Underfoot Pressure-Based Left and Right Foot Classification Algorithms: The Impact of Footwear
Bibliographic record
Abstract
High-resolution plantar pressure recordings have the potential to be used in gait biometrics, biomechanics, and clinical gait analysis. To accurately assess side-specific patterns and asymmetries, it is essential to differentiate between left and right steps, which can be challenging when manual labeling is not feasible and shoe type can vary. This research aimed to create and evaluate the performance of six distinct algorithms (two inspired by existing literature and four novel ones) that take advantage of spatial and temporal features combined with basic decision rules, machine learning, and deep learning to automatically classify left and right footsteps from underfoot pressure recordings, taking into account difficulties associated with footwear variability. A collection of more than 20,000 footsteps from 20 people and 41 different types of shoes was used to assess the six proposed classification algorithms. The results demonstrate that classification techniques based on spatial representations (peak pressure or binary images of footsteps) are more effective than those based on center-of-pressure (COP) time series. The most successful approach, which compares the area of the sole in different parts of the midfoot and forefoot, achieved an accuracy of 99.7% in determining left and right footsteps, with a convolutional neural network (CNN) algorithm at a close second (99.4%). These techniques were found to be robust to many types of footwear and may be valuable for a variety of practical, community-based gait classification tasks.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".