) filter. By using the fundamental frequency analysis the dominant frequency was found and with that frequency an optimal length for a window to be shifted across the whole signal for further purposes. And the turning region was extracted by using the Pearson correlation coefficient with the segments that overlapped by shifting the selected window over the whole signal, after getting the walking segments the stride time parameter is calculated by using a simple peak-picking algorithm. The stance and swing periods of the pseudo-steps, which emerged as a result of the double step time calculation algorithm, were found with the dynamic time warping method, and the ratio of the stance phase in a step to the whole step was calculated as a percentage. The results found were compared with the results of the APDM system, and the mean absolute error rate was calculated as 0.029 s for the stride time and 0.0084 for the stance phase ratio.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".