Evidence map›Paper›PMID 32679779›Full record

ArticleSensors (Basel, Switzerland)2020

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control.

Mahmoud Dahmani, Muhammad E H Chowdhury, Amith Khandakar, Tawsifur Rahman, Khaled Al-Jayyousi, Abdalla Hefny, Serkan Kiranyaz

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
6.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 64 citations in OpenAlex.

  1. Review
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  5. Review
  6. Wearable Near-Eye Tracking Technologies for Health: A Review.Bioengineering (Basel, Switzerland) · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 3 countries.

Mahmoud DahmaniSchool of Engineering, University of Maryland, College Park, MD 20742, USA.
Muhammad E H ChowdhuryDepartment of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.ORCID 0000-0003-0744-8206
Amith KhandakarDepartment of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.ORCID 0000-0001-7068-9112
Tawsifur RahmanDepartment of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.
Khaled Al-JayyousiDepartment of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.
Abdalla HefnyDepartment of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.
Serkan KiranyazDepartment of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.
Qatar University · QAUniversity of Dhaka · BDUniversity of Maryland, College Park · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Eye-Tracking TechnologyPersons with DisabilitiesWheelchairsAlgorithmsHumansNeural Networks, Computerconvolutional neural networks (CNNs)eye trackingmachine learningmotorized wheelchairultrasonic proximity sensors

Identifiers

PMID32679779
PMCPMC7412002
OpenAlexW3021505501

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.