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.

A proposed artificial intelligence-based real-time speech-to-text to sign language translator for South African official languages for the COVID-19 era and beyond: In pursuit of solutions for the hearing impaired.

BACKGROUND:  The emergence of the coronavirus disease 2019 (COVID-19) pandemic has resulted in communication being heightened as one of the critical aspects in the implementation of interventions. Delays in the relaying of vital information by policymakers have the potential to be detrimental, especially for the hearing impaired. OBJECTIVES:  This study aims to conduct a scoping review on the application of artificial intelligence (AI) for real-time speech-to-text to sign language translation and consequently propose an AI-based real-time translation solution for South African languages from speech-to-text to sign language. METHODS:  Electronic bibliographic databases including ScienceDirect, PubMed, Scopus, MEDLINE and ProQuest were searched to identify peer-reviewed publications published in English between 2019 and 2021 that provided evidence on AI-based real-time speech-to-text to sign language translation as a solution for the hearing impaired. This review was done as a precursor to the proposed real-time South African translator. RESULTS:  The review revealed a dearth of evidence on the adoption and/or maximisation of AI and machine learning (ML) as possible solutions for the hearing impaired. There is a clear lag in clinical utilisation and investigation of these technological advances, particularly in the African continent. CONCLUSION:  Assistive technology that caters specifically for the South African community is essential to ensuring a two-way communication between individuals who can hear clearly and individuals with hearing impairments, thus the proposed solution presented in this article.

Application of machine learning approaches to analyse student success for contact learning and emergency remote teaching and learning during the COVID-19 era in speech-language pathology and audiology.

BACKGROUND:  The onset of the COVID-19 pandemic across the globe resulted in countries taking several measures to curb the spread of the disease. One of the measures taken was the locking down of countries, which entailed restriction of movement both locally and internationally. To ensure continuation of the academic year, emergency remote teaching and learning (ERTL) was launched by several institutions of higher learning in South Africa, where the norm was previously face-to-face or contact teaching and learning. The impact of this change is not known for the speech-language pathology and audiology (SLPA) students. This motivated this study. OBJECTIVES:  This study aimed to evaluate the impact of the COVID-19 pandemic on SLPA undergraduate students during face-to-face teaching and learning, ERTL and transitioning towards hybrid teaching and learning. METHOD:  Using course marks for SLPA undergraduate students, K means clustering and Random Forest classification were used to analyse students' performance and to detect patterns between students' performance and the attributes that impact student performance. RESULTS:  Analysis of the data set indicated that funding is one of the main attributes that contributed significantly to students' performance; thus, it became one of the priority features in 2020 and 2021 during COVID-19. CONCLUSION:  The clusters of students obtained during the analysis and their attributes can be used in identification of students that are at risk of not completing their studies in the minimum required time and early interventions can be provided to the students.

Engineering noise control for mines: Lessons from the world.

OBJECTIVE: The main objective of this article was to present some of the engineering noise control methods that are currently being used across the world in the mining industry, while at the same time interrogating noise control measures that could be applied to effectively reduce noise emissions from the equipment utilised within South African mines. BACKGROUND: A brief summary of the hearing conservation programmes used in South Africa is presented. Summarised research on the use of engineering noise control in South Africa is also presented, with an overview of the various engineering noise control methods applied across the world in dealing with occupational noise. METHOD: For illustrative purposes, case studies were used to show how engineering noise controls could be used to reduce the noise levels and risks within this context. RESULTS: Some of the case studies used have cited a reduction in the noise intensity emitted by machinery from a range of 93 dBA - 104 dBA to a range of 90 dBA - 94 dBA, demonstrating quite a significant reduction in the noise emission of the equipment. This article further provides recommendations on how South African mines could contextualise these methods. CONCLUSION: One of the key recommendations is encouraging the South African mining industry towards the documenting and publishing of those engineering noise control methodologies that have proven to be effective for shared best practice. A need was identified for extensive research to be conducted and documented evidence to be made available to assist the South African mining industry with locating and assessing current engineering controls available in South Africa. Machines and processes that require noise control should be identified and, lastly, the current barriers to the use of engineering noise control methodologies should be identified, with the main goals of finding ways to overcome the noise challenges in the mines.