Biomechanical and ergonomic assessment of urban transit operators
Bibliographic record
Abstract
BACKGROUND: The prevalence of neck and low back musculoskeletal injuries in transit operators has been shown to be high; with work absences exceeding double the National average. There is a lack of biomechanical data generated from field researches to inform on musculoskeletal risk associated with transportation and driving occupations. Instead there has been a reliance on simulated driving and questionnaire-based research. OBJECTIVES: This study was designed to examine the musculoskeletal and biomechanical stresses experienced by urban bus drivers. The main objective was to obtain a baseline understanding of sitting posture, muscle activiation and subjective ratings of stress during regular driving tasks. PARTICIPANTS: Fifteen urban city bus drivers were recruited for this study. METHODS: Bus drivers drove the same 65-minute bus route once, at the same time of day, in the same preselected bus. Wireless surface electromyography monitored muscular activity of the drivers' neck, upper trapezius, and erector spinae and video analysis and seat pressure mapping was used to monitor changes in driving posture. A health and lifestyle questionnaire was administered to record specific neck and back pain experienced by drivers as well as to provide lifestyle habits. RESULTS: Drivers were found to exhibit non-neutral postures for less than 30% of the time while conducting routine tasks of turning and stopping for passenger loading and unloading. The neck posture was the greatest concern in terms of non-neutral posture and this was supported by the higher muscle activation to the neck musculature. There was significant posture adjustment made during the one-hour driving period with the adjustments increasing with driving time. CONCLUSION: Activities associated with bus driving appear to require the use of non-neutral postures an increase in muscle activation. Significant postural adjustments were needed over the one-hour observation period suggesting that the musculoskeletal demands may increase over a regular 12-hour shift.
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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) | 0.002 | 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".