###### Abstract
A study analysed measurements of personal exposure to radio-frequency electromagnetic fields and concluded that in most everyday contexts, exposure levels are below the recommended safety limits [30]. Despite international safety regulations, the variability of indoor conditions necessitates individualized assessments to minimize risks. Factors such as the proximity of devices, room layout, and the number of active sources influence SAR levels. Areas with a high electromagnetic density--such as offices with multiple wireless networks--may pose greater exposure risks than open spaces. The existing studies have significantly advanced the understanding of electromagnetic exposure [31]. However, most rely on periodic or simulated measurements, limiting real-time monitoring. Traditional methods often involve bulky laboratory setups or statistical estimations, rather than real-world data collection. Furthermore, the previous research has generally assumed static exposure models, overlooking the dynamic nature of human interaction with EMFs. To address these gaps, this study proposes a continuous monitoring system capable of real-time SAR and temperature tracking. The electronic device integrates sensor-based measurements with artificial intelligence (AI) processing, offering several advantages over conventional methods. Real-time data acquisition, unlike previous studies that rely on snapshots of exposure, is a significant innovation because the proposed system continuously collects SAR and temperature data during various activities. The realized device eliminates the need for bulky laboratory equipment, making it feasible for daily monitoring [32]. The monitoring device detects SAR levels and temperature changes under different indoor conditions. The collected data are transmitted to a cloud-based artificial intelligence system, where machine learning algorithms process and identify patterns of exposure trends. This study was conducted in a controlled indoor environment, where subjects were monitored both stationary and moving. The experimental conditions included a room size of 4 m \(\times\) 4 m, with walls designed to reflect typical home and office environments, participating subjects of different body compositions, monitored devices, Wi-Fi routers, cell phones and other electromagnetic sources, and measurement instruments. The feedforward neural network (FFNN) processes sensor inputs, identifies high-risk exposure patterns, and provides predictive information. The results reveal a direct correlation between subject mobility and SAR distribution. Under stationary conditions, hotspots formed in high-exposure areas, particularly near the head and torso. In contrast, movement dispersed SAR concentration, leading to a more even distribution. Higher SAR values were recorded when subjects remained motionless near Wi-Fi routers and mobile devices. Temperature peaks correlated with increased SAR levels, particularly in areas with limited ventilation [33]. Subjects closer to reflective surfaces experienced amplified exposure due to wave accumulation. The AI model accurately classified exposure patterns with a precision of 94%, outperforming traditional statistical approaches. This study demonstrates the feasibility of a real-time SAR and temperature monitoring system enhanced by AI-driven analysis. The findings suggest that adaptive shielding strategies, such as optimized workspace layouts and reduced Wi-Fi usage, could mitigate overexposure risks. By integrating sensor-based monitoring with artificial intelligence, this research advances the understanding of indoor electromagnetic exposure, providing a foundation for improved safety regulations and public health recommendations.