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

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 spe...

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Bibliographic Details
Published in:SN computer science 2023-01, Vol.4 (5), p.441-441, Article 441
Main Authors: Chang, Victor, Eniola, Rahman Olamide, Golightly, Lewis, Xu, Qianwen Ariel
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Color
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Computers
Data collection
Data Structures and Information Theory
Deep learning
Emerging Technologies and Services for post-COViD-19
Ethics
Gesture recognition
Image segmentation
Information Systems and Communication Service
Machine learning
Natural language processing
Original Research
Pattern Recognition and Graphics
Pixels
Sensors
Software Engineering/Programming and Operating Systems
Speech
Usability
User interface
Vision
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Summary: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.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01751-y