The NACOB multi-surface walking dataset
Bibliographic record
Abstract
Walking is a fundamental aspect of human movement, and understanding how irregular surfaces impact gait is crucial. Existing gait research often relies on laboratory settings with ideal surfaces, limiting the applicability of findings to real-world scenarios. While some irregular surface datasets exist, they are often small or lack biomechanical gait data. In this paper, we introduce a new irregular surface dataset with 134 participants walking on surfaces of varying irregularity, equipped with inertial measurement unit (IMU) sensors on the trunk and lower right limb (foot, shank, and thigh). Collected during the North American Congress on Biomechanics conference in 2022, the dataset aims to provide a valuable resource for studying biomechanical adaptations to irregular surfaces. We provide the detailed experimental protocol, as well as a technical validation in which we developed a machine learning model to predict the walking surface. The resulting model achieved an accuracy score of 95.8%, demonstrating the discriminating biomechanical characteristics of the dataset's irregular surface gait data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".