Enhancing Human-Computer Interaction with a Low-Cost Air Mouse and Sign Language Recognition System
Air Mouse; CNN; Hand Gesture; Mediapipe; Proton Assistant; Sign Language
The purpose of this study is to investigate the development of assistive technologies that are designed to empower people with disabilities by increasing their level of freedom and accessibility. Voice assistants, air mice, and software that recognizes sign language are some of the topics that are specifically covered in this. Those who have impaired fine motor skills can benefit from using air mice since they allow controls to be made by hand gestures. Using machine learning algorithms, sign language recognition software is able to decipher signs with an accuracy rate of over 90 percent, making it easier for people who are deaf or hard of hearing to communicate themselves. By relying solely on vocal instructions, voice assistants like Alexa make it possible to control devices without using your hands. Not only do these technologies have the potential to be revolutionary, but they also confront obstacles in terms of improving identification accuracy and integrating them into common gadgets. In this study, the development and impact of voice assistants, sign language software, and air mice are discussed. More specifically, the paper highlights the potential for these technologies to help millions of people with disabilities all over the world. Additionally, it examines potential enhancements that could be made to these technologies in the future in order to further improve accessibility and inclusivity. This research integrates computer vision and machine learning to create a multimodal system blending air mouse functionality with real-time sign language translation. Achieving 95% accuracy in gesture recognition for air mouse control and 98% accuracy in sign language letter classification using a basic webcam, the system promotes accessible interaction without specialized hardware. Despite limitations in vocabulary and lighting sensitivity, future efforts aim to broaden data training and explore mobile deployment. These advancements hold promise for enhancing natural human-computer interaction, particularly for users with disabilities, by enabling intuitive, hands-free control and communication. 2024 IEEE.
Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024, pp. 93-98.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICCPCT61902.2024.10673196 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205577953&doi=10.1109%2fICCPCT61902.2024.10673196&partnerID=40&md5=9bb0cbeafa7075e3ac8caee0c543e82f
Santhan H., CHRIST (Deemed to Be University), Computer Science, Bangalore, India; Sudhakar T., CHRIST (Deemed to Be University), Computer Science, Bangalore, India; Joy H.K., CHRIST (Deemed to Be University), Computer Science, Bangalore, India