## 6 Results
### One-Dimensional CNN for EIT Data Analysis  
Two CNN models were developed to classify hand gestures using 1D EIT data, as detailed in Table 2. The first model used 40 BVs as input, while the second was designed for 20 BVs. Both models share a similar overall layer architecture but have significant design variations to balance computational efficiency and performance. The 20 BV model features larger filter sizes, increased channel depths, and expanded fully connected layers to compensate for the reduced input dimensionality, thereby enhancing its ability to learn complex representations. To ensure a fair comparison of EIT input reduction effects, both models were carefully fine-tuned to achieve comparable accuracy and FLOPs. FLOPs, calculated based on multiply-accumulate operations, were approximately equal, with the 40 BV model requiring 3.813M FLOPs and the 20 BV model 3.783M FLOPs. Despite the increased computational demands of the convolutional and dense layers in the 20 BV model, \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline
**Subjects** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Accuracy with all BVs (\%) & 95.3 & 94.2 & 92.2 & 89.2 & 88.9 & 92.8 & 87.5 & 95.8 & 97.5 & 91.7 \\ Accuracy after PCA reduction (\%) & 75 & 71.67 & 75.83 & 75.1 & 68.6 & 82.4 & 71.67 & 92.5 & 85 & 73.33 \\ Accuracy after Chi-square reduction (\%) & 92.5 & 88.9 & 90.8 & 83.6 & 82.5 & 90 & 85 & 93.9 & 95.6 & 87.2 \\ Accuracy after mRMR reduction (\%) & 80 & 81.67 & 75 & 70.83 & 67.77 & 71.6 & 78.33 & 86.67 & 85 & 80 \\ Accuracy after Laplacian Score reduction (\%) & 69.17 & 78.33 & 80.83 & 65.83 & 60.33 & 68.33 & 72.5 & 90 & 87.5 & 80.83 \\ \hline \end{tabular}
\end{table} 
Table 1: Impact of data reduction on accuracy across 10 subjects. Figure 5: Comparison of different data reduction methods in terms of accuracy for 10 subjects. the reduced input size offset the additional cost. This balance highlights the effectiveness of hyperparameter tuning in designing models with similar computational requirements, enabling a better evaluation of performance trade-offs between the two input sizes. To assess the robustness and generalisability of both models, 5-fold cross-validation was used. In this approach, the dataset is split into five subsets, and the model is trained on four subsets and validated iteratively on the fifth. Cross-validation ensures that the performance metrics are less affected by random data splits, providing a more reliable estimate of model accuracy. Using this method, the 40 BVs model achieved a mean accuracy of \(94.72\%\pm 1.19\%\), while the 20 BVs model achieved a slightly higher mean accuracy of \(96.94\%\pm 1.19\%\). The results underline the ability of the 20 BVs model to maintain or even improve classification accuracy despite reduced input dimensionality, highlighting the effectiveness of its increased architectural complexity in extracting meaningful features. We can conclude that reducing the dimensionality of EIT input data effectively reduces signal ambiguity, leading to improved classification accuracy. This improvement is attributed to the elimination of redundant and less informative features in the original EIT input, which introduced noise in the feature space. By focusing on the most relevant aspects of the signal, the reduced input improves the model's ability to discriminate between the different classes of hand gestures. As a result, the model benefits from a cleaner, more interpretable dataset, which simplifies the learning process and contributes to higher accuracy. This result highlights the value of careful input dimensionality reduction in optimising model performance, particularly in applications where signal clarity is critical. Impact of Dimensionality Reduction and CNN Model Compression on EIT-Based ASL Hand Gesture Recognition  
A comprehensive evaluation was conducted on two key optimisations applied to 1D CNN models for ASL HGR using EIT data, and the results are presented in this section.