Hybrid Solution Through Systematic Electrical Impedance Tomography Data Reduction and CNN Compression for Efficient Hand Gesture Recognition on Resource-Constrained IoT Devices

Salwa Sahnoun (), Mahdi Mnif, Bilel Ghoul, Mohamed Jemal, Ahmed Fakhfakh and Olfa Kanoun ()
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Salwa Sahnoun: National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3018, Tunisia
Mahdi Mnif: National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3018, Tunisia
Bilel Ghoul: National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3018, Tunisia
Mohamed Jemal: National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3018, Tunisia
Ahmed Fakhfakh: National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3018, Tunisia
Olfa Kanoun: Measurements and Sensor Technology, Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, 09126 Chemnitz, Germany

Future Internet, 2025, vol. 17, issue 2, 1-20

Abstract: The rapid advancement of edge computing and Tiny Machine Learning (TinyML) has created new opportunities for deploying intelligence in resource-constrained environments. With the growing demand for intelligent Internet of Things (IoT) devices that can efficiently process complex data in real-time, there is an urgent need for innovative optimisation techniques that overcome the limitations of IoT devices and enable accurate and efficient computations. This study investigates a novel approach to optimising Convolutional Neural Network (CNN) models for Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT), which requires complex signal processing, energy efficiency, and real-time processing, by simultaneously reducing input complexity and using advanced model compression techniques. By systematically reducing and halving the input complexity of a 1D CNN from 40 to 20 Boundary Voltages (BVs) and applying an innovative compression method, we achieved remarkable model size reductions of 91.75% and 97.49% for 40 and 20 BVs EIT inputs, respectively. Additionally, the Floating-Point operations (FLOPs) are significantly reduced, by more than 99% in both cases. These reductions have been achieved with a minimal loss of accuracy, maintaining the performance of 97.22% and 94.44% for 40 and 20 BVs inputs, respectively. The most significant result is the 20 BVs compressed model. In fact, at only 8.73 kB and a remarkable 94.44% accuracy, our model demonstrates the potential of intelligent design strategies in creating ultra-lightweight, high-performance CNN-based solutions for resource-constrained devices with near-full performance capabilities specifically for the case of HGR based on EIT inputs.

Keywords: edge computing; TinyML; neural network compression; model compression techniques; lightweight model design; CNN; EIT; hand gesture recognition; EIT data reduction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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