Lower-Extremity Gait Kinematics on Slippery Surfaces in Construction Worksites
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the lower-extremity kinematics when walking on potentially slippery surfaces in simulated construction worksite environments. METHODS: A survey was conducted to select two types of footwear, two floorings, and four contaminants to represent the local construction worksite environments, making 16 simulated conditions. A mechanical slip-resistance test was conducted to evaluate the slipping potential of the 16 conditions by the value of the dynamic coefficient of friction. The 16 conditions were classified into three groups by slipping potential. Fifteen harnessed Chinese male subjects were instructed to walk and avoid slips on each of the 16 simulated 5-m walkways 10 times at their natural cadence. The movements in the sagittal plane were videotaped, digitized, and analyzed by a motion analysis system. Gait pattern parameters were obtained. Lower-extremity kinematic data were time-normalized from foot strike (0% stance) to take-off (100% stance) and were extracted from foot strike to midstance (50% stance) at 10% stance intervals. RESULTS: ANOVA showed that with increased slipping potential, changes in gait pattern parameters included increased stance and stride time, shortened stride length, decreased propagation speed, and gentle heel strike. In lower-extremity kinematic parameters, significant differences were found mainly at the ankle joint rather than the knee joint. CONCLUSION: Strategies to prevent slips included increased stance and stride time, shortened stride length, decreased propagation speed, and gentle heel strike. The ankle joint played the most important role in adaptation strategy. Such strategy included reducing range of motion, maintaining a stiff joint, and achieving flatfoot landing or a plantarflexed ankle joint during the first 10% stance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".