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An Exploration into Human-Computer Interaction: Hand Gesture Recognition Management in a Challenging Environment.

Victor Chang¹, Rahman Olamide Eniola², Lewis Golightly²

¹Aston University, Aston St, Birmingham, B4 7ET UK.

SN Computer Science

|June 19, 2023

Summary

This summary is machine-generated.

This study enhances human-computer interaction for the speech-impaired by developing a hand gesture recognition system. Image segmentation improved the Convolutional Neural Network (CNN) model

Keywords:

Convolutional neural network (CNN)Hand recognition Human–computer interaction Machine learning

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Area of Science:

Computer Science
Human-Computer Interaction
Artificial Intelligence

Background:

Speech-impaired individuals often rely on hand gestures for communication, yet are underrepresented in HCI research.
Existing human-computer interaction systems lack accessibility for the speech-impaired community.
Developing intuitive interaction methods is crucial for inclusivity.

Purpose of the Study:

To create an accessible hand gesture recognition system for the speech-impaired community.
To improve human-computer interaction efficiency and effortlessness without external devices.
To address the underrepresentation of the speech-impaired in HCI and automation research.

Main Methods:

A two-phase algorithm involving Region of Interest (ROI) segmentation and Convolutional Neural Network (CNN) image categorization.
Color space segmentation technique to isolate hand gestures from the background.
Utilizing Python Keras package for CNN model training and image classification.

Main Results:

The developed system demonstrated the necessity of image segmentation for effective hand gesture recognition.
The optimal CNN model achieved a 58% performance accuracy.
Performance increased by approximately 10% with image segmentation compared to without.

Conclusions:

Image segmentation is a critical component for improving hand gesture recognition accuracy.
The developed system offers a pathway towards more inclusive human-computer interaction for the speech-impaired.
Further research can build upon these findings to enhance accessibility in digital environments.