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

Victor Chang; Rahman Olamide Eniola; Lewis Golightly; Qianwen Ariel Xu

doi:10.1007/s42979-023-01751-y

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

Victor Chang^*, Rahman Olamide Eniola, Lewis Golightly, Qianwen Ariel Xu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.

Original language	English
Article number	441
Number of pages	17
Journal	SN Computer Science
Volume	4
Issue number	5
DOIs	https://doi.org/10.1007/s42979-023-01751-y
Publication status	Published - 12 Jun 2023

Bibliographical note

© The Author(s) 2023.. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords

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

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Cite this

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abstract = "Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system{\textquoteright}s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system{\textquoteright}s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.",

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