An Empirical Study and Application of Machine Learning to Assess the Impact of Wearable Devices Exposure on Humans in Underground Mine Environment

Abstract

The deployment of wearable wireless devices plays a crucial role in monitoring and enhancing the health and safety of underground miners. The widespread use of these devices has driven a significant expansion in their application, emphasizing the need to prioritize miners’ health and well-being in underground environments. Wearable wireless devices rely on radio-frequency electromagnetic fields (RF-EMF) to transmit and receive signals. The proximity of RF-EMF to the human body in the confined spaces of underground mines has raised significant concerns about po- tential health risks, particularly due to the combination of limited space, elevated temperatures, and prolonged device usage. Therefore, investigating RF-EMF ex- posure in underground mining settings is imperative to assess its potential health effects. Non-Ionizing Radiation Protection (ICNIRP) regulates RF-EMF expo- sure regarding specific absorption rate (SAR) and temperature elevation in the tissues. This study assesses the impact of RF-EMF exposure for a widely adopted wearable device and IoT module, specifically the ESP32-WROOM employing a meandered inverted F-shape antenna (MIFA), within underground mine environ- ments. The SAR was evaluated for MIFA at 2.4 GHz using a 4 mm human model for different mounting positions, distance between MIFA and body, power level, underground mine structure, and rock properties. Additionally, the study explores the temperature elevation in tissues caused by prolonged RF-EMF exposure, tak- ing into account the varying temperatures and exposure durations typical of the underground mine environment. Furthermore, machine learning (ML) and deep learning models were incorporated to predict SAR and temperature elevation in tissues for different variations of associated exposure parameters. The experimen- tal measurements were conducted for wearable devices in a mock mine tunnel using the Cornet ED88TPLUS RF-EMF Meter. The study concluded that (1) SAR remained below ICNIRP limits when the wearable device was positioned 12 mm away from the body, indicating that this distance is safe for minimizing RF- EMF exposure. (2) It was also found that the wrist is the optimal location for mounting the wearable device on a miner’s body, outperforming torso placements in minimizing RF-EMF exposure. (3) The study further observed that RF-EMF exposure levels slightly increase due to the confined space of the underground mine environment, with marginal variation across different simulated rock properties. Therefore, it is recommended that ICNIRP limits be maintained in these settings iii to mitigate RF-EMF exposure. (4) The primary contributor to temperature el- evation in tissues was found to be the ambient temperature of the underground mine. However, the temperature rise due to RF-EMF exposure from the wearable device remained below ICNIRP limits for all simulated scenarios. (5) Additionally, temperature elevation increased exponentially, reaching its peak after one hour of continuous exposure under the same conditions. (6) The study also concluded that ML and deep learning models can predict SAR and temperature elevation, providing a fast and accurate solution for estimating RF-EMF exposure. Integrat- ing these models into wearable devices could enable real-time RF-EMF exposure assessments, eliminating the need for separate equipment to measure the exposure. Further measurements were conducted at various distances from the wearable de- vice, with power densities assessed to confirm that a 10 mm distance provides a secure buffer. Assessing RF-EMF exposure in underground mining environments offers valuable insights for enhancing risk management strategies and minimizing health risks associated with RF-EMF exposure.