Ultra-Fast Edge Computing Approach for Hand Gesture Classification Based on EIT Measurements

Abstract

Gesture-based robot control offers intuitive interaction between humans and robots, with applications ranging from industrial automation to assistive robotics. However, existing solutions face challenges in achieving real-time requirements while ensuring accurate gesture recognition. This paper presents a new edge computing-based approach for real-time control of robots using Electrical Impedance Tomography (EIT) measurements to classify hand gesture numbers in American Sign Language (ASL). Existing solutions for gesture recognition struggle to achieve real-time performance while maintaining accuracy and energy efficiency. This challenge becomes higher in the case of EIT because of its relative complexity. We focus therefore on leveraging the capabilities of the edge device to implement effectively the Convolutional Neural Network (CNN) acceleration. The proposed solution combines hardware-aware optimization techniques to achieve fast and accurate gesture recognition by enabling rapid inference while minimizing energy consumption on a low-power resource-constrained device with Tiny Machine Learning (TinyML) capabilities. The lightweight CNN model required only 10.2 s to train using the Keras library of TensorFlow and achieved an accuracy of 89.37% for 10 sign language classes, with only 66 μs taken to run inference on the hardware-accelerated microcontroller-based device.