Real‐time vibration monitoring and analysis of agricultural tractor drivers using an IoT‐based system
Bibliographic record
Abstract
Abstract Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore‐and‐aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real‐field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat‐to‐head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x‐, y‐, and z‐axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID‐19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x‐, y‐, and z‐axes. The vibration energy was found to be dominant along the z‐axis than the x‐ and y‐axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x‐, y‐, and z‐axes depicted the low‐frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y‐ and z‐axes when compared with that along the x‐axis. The frequency range of 4–7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.
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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 | 0.000 |
| 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".