Camera Based Eye Movement and EOG Detection to Control Mobility Assistive Device Using Graphical User Interface / (Record no. 609112)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02548nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rashid, Anum |
| 245 ## - TITLE STATEMENT | |
| Title | Camera Based Eye Movement and EOG Detection to Control Mobility Assistive Device Using Graphical User Interface / |
| Statement of responsibility, etc. | Anum Rashid |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 77p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Researchers from all over the world have recently become increasingly interested in biobased human machine interfaces (HMI) for the assistance of paralyzed people enabling them to<br/>live an assistance free life. Among various approaches of designing a Human machine interface,<br/>eye signals are considered the most appropriate type of input. In general, eye-tracking systems<br/>assess a person's eyeball position and gaze direction and are classified into two approaches:<br/>electrooculography-based and computer vision based. This research uses EOG, and computer<br/>vision technique to predict which input method is more appropriate and user friendly for the<br/>mobility of an electric wheelchair. EOG data is acquired for four different eye movements i.e.,<br/>right, left, upward, downward using BIOPAC. Video based data set is acquired using a webcam<br/>mounted at a fixed distance from the subject. EOG dataset is then processed and classified using<br/>eleven different classifiers among which the Decision tree shows the highest accuracy and F1 score<br/>i.e., 88.94 ± 13.82, 89.12 ± 13.58 respectively. The other data set of videos is processed using<br/>computer vision. Deep learning algorithms are used to classify the results. Both systems mentioned<br/>in this study have their own limitations. For EOG based system, the attachment of electrodes is a<br/>must requirement. This causes irritation to the user and sometimes generates motion artifacts<br/>which can be a source of hinderance for the motion of any HMI. For computer vision-based system,<br/>camera is a must requirement. However, it can’t be used in dark rooms, outdoor; during night<br/>times, wearing sunglasses and in similar other situations. For such situations, another alternative<br/>is an infrared camera, but prolonged usage of such camera can damage the eye. Therefore, a hybrid<br/>system should be developed which involves both techniques i.e, EOG and a camera which can<br/>effectively drive any mobility assistive device. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/30865">http://10.250.8.41:8080/xmlui/handle/123456789/30865</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 05/14/2024 | 610 | SMME-TH-734 | Thesis |