ArticleSensors (Basel, Switzerland)2020
An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The trial behind it
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Who cites it
19 citing papers in PubMed, 64 citations in OpenAlex.
- The promise and peril of AI for disability health equity: A justice-centered narrative review.PLOS digital health · 2026Review
- Perception of oral hygiene during orthodontic treatment: an eye-tracking and cross-sectional study.Scientific reports · 2026Article
- Hands-Free Human-Machine Interfaces Using Piezoelectric Sensors and Accelerometers for Simulated Wheelchair Control in Older Adults and People with Physical Disabilities.Sensors (Basel, Switzerland) · 2025Article
- Visual attentional differences in psychology students with and without disabilities: a pilot study assessing the flanker task for prescriptive visual accommodative technologies.Frontiers in psychology · 2025Article
- Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration.Biomimetics (Basel, Switzerland) · 2024Review
- Wearable Near-Eye Tracking Technologies for Health: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification.Frontiers in physiology · 2024Article
- Design and Evaluation of the Extended FBS Model Based Gaze-Control Power Wheelchair for Individuals Facing Manual Control Challenges.Sensors (Basel, Switzerland) · 2023Article
- Review
- Intelligent Eye-Controlled Electric Wheelchair Based on Estimating Visual Intentions Using One-Dimensional Convolutional Neural Network and Long Short-Term Memory.Sensors (Basel, Switzerland) · 2023Article
- COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.Health information science and systems · 2022Article
- Human-machine interface-based wheelchair control using piezoelectric sensors based on face and tongue movements.Heliyon · 2022Article
- An Augmented Reality Serious Game for Learning Intelligent Wheelchair Control: Comparing Configuration and Tracking Methods.Sensors (Basel, Switzerland) · 2022Article
- Usability Evaluation of the SmartWheeler through Qualitative and Quantitative Studies.Sensors (Basel, Switzerland) · 2022Article
- Mini-review: Robotic wheelchair taxonomy and readiness.Neuroscience letters · 2022Review
- Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model.Sensors (Basel, Switzerland) · 2022Article
- Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort.Computational intelligence and neuroscience · 2022Article
- Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM.Biosensors · 2021Article
- Sensors, Signal and Image Processing in Biomedicine and Assisted Living.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the 34 developed and 156 developing countries, there are ~132 million disabled people who need a wheelchair, constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head. This paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input is images of the user's eye that are processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented, and tested; all of which were based on a benchmark database created by the authors. The first three techniques were automatic, employ correlation, and were variants of template matching, whereas the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e., 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on eight subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin. This work not only empowers any immobile wheelchair user, but also provides low-cost tools for the organization assisting wheelchair users.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.