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

• Published in: SN computer science Vol. 4; no. 5; p. 441
• Main Authors: Chang, Victor, Eniola, Rahman Olamide, Golightly, Lewis, Xu, Qianwen Ariel
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2023; Springer Nature B.V
• 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; Convolutional neural network (CNN); Machine learning; Hand recognition; Human–computer interaction
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-023-01751-y

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.