Medical Technologies Journal, Volume: 3, Issue: 1, January-March 2019, Pages: 316-333. Doi : https://doi.org/10.26415/2572-004X-vol3iss1p316-333 316 On Assisted Living of Paralyzed Persons through Real-Time Eye Features Tracking and Classification using Support Vector Machines Type of article: Original Qurban A Memon Associate Professor EE department, College of Engineering, UAE University, 15551, Al-Ain Abstract Background: The eye features like eye-blink and eyeball movements can be used as a module in assisted living systems that allow a class of physically challenged people speaks – using their eyes. The objective of this work is to design a real-time customized keyboard to be used by a physically challenged person to speak to the outside world, for example, to enable a computer to read a story or a document, do gaming and exercise of nerves, etc., through eye features tracking Method: In a paralyzed person environment, the right-left, up-down eyeball movements act like a scroll and eye blink as a nod. The eye features are tracked using Support Vector Machines (SVMs). Results: A prototype keyboard is custom-designed to work with eye-blink detection and eyeball- movement tracking using Support Vector Machines (SVMs) and tested in a typical paralyzed person-environment under varied lighting conditions. Tests performed on male and female subjects of different ages showed results with a success rate of 92%. Conclusions: Since the system needs about 2 seconds to process one command, real-time use is not required. The efficiency can be improved through the use of a depth sensor camera, faster processor environment, or motion estimation. Keywords: Assisted living; Rehabilitation; Paralyzed persons; Eye-blink detection; Eyeball detection; Biomedical engineering; SVM; Machine learning; Image processing. Corresponding author: Qurban A Memon, EE department, College of Engineering, UAE University, 15551, Al-Ain qurban.memon@uaeu.ac.ae Received: 26 January, 2019, Accepted: 28 Mars, 2019, English editing: 04 Mars, 2019,Published: 01 April, 2019. Screened by iThenticate..©2017-2019 KNOWLEDGE KINGDOM PUBLISHING. 1. Introduction Human feature detection and tracking are gaining more importance each day due to a wide variety of applications that can be built. One application is constructing interactive ways to communicate with Internet-enabled devices linked to people with disabilities [1]. Commuting and communication are the main issues of these patients. One such class of people with Tetra/quadriplegia face even communication difficulties. Another class has rehabilitative disabilities (spinal cord injury, repetitive strain injury, etc.) and motor disabilities (autism, cerebral palsy, Lou Gehrig's, and so forth). Historically, techniques like Partner-Assisted Scanning (PAS) have been used to help these people communicate. In this technique, the nurse/caregiver presents a set of symbols (e.g., words, alphabets, pictures, letters) on a screen to the disabled patient, observes the patient’s eye on the screen, and then determines selection from among those symbols to express needs. Augmentative and Alternative Communication (AAC) is a very general term and is diversified into two types; aided and unaided systems [2]. In aided