Design and Evaluation of a Suspension Seat to Reduce Vibration Exposure of Subway Operators: A Case Study
Bibliographic record
Abstract
Subway operators have complained about discomfort caused by whole-body vibration. To address this problem, a suspension seat with extensive ergonomic features has been adapted to the confined space of the subway operator cab. The suspension was modified from an existing suspension in order to reduce the dominant frequency of the subway vertical vibration (2.4 Hz). The suspension seat has been extensively tested on a vertical hydraulic shaker. These tests have shown that the SEAT value was lower for a higher vibration level, for higher subject weight, and for the suspension adjusted at median height. The seat also produces a lower SEAT value when there was a predominance of the 6 Hz vibration component. The horizontal seat adjustments had no influence on the suspension SEAT value. Removing the suspension damper also decreases the SEAT value for all the tested configurations. The final version of the suspension seat prototype was validated during normal subway operation with 19 different operators having weight in the 5th, 50th and 95th percentile of the operator population. Accelerations were measured with triaxial accelerometers at the seat cushion, above the suspension and on the floor. In addition to the vibration measurements, each operator was asked about his perceived discomfort from vibration exposure. Globally, the suspension seat attenuated the vertical vibration (SEAT values from 0.86 to 0.99), but discomfort due to amplification of the 2.4 Hz component occurred when the suspension height was adjusted at the minimum, even when the global weighted acceleration was lower (SEAT value < 1). These results suggest that in order to reduce the discomfort caused by whole-body vibration, the transmissibility of the seat should also be considered, in particular when there is a dominant frequency in the vibration spectra.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".