Eyelid Movement Command Classification Using Machine Learning

Philip P. Graybill, Mehdi Kiani

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

Abstract

The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four machine learning classifiers are trained on data taken from one subject and validated offline on the training subject plus two additional subjects. The classifiers are assessed for accuracy, computational and memory requirements, and transferability from the "training" subject to the other two subjects. A support vector machine (SVM) achieved the highest level of accuracy (97.5%) while using a potentially prohibitive level of computational and memory resources. A logistic regression classifier also achieved excellent accuracy (96.5%) while using two to three orders of magnitude fewer computational and memory resources than the SVM.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3637-3640
Number of pages4
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Graybill, P. P., & Kiani, M. (2019). Eyelid Movement Command Classification Using Machine Learning. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 3637-3640). [8857766] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857766