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Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices.

Alberto Tellaeche Iglesias¹, Ignacio Fidalgo Astorquia², Juan Ignacio Vázquez Gómez¹

¹Computer Science, Electronics and Communication Technologies Department, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This study introduces a new deep learning system for hand gesture recognition using convolutional neural networks (CNNs). The system achieves 96.92% accuracy, overcoming common computer vision challenges for robust human-machine interaction.

Keywords:

deep learning embedded systems gesture detection real time

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Area of Science:

Computer Vision
Human-Machine Interaction
Deep Learning

Background:

Human-machine interaction (HMI) heavily relies on gesture detection.
Traditional computer vision methods face challenges like lighting variations and occlusions.
Deep learning offers effective solutions for these image processing issues.

Purpose of the Study:

To develop a robust hand gesture recognition system using CNNs and color images.
To ensure real-time performance on embedded systems.
To address environmental variations and image processing limitations.

Main Methods:

A novel, small-architecture Convolutional Neural Network (CNN) was designed.
The system utilizes color images for gesture detection.
The network is optimized for computationally limited embedded devices.

Main Results:

The proposed system achieved an average success rate of 96.92%.
Performance surpasses existing algorithms in state-of-the-art comparisons.
The system demonstrates robustness against environmental variations.

Conclusions:

The developed CNN-based system provides an effective solution for hand gesture recognition.
The system is suitable for real-time applications on embedded systems.
This approach enhances HMI by offering a robust and accurate gesture detection method.