Spatiotemporal walking performance in different settings: effects of walking speed and sex
Bibliographic record
Abstract
Background: Understanding the factors that influence walking is important as quantitative walking assessments have potential to inform health risk assessments. Wearable technology innovation has enabled quantitative walking assessments to be conducted in different settings. Understanding how different settings influence quantitative walking performance is required to better utilize the health-related potential of quantitative walking assessments. Research question: How does spatiotemporal walking performance differ during walking in different settings at different speeds for young adults? Methods: Forty-two young adults [21 male (23 ± 4 years), 21 female (24 ± 5 years)] walked in two laboratory settings (overground, treadmill) and three non-laboratory settings (hallway, indoor open, outdoor pathway) at three self-selected speeds (slow, preferred, fast) following verbal instructions. Six walking trials of each condition (10 m in laboratory overground, 20 m in other settings) were completed. Participants wore 17 inertial sensors (Xsens Awinda, Movella, Henderson, NV) and spatiotemporal parameters were computed from sensor-derived kinematics. Setting × speed × sex repeated measures analysis of variance were used for statistical analysis. Results: Regardless of the speed condition, participants walked faster overground when compared to while on the treadmill and walked faster in the indoor open and outdoor pathway settings when compared to the laboratory overground setting. At slow speeds, participants also walked faster in the hallway when compared to the laboratory overground setting. Females had greater cadence when compared to males, independent of settings and speed conditions. Significance: Particularly at slow speeds, spatiotemporal walking performance was different between the settings, suggesting that setting characteristics such as walkway boundary definition may significantly influence spatiotemporal walking performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".