Eye Movement Controlled Smart Wheelchair

The Eye Movement Controlled Smart Wheelchair is an intelligent mobility solution designed for people with severe motor disabilities. It uses real-time eye tracking through a camera and OpenCV to detect gaze direction and convert it into wheelchair movement commands. A Raspberry Pi processes the eye movement data and controls navigation such as forward, left, right, and stop. Ultrasonic sensors are integrated for obstacle detection to ensure user safety and prevent collisions. The system provides hands-free, independent mobility with fast response and high accuracy. It is a cost-effective and user-friendly assistive technology for smart healthcare applications. The project combines computer vision, embedded systems, and automation into a single assistive platform. It offers reliable real-time performance with minimal delay between eye movement detection and wheelchair response. The design reduces dependency on caregivers and improves the quality of life for physically challenged individuals. Future enhancements can include AI-based tracking, voice assistance, and autonomous navigation features.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-30
DOI
https://doi.org/10.5281/zenodo.19919595
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Eye Movement Controlled Smart Wheelchair

Prathamesh Madhukar Pawar, Rohit Somnath Gade, Chaitanya Balu Vidhate, Prof.SD.Bhopale
Zenodo (CERN European Organization for Nuclear Research)
Gaze Tracking and Assistive Technology
article

Eye Movement Controlled Smart Wheelchair

Prathamesh Madhukar Pawar, Rohit Somnath Gade, Chaitanya Balu Vidhate, Prof.SD.Bhopale
article en

Abstract

The Eye Movement Controlled Smart Wheelchair is an intelligent mobility solution designed for people with severe motor disabilities. It uses real-time eye tracking through a camera and OpenCV to detect gaze direction and convert it into wheelchair movement commands. A Raspberry Pi processes the eye movement data and controls navigation such as forward, left, right, and stop. Ultrasonic sensors are integrated for obstacle detection to ensure user safety and prevent collisions. The system provides hands-free, independent mobility with fast response and high accuracy. It is a cost-effective and user-friendly assistive technology for smart healthcare applications. The project combines computer vision, embedded systems, and automation into a single assistive platform. It offers reliable real-time performance with minimal delay between eye movement detection and wheelchair response. The design reduces dependency on caregivers and improves the quality of life for physically challenged individuals. Future enhancements can include AI-based tracking, voice assistance, and autonomous navigation features.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 4%
Gaze Tracking and Assistive Technology
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.