## 2 Related Works  
The interaction between electromagnetic fields (EMFs) and biological systems has been widely studied. Understanding the internal distribution of electric fields, currents, and the rate of energy deposition (i.e., the \"dose\" of the EMF) is essential for describing this interaction [34]. Computational models have become a key tool in the bio-electromagnetism research, allowing the researchers to quantify EM field distributions and their magnitudes in human tissues. Several studies have focused on the implementation of computational electromagnetic techniques to assess field intensities induced by EMF-based biomedical applications [35; 36]. The increasing use of wireless technology in indoor environments has led the researchers to analyse its effects on human exposure. Many studies have monitored stationary subjects sitting in front of computers [37], focusing on two primary parameters: electric field distribution and specific absorption rate (SAR). While these studies contribute to understanding EMF impact, they only address certain aspects of potential health risks [38]. The scientifically established short-term effects of radiofrequency EMFs are thermal in nature, resulting from the conversion of absorbed electromagnetic energy into heat [39]. The increase in body temperature depends on thermoregulation mechanisms, such as increased blood circulation, sweating, and respiration, which limit excessive warming [40]. Studies suggest that body temperature increases below 1 \({}^{\circ}\)C are unlikely to have adverse health effects [41]. A widely discussed topic in the scientific community is the comparison of SAR measurements from wireless devices, often using standard dipole antennas and phantom models [42]. SAR evaluations have been validated through intercomparisons of measurements in head phantoms filled with simulant fluid. Studies investigating the impact of 5G networks have focused on millimetre-wave communications in dense indoor environments, analysing whether exposure to higher frequencies could negatively affect human health [43]. One study analysed RF-EMF exposure in high-density environments, such as shopping malls, where Wi-Fi (IEEE 802.11n/ac) and mobile communications (2G-5G) coexist. Measurements were taken in worst-case conditions using deterministic simulations,demonstrating a good agreement with the experimental data. This research highlights how the coexistence of multiple networks affects overall exposure [44]. The introduction of 6G technology, expected by 2030, has prompted further analysis of distributed multi-input multi-output (D-MIMO) architectures. One study used a hybrid simulation approach combining ray tracing and full-wave simulations for 3.5 GHz indoor transmission. Exposure levels were assessed using incident power density, local SAR, and whole-body SAR [45]. Most studies focus on simplified simulations, neglecting dynamic factors, such as human movement. Additionally, little attention is given to non-thermal effects, despite concerns over potential biochemical alterations due to long-term exposure. Many studies primarily examine tissue heating, overlooking environmental influences, such as the material composition and spatial arrangement of sources, which impact exposure variability. These limitations make it difficult to reproduce results across different indoor settings. Recent years have seen a growing interest in using artificial intelligence (AI) for monitoring body temperature in human tissue [46]. Multimodal sensor analysis has improved diagnostic capabilities, allowing for a more comprehensive assessment of tissue conditions. Machine learning (ML) and deep learning (DL) have been widely applied to analyse complex signals produced by sensors, enhancing the accuracy and efficiency in exposure assessments [47]. Some studies use synthetic datasets based on numerical models of human tissues, training convolutional neural networks (CNNs) to identify anomalies in SAR signals. These models have achieved high diagnostic accuracy.