Biomechanical Effects of Mobile Computer Location in a Vehicle Cab
Bibliographic record
Abstract
OBJECTIVE: The objective of this research is to determine the best location to place a conventional mobile computer supported by a commercially available mount in a light truck cab. BACKGROUND: U.S. and Canadian electric utility companies are in the process of integrating mobile computers into their fleet vehicle cabs. There are no publications on the effect of mobile computer location in a vehicle cab on biomechanical loading, performance, and subjective assessment. METHOD: The authors tested four locations of mobile computers in a light truck cab in a laboratory study to determine how location affected muscle activity of the lower back and shoulders; joint angles of the shoulders, elbows, and wrist; user performance; and subjective assessment. A total of 22 participants were tested in this study. RESULTS: Placing the mobile computer closer to the steering wheel reduced low back and shoulder muscle activity. Joint angles of the shoulders, elbows, and wrists were also closer to neutral angle. Biomechanical modeling revealed substantially less spinal compression and trunk muscle force. In general, there were no practical differences in performance between the locations. Subjective assessment indicated that users preferred the mobile computer to be as close as possible to the steering wheel. CONCLUSION: Locating the mobile computer close to the steering wheel reduces risk of injuries, such as low back pain and shoulder tendonitis. APPLICATION: Results from the study can guide electric utility companies in the installation of mobile computers into vehicle cabs. Results may also be generalized to other industries that use trucklike vehicles, such as construction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".