Work

Design of Real-Time Gesture Recognition with Convolutional Neural Networks on a Low-End FPGA

Year: 2025

Type: article

Abstract: Hand gesture recognition is used in human-computer interaction, with multiple applications in assistive technologies, virtual reality, and smart systems. While vision-based methods are commonly employed, they are often computationally intensive, sensitive to environmental conditions, and raise privacy concerns. This work proposes a hardware/software co-optimized system for real-time hand gesture recognition using accelerometer data, designed for a portable, low-cost platform. A Convolutional Neural Network from TinyML is implemented on a Xilinx Zynq-7000 SoC-FPGA, utilizing fixed-point arithmetic to minimize computational complexity while maintaining classification accuracy. Additionally, combined architectural optimizations, including pipelining and loop unrolling, are applied to enhance processing efficiency. The final system achieves a 62× speedup over an unoptimized floating-point implementation while reducing power consumption, making it suitable for embedded and battery-powered applications.

Source: Electronics

DOI: 10.3390/electronics14173457

Authors: Rui Policarpo Duarte, Tiago Gonçalves, Gustavo Jacinto, Paulo Flores, Mário Véstias

Institutions: Universidade Politécnica de Lisboa, Instituto de Novas Tecnologias, Institute for Systems Engineering and Computers, Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, Instituto Superior Técnico

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Fundação para a Ciência e a Tecnologia, Center for Substance Abuse Treatment