Lower extremity preventive measures for slips – joint moments and myoelectric analysis
Bibliographic record
Abstract
This study investigated the lower extremity preventive measures for slips on simulated slippery surfaces in construction worksites. A total of 15 harnessed Chinese males walked without slips on a 5-m walkway in 16 simulated conditions 10 times at their natural cadence. Joint moments were calculated from kinetics, kinematics and anthropometric data recorded from a force plate, a Novel Pedar system and a motion analysis system. Data were evaluated from footstrike to mid-stance at 10%-stance intervals. Electromyography signals from tibialis anterior, gastrocnemius, rectus femoris and biceps femoris in one stride were evaluated in four phases, including early/late stance and swing. Results showed that lower extremity preventive measures for slips included prolonged ankle plantar flexion moments from 25% to 92% stance, increased ankle plantar flexion moments from 30% stance to mid-stance, and diminished knee extension moments from 10% to 30% stance. Higher activity of rectus femoris and gastrocnemius were found in the late stance and the swing phase respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".