## 2 Related Works  
EIT has been extensively studied for its potential in wearable and non-invasive HGR systems. Despite its promise, existing approaches often face challenges related to real-time processing, energy efficiency, and system complexity. In terms of accuracy, EIT systems have shown remarkable results. For example, [PERSON] et al. [13] reported an impressive 95.94% accuracy in recognising 10 American Sign Language (ASL) gestures. This system used eight electrodes with the four-pole sensing method, resulting in 40 boundary voltage measurements. However, the high number of measurements increases the computational load, which makes it less suitable for real-time wearable applications. Similarly, [PERSON] et al. [14] achieved an even higher accuracy of 98.13% for eight hand gestures using a compact neural network combined with a spatial filter. However, the study focused on optimising recognition accuracy without addressing energy efficiency or dataset complexity, which are critical for edge-based wearable devices. In the context of dimensionality reduction, [PERSON] et al. [15] investigated using Principal Component Analysis (PCA) to reduce the dimensionality of EIT data for forearm mobility analysis. The study showed that PCA effectively reduces the data size while preserving relevant features for classification. However, PCA has limitations in terms of adaptability and scalability, particularly for real-time edge computing solutions that require high energy efficiency. Its reliance on linear transformations may not always capture complex data relationships, leaving room for more advanced methods. Regarding wearability, [PERSON] et al. [16] presented a compact EIT-based system that could classify two simple gestures (fist and open hand) using a neural network classifier, achieving 78.33% accuracy. The study highlighted the inherent trade-off between device miniaturisation and signal quality. While the system's compact design is suitable for wearable applications, reduced electrode coverage, and signal resolution compromise accuracy, limiting its applicability to more complex gesture recognition tasks. These studies illustrate the potential of EIT for gesture recognition but also highlight its limitations. The reliance on large datasets without innovative reduction techniques significantly impacts processing speed and energy efficiency. Furthermore, the focus on either data quality or hardware limitations, rather than an integrated approach, leaves significant room for improvement. Edge computing [17] has emerged as a transformative paradigm, enabling faster inference and reduced latency in gesture recognition systems. Various sensing modalities, including radar, ultrasonic, and electromyography, have been employed in edge-optimised implementations. For radar-based gesture recognition, [PERSON] et al. [18] developed a system using radar technology on an NVIDIA Jetson Nano. Their model achieved 93.11% accuracy with a latency of 200 ms, demonstrating its suitability for gesture classification. However, the power-hungry design of the system makes it impractical for low-power edge devices where energy efficiency is paramount. [PERSON] et al. [19] addressed this challenge by developing deep learning models optimised for radar-based recognition on a Raspberry Pi 4. Their system achieved 97.57% accuracy with a latency of 40.8 ms, but the reliance on external accelerators results in additional power consumption. The authors used an iterative filter pruning (FP) approach to minimise the model size. This process involved calculating the L1 norm of the weights for each convolutional filter, identifying the filters with the least significant impact on the model, and removing them from the convolutional layers. For ultrasound- and electromyography (EMG)-based solutions, [PERSON] et al. [20] used ultrasound sensors integrated with Neuroshield, achieving a latency of 250 ms. Although the system provided reasonable accuracy, it did not prioritise energy efficiency, an essential factor for continuous use in wearable devices.