## 7 Conclusions  
The fusion of TinyML and edge computing has opened the possibility of real-time, energy-efficient processing in resource-constrained systems. By performing computations directly on the device, TinyML reduces latency, minimises energy consumption, and improves privacy, while edge computing provides the infrastructure to process and analyse data locally. These advances are particularly transformative for applications such as HGR, where fast, intuitive, and reliable interaction is essential. HGR systems play a key role in enabling seamless human-machine interaction, especially in scenarios that require localised intelligence and real-time responsiveness, such as robotics and assistive technologies. Reducing the input dimensionality of the EIT reduces signal ambiguity by removing redundant or noisy features, increasing clarity, and improving the classification accuracy. This highlights the effectiveness of focusing on the most relevant data to optimise model performance. However, reducing the CNN model input from 40 to 20 BVs initially increases model size and complexity, which suggests that input reduction alone is not sufficient for efficient deployment on IoT devices with limited resources and should be paired with model compression and optimisation. Our research demonstrates that the developed compression method is a highly effective optimisation technique, achieving substantial reductions in CNN model size, computational complexity, and FLOPs while maintaining accuracy. Specifically, there was a 91.75% and 97.49% reduction in model size for 40 and 20 BV EITinputs, respectively. Furthermore, compression resulted in a 99.2% and 99.6% reduction in computational complexity and FLOPs for the same inputs. Notably, these reductions were achieved without compromising accuracy, with the compressed models achieving 97.22% and 94.44% accuracy for 40 and 20 BV EIT inputs, respectively. The compression process introduced only a marginal increase in loss, from 0.31 to 0.36, indicating a negligible impact on the overall performance. These reductions are critical for edge devices, where memory and processing capabilities are constrained. For example, the compression of models with 40 BV reduces the model size from 173.73 KB to 14.35 KB, and from 347.73 KB to 8.73 KB for the 20 BV case, demonstrating how the compression method significantly enhanced model efficiency. The computational savings are similarly impressive, reducing FLOPs from 3.8 million to under 40,000 for both configurations. These improvements directly address the limitations of embedded systems and enable more efficient real-time processing. The hybrid approach, combining input reduction and model compression, strikes a balance between model compactness and performance. Despite achieving significant reductions in model size and computational requirements, the compressed 20 BV model exhibited a slight increase in loss and a marginal drop in accuracy. This highlights the challenges faced when aggressively reducing dimensionality and compressing models simultaneously. The model's accuracy for the 40 BV compressed case remained at 94.44%, with loss as low as 0.01, indicating that compression does not always introduce detrimental effects if carefully applied. However, the trade-offs observed in the compressed 20 BV model reflect the delicate balance between reducing input complexity and maintaining model performance. Future research should focus on fine-tuning compression techniques, to minimise loss and maintain or even improve accuracy. Additionally, to fully assess the feasibility of deploying such models on resource-constrained IoT edge devices, it is crucial to evaluate their real-time inference capabilities, including latency, energy consumption, and overall system performance. This will ensure that the system is ready for deployment in practical applications that require fast, efficient, and low-latency processing, such as human-machine interaction systems in robotics and assistive technology. Conceptualisation, M.M., S.S. and A.F.