Covariate Analysis for Footstep Recognition Using Unsupervised Hierarchical Clustering
Bibliographic record
Abstract
Footstep recognition is an emerging biometric that identifies or verifies users based on footstep pressure patterns obtained while walking. However, the impact of covariates on footstep recordings is not well understood, unlike more established biometric traits such as fingerprint and facial recognition. Therefore, this study used unsupervised hierarchical clustering (HCA) to examine the internal and external covariate influence on spatial and temporal footstep features of twenty individuals. Using 22 cluster validity indices, a robust HCA technique identified two distinct clusters in spatial representations (i.e., peak pressure images) and temporal representations (i.e., ground reaction force (GRF) and center of pressure (COP) time series) of the gait patterns. The clusters determined in both feature domains were distinguishable by body weight, age, race, and shoe type. Interestingly, trends related to sex and walking speed existed only in the temporal domain. These findings suggest dual implications for footstep biometric systems, which may leverage covariate information as soft biometrics to improve user recognition, or require mitigation to limit model bias and improve generalization to new users and conditions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".