Enhancing Accessibility: A Natural Human-Computer Interaction System Using Hand Gestures and Voice for Disabled Populations

This paper presents an innovative application that enables control of multimedia content through voice commands and static hand gestures, offering a transformative solution for individuals with disabilities. The application provides an intuitive, natural method for interacting with slides, images, videos, and audio, bypassing traditional input devices like keyboards and mice. It enhances accessibility and user experience by accommodating users with mobility impairments or low vision. Another key feature is the virtual writing capability, allowing users to draw or erase in mid-air and project the writing and drawing onto the screen, providing greater flexibility. The software leverages advanced technologies such as MediaPipe for gesture recognition, SpeechRecognition for voice command processing, and PyAutoGUI for automation, making it suitable for various environments, including personal entertainment, classroom, and conference settings. Additionally, users can customize hand gestures for specific commands, with newly captured gestures used to retrain the model. This paper outlines the system architecture, implementation, and performance evaluation, and discusses future developments.

Authors

Institutions

Publication Details

Published
2025-05-05
DOI
https://doi.org/10.1109/cai64502.2025.00048
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Accessibility: A Natural Human-Computer Interaction System Using Hand Gestures and Voice for Disabled Populations

Michelle Zhu, Mohammed Wasim Siddiqui, Siddharth Singh
Hand Gesture Recognition Systems
article

Enhancing Accessibility: A Natural Human-Computer Interaction System Using Hand Gestures and Voice for Disabled Populations

Michelle Zhu, Mohammed Wasim Siddiqui, Siddharth Singh
article en

Abstract

This paper presents an innovative application that enables control of multimedia content through voice commands and static hand gestures, offering a transformative solution for individuals with disabilities. The application provides an intuitive, natural method for interacting with slides, images, videos, and audio, bypassing traditional input devices like keyboards and mice. It enhances accessibility and user experience by accommodating users with mobility impairments or low vision. Another key feature is the virtual writing capability, allowing users to draw or erase in mid-air and project the writing and drawing onto the screen, providing greater flexibility. The software leverages advanced technologies such as MediaPipe for gesture recognition, SpeechRecognition for voice command processing, and PyAutoGUI for automation, making it suitable for various environments, including personal entertainment, classroom, and conference settings. Additionally, users can customize hand gestures for specific commands, with newly captured gestures used to retrain the model. This paper outlines the system architecture, implementation, and performance evaluation, and discusses future developments.

Montclair State University (US)
Quality Education
Openalex Percentile: Top 84%
Hand Gesture Recognition Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.