ArticleBiosensors2021
Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- One-stop strabismus digital diagnosis via AI-integrated skin-like and wearable "Eyelectronics".Science advances · 2026Article
- Safe Meat, Smart Science: Biotechnology's Role in Antibiotic Residue Removal.Antibiotics (Basel, Switzerland) · 2025Review
- A Comprehensive Framework for Eye Tracking: Methods, Tools, Applications, and Cross-Platform Evaluation.Journal of eye movement research · 2025Review
- Next-Generation swimming pool drowning prevention strategy integrating AI and IoT technologies.Heliyon · 2024Review
- Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories.Frontiers in bioengineering and biotechnology · 2024Article
- Creating an AI-Enhanced Morse Code Translation System Based on Images for People with Severe Disabilities.Bioengineering (Basel, Switzerland) · 2023Article
- Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications.Biosensors · 2022Article
- Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements.Biosensors · 2022Review
- An IoT-Based Smart System with an MQTT Broker for Individual Patient Vital Sign Monitoring in Potential Emergency or Prehospital Applications.Emergency medicine international · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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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.