Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Introduction

Occupational Noise Induced Hearing Loss (ONIHL) is one of the most prevalent conditions among mine workers globally. This reality is due to mine workers being exposed to noise produced by heavy machinery, rock drilling, blasting, and so on. This condition can be compounded by the fact that mine workers often work in confined workspaces for extended periods of time, where little to no attenuation of noise occurs. The objective of this research work is to present a preliminary study of the development of a hearing loss, early monitoring system for mine workers.

Methodology

The system consists of a smart watch and smart hearing muff equipped with sound sensors which collect noise intensity levels and the frequency of exposure. The collected information is transferred to a database where machine learning algorithms namely the logistic regression, support vector machines, decision tree and Random Forest Classifier are used to classify and cluster it into levels of priority. Feedback is then sent from the database to a mine worker smart watch based on priority level. In cases where the priority level is extreme, indicating high levels of noise, the smart watch vibrates to alert the miner. The developed system was tested in a mock mine environment consisting of a 67 metres tunnel located in the basement of a building whose roof top represents the "surface" of a mine. The mock-mine shape, size of the tunnel, steel-support infrastructure, and ventilation system are analogous to deep hard-rock mine. The wireless channel propagation of the mock-mine is statistically characterized in 2.4-2.5 GHz frequency band. Actual underground mine material was used to build the mock mine to ensure it mimics a real mine as close as possible. The system was tested by 50 participants both male and female ranging from ages of 18 to 60 years.

Results and discussion

Preliminary results of the system show decision tree had the highest accuracy compared to the other algorithms used. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. The system also showed a good response level in terms of detection of noise input levels of exposure, transmission of the information to the data base and communication of recommendations to the miner. The developed system is still undergoing further refinements and testing prior to being tested in an actual mine.

Occupational Hearing Loss for Platinum Miners in South Africa: A Case Study of Data Sharing Practices and Ethical Challenges in the Mining Industry

BACKGROUND

The relevant legislation ensures confidentiality and has paved the way for data handling and sharing. However, the industry remains uncertain regarding big data handling and sharing practices for improved healthcare delivery and medical research.

METHODS

A semi-qualitative cross-sectional study was used which entailed analysing miners' personal health records from 2014 to 2018. Data were accessed from the audiometry medical surveillance database (n = 480), the hearing screening database (n = 24,321), and the occupational hygiene database (n = 15,769). Ethical principles were applied to demonstrate big data protection and sharing.

RESULTS

Some audiometry screening and occupational hygiene records were incomplete and/or inaccurate (N = 4675). The database containing medical disease and treatment records could not be accessed. Ethical challenges included a lack of clarity regarding permission rights when sharing big data, and no policy governing the divulgence of miners' personal and medical records for research.

CONCLUSION

This case study illustrates how research can be effectively, although not maliciously, obstructed by the strict protection of employee medical data. Clearly communicated company policies should be developed for the sharing of workers' records in the mining industry to improve HCPs.

South African hearing conservation programmes in the context of tele-audiology: A scoping review

BACKGROUND

The limited involvement of audiologists in occupational noise-induced hearing loss (ONIHL) management through hearing conservation programmes (HCPs) is a global issue. In low- and middle-income (LAMI) countries such as South Africa, this is also exacerbated by demand versus capacity challenges. Tele-audiology is an option requiring serious deliberation by the audiology community within HCPs in LAMI contexts.

OBJECTIVES

This scoping review explores if tele-audiology has a potential value in HCPs and reviews what has been documented in the literature on the use of tele-audiology in HCPs.

METHOD

A scoping review was conducted using the Arksey and O'Malley's framework. A search was conducted in five electronic bibliographic databases including Science Direct, PubMed, Scopus Medline, ProQuest and Google Scholar and the grey literature to identify publications presenting considerations around tele-audiology in the implementation of HCPs.

RESULTS

Findings revealed significant dearth of evidence specific to the use or application of tele-audiology in ONIHL and/or HCPs both within the African context and internationally, despite the purported potential benefit of this service delivery model, particularly in resource-constrained contexts such as LAMI countries. Of the publications deemed potentially relevant to this scoping review, none were found that specifically investigated or addressed the use of tele-audiology in ONIHL or HCPs as their main objective. Nuanced analysis of publications revealed that in the last decade, indication for potential growth in the use of tele-audiology within occupational audiology is indicated.

CONCLUSION

Because of the significant demand versus capacity challenges in LAMI countries, and because of the need for scaling up audiology professionals' management of HCPs, careful consideration of teleaudiology as a platform to deliver services in these contexts is required.