Medical Device for Communication with Neuromotor Disabled Patients

In some circumstances, with the aid of assistive technologies, patients having neuromotor disabilities can interact with virtual keyboards by triggering virtual switches or moving a cursor on a screen to select desired items. This paper presents an electronic medical device used in eye tracking systems that helps the neuromotor disabled patients to communicate with the clinicians through a device connected to a computer. The proposed device contains a custom designed electronic module used to acquire the electrooculography signals, an Arduino Leonardo microprocessor board for the signal acquisition and processing, and a PC running a software in form of a visual keyboard. Due to their affections, the neuromotor disabled patients can't speak or write, but present cognitive function and manifest their intentions by moving their eyeballs. The application running an Arduino detects the patients' actions by moving the eyeballs in four directions and selects a key from a virtual keyboard by using dwell time selection method. The proposed medical device has been implemented and tested on simulated patients and the performances in terms accuracy of the desired key selection have been presented.

[1]  Rajesh Singla,et al.  EOG and EMG based virtual keyboard: A brain-computer interface , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[2]  Chun-Liang Hsu,et al.  EOG-based Human-Computer Interface system development , 2010, Expert Syst. Appl..

[3]  Margrit Betke,et al.  Real Time Eye Tracking and Blink Detection with USB Cameras , 2005 .

[4]  Diane Pedrotty Bryant,et al.  Assistive Technology for People with Disabilities , 2002 .

[5]  Laura Chamberlain Eye Tracking Methodology; Theory and Practice , 2007 .

[6]  Suresh R. Norman,et al.  Interface and Control of Appliances by the Analysis of Electrooculography Signals , 2015 .

[7]  Alexandru Caranica,et al.  Communication between the Sensor Levels for Monitoring Subjects with Disabilities , 2009, 2009 Advanced Technologies for Enhanced Quality of Life.

[8]  Markus H. Muser,et al.  Comparing eye movements recorded by search coil and infrared eye tracking , 2007, Journal of Clinical Monitoring and Computing.

[9]  Yunyoung Nam,et al.  Low-Cost Infrared Video-Oculography for Measuring Rapid Eye Movements , 2017, MUE/FutureTech.

[10]  F. Ungureanu,et al.  A SURVEY OF EYE TRACKING METHODS AND APPLICATIONS , 2014 .

[11]  Andrew Tucker,et al.  Monitoring eye and eyelid movements by infrared reflectance oculography to measure drowsiness in drivers , 2007 .

[12]  Serkan Gurkan,et al.  Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard , 2010, IEEE Transactions on Instrumentation and Measurement.

[13]  M. Hnatiuc,et al.  Location of a Person by Means of Sensors' Network , 2010, 2010 Advanced Technologies for Enhancing Quality of Life.

[14]  Manuel Mazo,et al.  Wheelchair Guidance Strategies Using EOG , 2002, J. Intell. Robotic Syst..

[15]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.