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dc.contributor.advisor Moustafa, Mohamed
dc.contributor.author Zahran, Ahmed
dc.date.accessioned 2017-09-13T07:42:58Z
dc.date.available 2019-09-13T22:00:07Z
dc.date.created Fall 2017 en_US
dc.date.issued 2017-09-13
dc.identifier.uri http://dar.aucegypt.edu/handle/10526/5188
dc.description.abstract This study investigates the utilization of an Electromyography (EMG) based device to recognize five eye gestures and classify them to have a hands free interaction with different applications. The proposed eye gestures in this work includes Long Blinks, Rapid Blinks, Wink Right, Wink Left and finally Squints or frowns. The MUSE headband, which is originally a Brain Computer Interface (BCI) that measures the Electroencephalography (EEG) signals, is the device used in our study to record the EMG signals from behind the earlobes via two Smart rubber sensors and at the forehead via two other electrodes. The signals are considered as EMG once they involve the physical muscular stimulations, which are considered as artifacts for the EEG Brain signals for other studies. The experiment is conducted on 15 participants (12 Males and 3 Females) randomly as no specific groups were targeted and the session was video taped for reevaluation. The experiment starts with the calibration phase to record each gesture three times per participant through a developed Voice narration program to unify the test conditions and time intervals among all subjects. In this study, a dynamic sliding window with segmented packets is designed to faster process the data and analyze it, as well as to provide more flexibility to classify the gestures regardless their duration from one user to another. Additionally, we are using the thresholding algorithm to extract the features from all the gestures. The Rapid Blinks and the Squints were having high F1 Scores of 80.77% and 85.71% for the Trained Thresholds, as well as 87.18% and 82.12% for the Default or manually adjusted thresholds. The accuracies of the Long Blinks, Rapid Blinks and Wink Left were relatively higher with the manually adjusted thresholds, while the Squints and the Wink Right were better with the trained thresholds. However, more improvements were proposed and some were tested especially after monitoring the participants actions from the video recordings to enhance the classifier. Most of the common irregularities met are discussed within this study so as to pave the road for further similar studies to tackle them before conducting the experiments. Several applications need minimal physical or hands interactions and this study was originally a part of the project at HCI Lab, University of Stuttgart to make a hands-free switching between RGB, thermal and depth cameras integrated on or embedded in an Augmented Reality device designed for the firefighters to increase their visual capabilities in the field. en_US
dc.description.sponsorship Acknowledgment to HCI Lab, VIS Institute, University of Stuttgart namely Jakob Karlous, Mariam Hassib and Yomna Abdel Rahman for their guidance, support and providing the necessary facilities and equipments to complete this project. en_US
dc.format.extent 94 p. en_US
dc.format.medium theses en_US
dc.language.iso en en_US
dc.rights Author retains all rights with regard to copyright. en
dc.subject EMG en_US
dc.subject Electromyography en_US
dc.subject Eye Gesture en_US
dc.subject Blink en_US
dc.subject Wink en_US
dc.subject Squint en_US
dc.subject Frown en_US
dc.subject MUSE en_US
dc.subject Brain Computer Interface en_US
dc.subject BCI en_US
dc.subject Dynamic Sliding Window en_US
dc.subject Packet Segmentation en_US
dc.subject Threshold Algorithm en_US
dc.subject Hands Free Interaction en_US
dc.subject Augmented Reality en_US
dc.subject Firefighters en_US
dc.subject.lcsh Thesis (M.S.)--American University in Cairo en_US
dc.title EMG-based eye gestures recognition for hands free interfacing en_US
dc.type Text en_US
dc.subject.discipline Robotics, Control and Smart Systems en_US
dc.rights.access This item is restricted for 2 years from the date issued en_US
dc.contributor.department American University in Cairo. School of Engineering Interdisciplinary Program en_US
dc.description.irb American University in Cairo Institutional Review Board approval has been obtained for this item. en_US
dc.contributor.committeeMember Rafea, Ahmed
dc.contributor.committeeMember Eldawlatly, Seif
dc.contributor.committeeMember El-Morsi, Mohamed


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  • Theses and Dissertations [1776]
    This collection includes theses and dissertations authored by American University in Cairo graduate students.

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