## 3 Methodology
### Model Training and Hyperparameters  
The proposed ConvResBiGRU-SE model's performance is dependent on the availability of sufficient and diverse training data and the careful tuning of architectural design parameters, commonly known as hyperparameters. The hyperparameters comprise epoch  
Figure 6: Structure of the SE module.  
counts, learning rates, and batch size. To guarantee strong model performance, we utilized a conventional method that entails partitioning the data into training and validation sets. The training set facilitated hyperparameter optimization, whereas the holdout validation set served for independent comparative evaluation.  
We identified the optimal hyperparameter settings that maximized the model's accuracy through trial and error. The parameters consist of a batch size of 128, a learning rate of \(1\times 10^{-3}\), and a maximum epoch limit of 200. An early stopping strategy was employed to prevent overfitting and ensure that the model learns generalizable patterns rather than merely memorizing the training data. The training was automatically halted after 10 consecutive epochs without any enhancement in validation accuracy. Furthermore, we instituted an adaptive learning rate strategy that decreases the learning rate by 25% if no enhancement is detected during the specified interval. This method facilitates more efficient model convergence and prevents entrapment in suboptimal solutions.  
Table 1 presents a comprehensive overview of the hyperparameters employed in our model architecture. The convolutional block employed a kernel size of 3 and 256 filters, facilitating efficient spatial feature extraction from the raw sensor data. The SE-Residual blocks, central to our proposed architecture, were designed with BiGRU units featuring 128 hidden states to capture temporal dependencies, while the convolutional paths utilized varying kernel sizes (3 and 5) to extract multi-scale features. The classification block employed global average pooling, succeeded by fully connected layers, resulting in an output layer with neurons corresponding to the number of activity classes.  
This carefully tuned hyperparameter configuration yielded a model that achieved outstanding performance while maintaining reasonable computational complexity, making it suitable for practical applications in human activity recognition using wearable sensors.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Stage** & **Hyperparameters** & **Values** \\ \hline Architecture & Convolution Block & & \\ \multirow{3}{*}{1D Convolution} & Kernel Size & 3 \\  & Stride & 1 \\  & Filters & 256 \\ \cline{2-4}  & Batch Normalization & & - \\  & Activation & & ELU \\  & Max Pooling & 2 \\ \hline SE-Residual Block - (Path 1) & & & \\   BiGRU Unit & & 128 \\ \hline (Path 2) & & & \\ \multirow{3}{*}{Conv1D} & Kernel Size & 3 \\  & Stride & 1 \\  & Filters & 256 \\ \cline{2-4}  & Batch Normalization & & - \\  & Activation & & ELU \\  & Kernel Size & 5 \\  & Stride & 1 \\  & Filters & 256 \\ \cline{2-4}  & Batch Normalization & & - \\  & SE & - \\ \cline{2-4}  & Classification Block & & \\  & Global Average Pooling & - \\  & Dense & 64 \\ \cline{2-4}  & Dense & No. of activity classes \\ \hline Training & Loss Function & Cross-entropy \\  & Optimizer & Adam \\  & Batch Size & 128 \\  & Number of Epochs & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of hyperparameters of the ConvResBiGRU-SE model.