Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation
In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the comple...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/11/9/343 |
| _version_ | 1797520074759733248 |
|---|---|
| author | Chin-Teng Lin Wei-Ling Jiang Sheng-Fu Chen Kuan-Chih Huang Lun-De Liao |
| author_facet | Chin-Teng Lin Wei-Ling Jiang Sheng-Fu Chen Kuan-Chih Huang Lun-De Liao |
| author_sort | Chin-Teng Lin |
| collection | DOAJ |
| description | In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications. |
| first_indexed | 2024-03-10T07:51:41Z |
| format | Article |
| id | doaj.art-d3156bf3256e4992ab91a17426e8183f |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T07:51:41Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-d3156bf3256e4992ab91a17426e8183f2023-11-22T12:12:57ZengMDPI AGBiosensors2079-63742021-09-0111934310.3390/bios11090343Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental ValidationChin-Teng Lin0Wei-Ling Jiang1Sheng-Fu Chen2Kuan-Chih Huang3Lun-De Liao4Australia Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, AustraliaInstitute of Electrical Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli City 35053, TaiwanInstitute of Electrical Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli City 35053, TaiwanIn the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.https://www.mdpi.com/2079-6374/11/9/343human–computer interfaceelectrooculographyeye-movement detectionfixationsaccadeblink |
| spellingShingle | Chin-Teng Lin Wei-Ling Jiang Sheng-Fu Chen Kuan-Chih Huang Lun-De Liao Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation Biosensors human–computer interface electrooculography eye-movement detection fixation saccade blink |
| title | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
| title_full | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
| title_fullStr | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
| title_full_unstemmed | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
| title_short | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
| title_sort | design of a wearable eye movement detection system based on electrooculography signals and its experimental validation |
| topic | human–computer interface electrooculography eye-movement detection fixation saccade blink |
| url | https://www.mdpi.com/2079-6374/11/9/343 |
| work_keys_str_mv | AT chintenglin designofawearableeyemovementdetectionsystembasedonelectrooculographysignalsanditsexperimentalvalidation AT weilingjiang designofawearableeyemovementdetectionsystembasedonelectrooculographysignalsanditsexperimentalvalidation AT shengfuchen designofawearableeyemovementdetectionsystembasedonelectrooculographysignalsanditsexperimentalvalidation AT kuanchihhuang designofawearableeyemovementdetectionsystembasedonelectrooculographysignalsanditsexperimentalvalidation AT lundeliao designofawearableeyemovementdetectionsystembasedonelectrooculographysignalsanditsexperimentalvalidation |