Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection
Bibliographic record
Abstract
Gait analysis has been considered in various scenarios to provide information about the ambulatory physical activity. In this regard, studying gait phases can provide valuable information about the quality of gait. Force myography (FMG) techniques have been successfully employed to detect gait events using pattern recognition methods. This paper explores how the accuracy of detecting gait phases is correlated with the parameters of gait and FMG signal. To this end, FMG data were collected from 11 volunteers walking on a treadmill with a custom-designed FMG ankle band. The collected FMG data were classified into four gait phases using Linear Discriminant Analysis (LDA) algorithm. The correlation between the error in classification and the parameters of gait and FMG signal was then investigated. The results show that in comparison with other studied parameters, variations in stride length have the most impact on the accuracy of gait phase classification with a coefficient of determination (R2) of 0.80. Such an effect is more pronounced when signal power-related features, such as root mean square (RMS), are used in the classification algorithm. This study provides insight into the factors affecting the accuracy of FMG-based techniques for gait analysis and is a preliminary step towards developing high performance FMG-based wearable ambulatory activity monitoring systems.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".