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https://www.um.edu.mt/library/oar/handle/123456789/115296
Title: | HMM-based gesture recognition for eye-swipe typing |
Authors: | Mifsud, Matthew Camilleri, Tracey A. Camilleri, Kenneth P. |
Keywords: | Eye -- Movements Gesture recognition (Computer science) Eye tracking Hidden Markov models |
Issue Date: | 2023 |
Publisher: | Elsevier BV |
Citation: | Mifsud, M., Camilleri, T. A. & Camilleri, K. P. (2023). HMM-based gesture recognition for eye-swipe typing. Biomedical Signal Processing and Control, 86(Part A), 105161. |
Abstract: | Eye-swipe typing requires users to simply look in the vicinity of the keys forming the desired word, similar to swiping their finger on a touch screen device. This work presents a novel HMM-based approach for swipe typing which can be used with any eye movement recording technique. In this study, three different HMMbased methods are developed, tested, and compared to the state-of-the-art performing LCSMapping algorithm with eye movement data acquired from the electrooculogram (EOG). When tested by ten subjects, the top performing Key-based HMM yielded an average top-five rate of 91.00±6.63% in comparison to an average top-five rate of 76.00±12.61% achieved by the LCSMapping algorithm. This study also presents the analysis of a real-time eye controlled swipe typing application which yielded an average typing speed of 12.85±2.14 WPM in contrast to an average typing speed of 3.83±1.24 WPM achieved using a dwell-based alternative. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/115296 |
Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
File | Description | Size | Format | |
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2023 Mifsud - HMM based Gesture recognition for eye-swipe typing.pdf Restricted Access | 1.45 MB | Adobe PDF | View/Open Request a copy |
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