## 3 Materials and Methods
### Post-Processing of SAR and Temperature Variation Signals with the FFNN  
Unlike traditional models, which often focus on static exposure conditions, this work considered both stationary and dynamic environments, where real-world motion affects the exposure distribution. This study compared simulated predictions with actual measurements, validating the computational model. The acquired data were processed by a feedforward neural network (FFNN) designed to predict SAR values. The network consists of dense layers with five input variables, structured with 64, 32, and 16 nodes as  
Figure 5: Monitoring board design for SAR and temperature variation signal acquisition. hidden layers and one node for continuous SAR prediction as the output rate. By integrating real-time measurements, artificial intelligence-driven analysis, and environmental modelling, this work improves the understanding of electromagnetic exposure and its effects on human health. This research also aims to contribute to new safety guidelines by leveraging artificial intelligence for more accurate exposure assessments. In this study, we developed a feedforward neural network (FFNN) designed for post-processing SAR signals and temperature variations for predictive analysis [55]. The input of the network is an array \(x=[x_{SAR},x_{frequency},x_{temperature\ variation}]\) of size \((n_{features})\), where \(n_{features}\) is equal to 3, i.e., the number of features. The input of the network is an array size equal to 3, i.e., the number of features. The SLM architecture of the FFNN consisted of 3 nodes and the input values were pre-processed in a range [0, 1]. The hidden layer had a size of 8 nodes and had a rectified linear unit activation function (ReLU), shown in Equation (1). \[f(x)=max(0,x) \tag{1}\]  
The second hidden layer, on the other hand, had a size of 6 nodes and a ReLU. The output layer had a dimension of 2 nodes--temperature predictions and SAR levels--and a linear activation function (Figure 6). The dataset used for the training and evaluation of the feedforward network was constructed using data from the two main sensor types, namely the \"Semtech SX9306\" SAR sensor and the \"Omron IR\" infrared temperature sensor. The dataset included supervised labels necessary for the network learning process. These labels specifically include numerical values of expected temperatures, expected SAR exposure levels, and qualitative risk classes, such as safe, moderate exposure, and critical exposure. To ensure effective model training and minimize errors or overfitting, the collected data underwent pre-processing. All data were scaled with the MinMax Scaling function to normalize inputs to a [0,1] range to prevent disproportionate influences from different scales (e.g., temperature and SAR intensity). A low-pass filter was applied to SAR signals to attenuate high-frequency interference, while temperature measurements were cleaned of outliers for accurate thermal representation. To enhance generalization and prevent overfitting, data compression was implemented, considering potential signal quality loss. Principal Component Analysis (PCA) was used to transform multidimensional data, retaining key information while discarding less significant variance components. Figure 6: Feedforward neural network architecture. Figure 7 shows how the steps performed resulted in a clean, scaled, and optimised dataset, ready to be used in feedforward network training. Pre-processing not only improves the quality of the data, but also reduces the risk of overfitting, ensuring the greater predictive ability of the model. The choice of using a FFNN instead of more complex algorithms, like CNN or RNN, was due to the nature of the dataset that consisted of numerical measurements (SAR, temperature, and distance). Each data point represents an independent measurement, meaning there is no need to capture temporal dependencies. The last advantage of the FFNN is the high accuracy with low computational cost, making it suitable for real-time applications.