To overcome these challenges, edge computing has emerged as a promising solution, particularly in addressing the power management and latency limitations of cloud computing, as discussed by [4]. Tiny machine learning (TinyML) brings ML and deep learning to embedded systems using ultra-low-power processors such as MCUs and digital signal processors [5]. By running models directly on edge devices, TinyML improves privacy, reduces latency, and lowers network and server loads, enabling faster, more efficient, and more secure real-time processing [6]. TinyML faces several challenges that hinder its wider adoption. The reliance on manual tuning and optimisation limits scalability, while strict performance constraints limit the complexity of models, often compromising their accuracy and robustness. Many current approaches focus on optimising specific metrics such as energy efficiency or latency while overlooking the trade-offs with other important aspects such as model accuracy. Addressing these challenges is critical to unlocking the full potential of TinyML and ensuring its smooth integration into diverse applications [7]. In this context, to address the challenges of merging edge computing and TinyML, Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT) data [8] was used as a comprehensive example of applications requiring localised real-time processing. HGR is a critical aspect of HMI, enabling meaningful interactions through movements of the human hand [9]. While gesture-based interaction is promising for improving human-robot interaction by providing intuitive control mechanisms that mimic natural communication, achieving real-time responsiveness in gesture-based robot control systems is challenging due to computational and energy efficiency constraints. HGR is achieved through two primary methods: Sensor-based [10] and camera-based approaches [11]. Camera-based systems leverage computer vision to analyse hand postures using video streams and images. While effective, camera-based approaches face significant challenges, such as sensitivity to lighting conditions and reflective surfaces, difficulty detecting skin tones due to limited resolution, and the high computational demands required to process video data. In contrast, sensor-based systems rely on wearable devices to capture sensory data, offering direct and robust measurements of hand movements. One notable sensor-based method is EIT, a technology that facilitates the control of robotic hands and prostheses through HMI [12]. EIT is particularly promising for capturing changes in hand posture due to its high sensitivity to changes in tissue conductivity and its ability to provide insight into deep muscle activity. The goal is to design a fast, energy-efficient, and data-driven autonomous system for HGR using EIT signals, leveraging edge computing and parameter reduction through advanced deep learning model compression. The system aims to deliver high accuracy while minimising energy consumption and processing latency. To achieve this, advanced data reduction methods will also be integrated to optimise the processing pipeline, reducing the complexity of EIT signals without compromising the quality of the information used for gesture classification. This paper is organised as follows: Section 2 examines the related work, concentrating on techniques for reducing gesture recognition data and exploring edge computing-based systems for HGR. Section 3 describes the system architecture and data collection protocol, including the EIT measurement setup, hardware components, and the data acquisition process. Section 4 presents the methods used for EIT data reduction and Convolutional Neural Network (CNN) model compression, emphasising their role in enabling efficient deployment on edge devices. Section 5 elaborates on the methodology for reducing EITdata dimensionality and compressing CNN models, highlighting the specific approaches for parameter reduction and their integration.