IntroductionOccupational 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.MethodologyThe 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 discussionPreliminary 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.

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基于人工智能的矿山工人职业噪声性听力损失预警系统的开发

简介职业噪声性听力损失 (ONIHL) 是全球矿工中最普遍的病症之一。这一现实是由于矿山工人暴露在重型机械、凿岩、爆破等产生的噪音中。矿工经常在有限的工作空间中长时间工作，噪音几乎没有衰减，这一事实可能会加剧这种情况。这项研究工作的目的是对矿井工人听力损失早期监测系统的开发进行初步研究。方法该系统由智能手表和智能助听器组成，配有声音传感器，可收集噪声强度水平和频率的曝光量。收集到的信息被传输到数据库，其中使用机器学习算法（即逻辑回归、支持向量机、决策树和随机森林分类器）将其分类和聚类为优先级。然后，反馈会根据优先级从数据库发送到矿工智能手表。如果优先级非常高，表明噪音水平很高，智能手表会振动以提醒矿工。所开发的系统在模拟矿山环境中进行了测试，该环境由位于建筑物地下室的 67 米隧道组成，其屋顶代表矿山的“表面”。模拟矿井的形状、隧道尺寸、钢支撑基础设施和通风系统与深部硬岩矿井类似。模拟地雷的无线信道传播在 2.4-2.5 GHz 频段进行统计表征。使用实际的地下矿山材料来建造模拟矿山，以确保它尽可能接近真实的矿山。该系统由 50 名年龄从 18 岁到 60 岁的男性和女性参与者进行了测试。结果和讨论该系统的初步结果表明，与其他使用的算法相比，决策树具有最高的准确性。平均测试准确率为91.25%，平均训练准确率为99.79%。该系统在检测噪声输入暴露水平、将信息传输到数据库以及向矿工传达建议方面也表现出良好的响应水平。在实际矿山测试之前，开发的系统仍在进一步完善和测试。