A Parallel Network Architecture for Automatic Slip Detection Task in Human-Centered Footwear Test
Bibliographic record
Abstract
Slips and falls are significant public health concerns, making slip-resistant footwear an essential component of prevention. This study aimed to develop an automated slip detection algorithm, designed to replace human observers in human-centered footwear testing protocols and provide a proof of concept for future real-world assessment. A parallel network architecture was proposed to classify videos into slip and no slip events, consisting of two distinct models: Inflated 3D convolutional neural networks (I3D) and Multiscale Vision Transformer (MViT). The two models operate in parallel, independently processing the input video data to capture diverse temporal and spatial features. To optimize the network, predictions from each model are aggregated by fusing their losses using a weighted averaging mechanism, ensuring a balanced contribution during training. The proposed approach was evaluated using two cross-validation techniques: 5-fold and Leave-One-Subject-Out (LOSO) to assess both overall performance and generalizability across subjects. Our proposed parallel network achieved the highest performance in both cross-validation techniques, with an accuracy of 94.63% ± 1.39% (5-fold) and 93.37% ± 3.02% (LOSO), and an F1 score of 94.33% ± 1.46% (5-fold) and 93.02% ± 2.93% (LOSO) on unseen test data. Notably, it outperformed standalone I3D and MViT models by approximately 7% and 4%, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".