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.

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.

Background: Simulation plays an important role as an alternative method for training of students, particularly in health education. As a result of coronavirus disease 2019 (COVID-19) restrictions of face-to-face interactions, traditional teaching methods have been disrupted, increasing the need for alternative methods to supplement modes of student clinical training in healthcare programmes.Objectives: The scoping review aimed to determine what has been documented about simulation as a mode of clinical training in healthcare professions (HCPs) in order to guide speech-language pathology and audiology (SLPA) professions during the COVID-19 pandemic and beyond.Method: A scoping review was conducted. Electronic bibliographic databases including Science Direct, PubMed, Scopus MEDLINE, ProQuest, Google Scholar and WorldCat were searched to identify peer reviewed publications, published in English, between January 2011 and December 2021, and related to the use of simulation in HCPs clinical training programmes.Results: A total of 32 articles met the inclusion criteria for this scoping review. Three themes emerged when reviewing the studies: (1) face-to-face simulations as a mode of clinical training, (2) virtual reality simulation and telesimulation as modes of clinical training and (3) simulation as a complementary mode of clinical training. Evidence suggests that whilst simulations are cost-effective, accessible and efficacious as clinical training modes, they need to be combined with other modes of training such as the traditional clinical training to yield better learning outcomes.Conclusions: Current findings highlight the role and value of simulation as a clinical training mode during COVID-19 and beyond. However, there are aspects that need to be considered to ensure that this mode of clinical training is effective, with endorsement and regulations by the SLPA Professional Board of the Health Professions Council of South Africa (HPCSA). Simulations need to be complemented with traditional clinical training methods. In the context of SLPA, particularly in low- and middle-income countries (LMICs), simulation can be used to better prepare students for their clinical placement where clinical training platforms are limited and where simulation combined with teletraining or telesupervision can be utilised to increase access to training.

The South African Journal of Communication Disorders (SAJCD) publishes reports and papers concerned with research, and critically evaluative theoretical, philosophical and conceptual issues dealing with aspects of human communication and its disorders, dysphagia, service provision, training and policy.

Machine learning (ML) and sixth-generation (6G) wireless networks together present a revolutionary paradigm ready to meet hitherto unheard-of needs in connection, scalability, and intelligence. Including physical-layer signal processing, edge computing, resource optimisation, network slicing, and security, this study provides a thorough and cross-layer study of ML integration into 6G systems. Unlike previous works that concentrate on isolated use cases, this paper systematically classifies and evaluates developing techniques such as federated learning (FL), quantum machine learning (QML), swarm intelligence, and explainable AI within the framework of key 6G performance indicators including ultra-low latency, energy efficiency, and privacy. Presenting a thorough taxonomy and comparative benchmarking, we show the operational trade-offs of centralised, distributed, and meta-learning models. We also find important difficulties like model heterogeneity across ultra-dense networks, adversarial vulnerability in FL, and reconfigurable intelligent surfaces (RIS)-assisted privacy risks. Moreover, we describe open research areas and suggest a single orchestration design to support intelligent, scalable, and reliable 6G systems. Researchers and professionals aiming to create strong, ML-driven wireless infrastructures beyond 5G find basic reference in this work.

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.