## 4 Methods for EIT Data Reduction and CNN Model Compression for Efficient Edge Deployment
### CNN Model Compression for Compensating Reduced Input Dimensionality  
CNNs represent a feedforward neural network architecture that includes convolutional, and fully connected components, which are central to feature extraction and classification processes [39]. One-Dimensional (1D) CNNs are particularly effective in processing time-series data, such as EIT BVs measurements. This architectural design is exceptionally well suited to sequential data analysis and is particularly useful for tasks requiring temporal pattern recognition, such as HGR. The core of 1D CNNs lies in their convolutional layers, which use sliding filters over input sequences to identify localised features. These filters detect specific patterns within temporal data, capturing voltage fluctuations that indicate specific muscle contractions and hand movement. As information passes through the fully connected layers of the network, these localised pattern detections are used to differentiate and classify gestures. For this reason, 1D CNNs excel at analysing sequential data because they automatically learn the temporal patterns that are crucial for identifying complex actions such as complex ASL gestures.  
Additionally, building on previous work, a methodology for efficiently compressing CNN models to reduce their computational footprint has been developed and validated [40]. This approach was used to develop a lightweight model suitable for efficient deployment on IoT devices. In previous research, CNNs were chosen over alternative architectures because of their compatibility with hardware-accelerated platforms, which include specialised CNN accelerators [23]. This compatibility ensures optimised real-time processing, allowing these neural networks to achieve high computational efficiency. In addition, their adaptability to  
Figure 3: Methodology of EIT data reduction.  
hardware acceleration technologies makes CNN-based solutions particularly practical and effective for advanced sensing and classification tasks.  
As shown in Figure 4, two CNN architectures were developed to evaluate the effects of input dimensionality on classification performance: a full input model that processes the original dimensionality of EIT data and a reduced input model that operates on reduced input dimensionality. Both models share a similar overall layer architecture but are designed to balance computational efficiency and performance. Indeed, the reduced input model has larger filter sizes, deeper convolutional layers, and expanded fully connected layers with more neurons to compensate for the reduced input size while maintaining classification accuracy. In fact, both models were compressed using an identical KD approach, where a pre-trained teacher model guides the training of a smaller student model with the same architecture and hyperparameters. The idea is to have the same student model for the two inputs, but the input with reduced data has a model with higher parameters to compensate for the reduced input of the teacher model of the non-reduced model. By keeping the FLOPs equivalent, the primary variable evaluated is the impact of input dimensionality reduction, rather than differences in computational complexity. This allows a direct assessment of how reducing the input affects the model's ability to learn and classify. If one model has significantly lower FLOPs, it inherently has a computational advantage, making it difficult to determine whether performance differences are due to architectural design or computational resources. This approach ensured consistent optimisation across models with different input sizes, maintaining classification performance while reducing computational complexity.