Detecting of eyeball movements for choosing menu in display monitor using height and sector percentage measurement approaches

The number of disable people increases each years. This growing phenomenon attracts attention, especially for many researcher in order to help people with disabilities. Generally, disable people has a problem to do an activity by himself, because their hand and feet can not to be used normally. Many developed technology’s with an aim of helping the disabilities. One of them is a wheelchair. It is the most common stuff that used for helping disabilities as a tool for mobilization. There are two types of wheelchair. The first is the manual wheelchair, operated by hand. The second is an electrical wheelchair, that operated by joystick or other electric device. Both types of wheelchairs still uses a hand for operating the navigation. Meanwhile, recently, the development of smart wheelchair technology has been built up by using a display monitor with several menus selection and caused a problem for selecting menu in display monitor for disable people with hand defects. Based on those problems, this research proposed a selection method of navigation menu in the smart-wheelchair by utilizing the movements of the eyeball as an alternative solution for persons with disabilities in handicapped hands by using Height Measurement Approach and Sector Percentage Measurement.

[1]  Xiaoming Liu,et al.  Face Alignment in Full Pose Range: A 3D Total Solution , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Garrison W. Cottrell,et al.  Color-to-Grayscale: Does the Method Matter in Image Recognition? , 2012, PloS one.

[3]  Peter Peer,et al.  Influence of segmentation on deep iris recognition performance , 2019, 2019 7th International Workshop on Biometrics and Forensics (IWBF).

[4]  Joanna Isabelle Olszewska.  Designing Transparent and Autonomous Intelligent Vision Systems , 2019, ICAART.

[5]  Haniza Yazid,et al.  Performance analysis of image thresholding: Otsu technique , 2018 .

[6]  N. Hollis,et al.  Prevalence of Disabilities and Health Care Access by Disability Status and Type Among Adults — United States, 2016 , 2018, MMWR. Morbidity and mortality weekly report.

[7]  G. Deng,et al.  An adaptive Gaussian filter for noise reduction and edge detection , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[8]  Nathalia Peixoto,et al.  Voice controlled wheelchairs: Fine control by humming , 2013, Comput. Methods Programs Biomed..

[9]  R. Posada-Gomez,et al.  A Hands Gesture System Of Control For An Intelligent Wheelchair , 2007, 2007 4th International Conference on Electrical and Electronics Engineering.

[10]  Fitri Utaminingrum,et al.  Eye State Recognition Using Multiple Methods for Applied to Control Smart Wheelchair , 2019, International Journal of Intelligent Engineering and Systems.

[11]  Hyung-Shik Jung,et al.  Development and evaluation of one-hand drivable manual wheelchair device for hemiplegic patients. , 2015, Applied ergonomics.

[12]  Vincent Vanhoucke,et al.  Improving the speed of neural networks on CPUs , 2011 .

[13]  Gowers Wr,et al.  The Movements of the Eyelids , 1879 .

[14]  Zeeshan Ali Haq,et al.  Eye-blink rate detection for fatigue determination , 2016, 2016 1st India International Conference on Information Processing (IICIP).

[15]  P. Godfrey-Faussett,et al.  Effects of genetic variability of Mycobacterium tuberculosis strains on the presentation of disease. , 2005, The Lancet. Infectious diseases.

[16]  Elcio Hideiti Shiguemori,et al.  A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery , 2016 .

[17]  Joanna Isabelle Olszewska.  Clock-Model-Assisted Agent's Spatial Navigation , 2017, ICAART.

[18]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Honghai Liu,et al.  Accurate Eye Center Localization via Hierarchical Adaptive Convolution , 2018, BMVC.

[20]  Chiharu Ishii,et al.  Navigation of an Electric Wheelchair Using Electromyograms, Electrooculograms, and Electroencephalograms , 2016 .

[21]  Fitri Utaminingrum,et al.  Triangle similarity approach for detecting eyeball movement , 2017, 2017 5th International Symposium on Computational and Business Intelligence (ISCBI).

[22]  Byung Cheol Song,et al.  Accurate Eye Pupil Localization Using Heterogeneous CNN Models , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[23]  Fitri Utaminingrum,et al.  Adaptive human tracking for smart wheelchair , 2017, 2017 5th International Symposium on Computational and Business Intelligence (ISCBI).

[24]  Joanna Isabelle Olszewska,et al.  iFR: Interactively Pose Corrected Face Recognition , 2016, BIOSIGNALS.