Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems

Pedro Ponce, Troy McDaniel, Arturo Molina, Omar Mata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The eye’s muscles are difficult to model to build an eye prototype or an interface between the eye’s movements and computers; they require complex mechanical equations for describing their movements and the generated voltage signals from the eye are not always adequate for classification. However, they are very important for developing human machine interfaces based on eye movements. Previously, these interfaces have been developed for people with disabilities or they have been used for teaching the anatomy and movements of the eye’s muscles. However, the eye’s electrical signals have low amplitude and sometimes high levels of noise. Hence, artificial neural networks and fuzzy logic systems are implemented using an ANFIS topology to perform this classification. This paper shows how the eye’s muscles can be modeled and implemented in a concept prototype using an ANFIS topology that is trained using experimental signals from an end user of the eye prototype. The results show excellent performance for prototype when the ANFIS topology is deployed.

Original languageEnglish (US)
Title of host publicationUniversal Access in Human-Computer Interaction. Multimodality and Assistive Environments - 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
EditorsMargherita Antona, Constantine Stephanidis
PublisherSpringer Verlag
Pages300-311
Number of pages12
ISBN (Print)9783030235628
DOIs
StatePublished - Jan 1 2019
Event13th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019 - Orlando, United States
Duration: Jul 26 2019Jul 31 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11573 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
CountryUnited States
CityOrlando
Period7/26/197/31/19

Fingerprint

Adaptive Neuro-fuzzy Inference System
Eye Movements
Eye movements
Fuzzy inference
Muscle
Topology
Prototype
Modeling
Human-machine Interface
Fuzzy logic
Interfaces (computer)
Fuzzy Logic System
Teaching
Disability
Anatomy
Neural networks
Artificial Neural Network
Voltage
Electric potential
Human

Keywords

  • Artificial neural networks
  • Eye muscles
  • Fuzzy logic
  • Human eye movement

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ponce, P., McDaniel, T., Molina, A., & Mata, O. (2019). Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems. In M. Antona, & C. Stephanidis (Eds.), Universal Access in Human-Computer Interaction. Multimodality and Assistive Environments - 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings (pp. 300-311). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11573 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-23563-5_24

Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems. / Ponce, Pedro; McDaniel, Troy; Molina, Arturo; Mata, Omar.

Universal Access in Human-Computer Interaction. Multimodality and Assistive Environments - 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings. ed. / Margherita Antona; Constantine Stephanidis. Springer Verlag, 2019. p. 300-311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11573 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ponce, P, McDaniel, T, Molina, A & Mata, O 2019, Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems. in M Antona & C Stephanidis (eds), Universal Access in Human-Computer Interaction. Multimodality and Assistive Environments - 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11573 LNCS, Springer Verlag, pp. 300-311, 13th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019, Orlando, United States, 7/26/19. https://doi.org/10.1007/978-3-030-23563-5_24
Ponce P, McDaniel T, Molina A, Mata O. Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems. In Antona M, Stephanidis C, editors, Universal Access in Human-Computer Interaction. Multimodality and Assistive Environments - 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings. Springer Verlag. 2019. p. 300-311. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-23563-5_24
Ponce, Pedro ; McDaniel, Troy ; Molina, Arturo ; Mata, Omar. / Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems. Universal Access in Human-Computer Interaction. Multimodality and Assistive Environments - 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings. editor / Margherita Antona ; Constantine Stephanidis. Springer Verlag, 2019. pp. 300-311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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