Combined measurement of noise exposure and induced auditory fatigue using a digital hearing protector
Bibliographic record
Abstract
Despite the efforts to integrate hearing conservation programs in the workplace, noise-induced hearing loss (NIHL) remains the most common and expensive occupational disease. Although industrial workers do wear hearing protection devices (HPD), it is difficult to estimate the effective noise exposure using a personal dosimeter placed on their shoulder, since the residual ambient sound pressure level (SPL) behind the hearing protector is usually unknown. Many factors need to be considered when estimating the effective residual noise exposure, such as the attenuation rating of the hearing protector, the quality of the HPD fit, the duration the protector was removed during noise exposure, as well as how the human auditory mechanisms interact with changes in noise exposure levels. Moreover, hearing assessment with audiometric measurement is often conducted on too long intervals, after the hearing damage might have appeared, and thus do not prevent the occupational hearing loss. There is also no way to ensure that the recommended maximum noise exposure of 90 dBA, based on an 8-hour work shift, in Quebec is suitable for a given individual due to his own susceptibility to NIHL. Besides, recent studies show that global noise exposure levels are not sufficient to explain changes in hearing health and temporal fluctuations in noise in addition to noise spectral balance measurements should be considered in NIHL risk assessment. In order to establish a dose-response relationship between noise exposure and its effects on hearing health, an approach is suggested to continuously monitor the workers’ otoacoustic emissions (OAE) levels during their work while recording the noise exposure. The objectives of the thesis project are first to assess the individual’s hearing health with OAEs in a simulated noisy environment to validate the first prototype of the developed OAE measurement system. Then, develop a method to monitor the residual ambient sound pressure level behind the earplug and the hearing protection level in situ using the designed portable system. This finally allows to estimate the dose-response relationship based on the effective noise exposure and OAE levels. The results obtained in this thesis show that it is possible to detect changes in hearing health during moderate noise exposure levels. Hearing conservation methods are therefore proposed to reduce the risk of NIHL.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".