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Drowsiness Detection by the Systems Dynamic Approach of Oculomotor Systems
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Author(s): Dabbu Suman (Osmania University, India), Malini Mudigonda (Osmania University, India), B. Ram Reddy (Apollo Institute of Medical Sciences, India)and Yashwanth Vyza (EPFL, Switzerland)
Copyright: 2022
Volume: 11
Issue: 1
Pages: 27
Source title:
International Journal of Biomedical and Clinical Engineering (IJBCE)
Editor(s)-in-Chief: Natarajan Sriraam (M.S. Ramaiah Institute of Technology, India)
DOI: 10.4018/IJBCE.295866
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Abstract
literature shows that Blink Rate, Blink Duration, and Percentage of Eye Closure (PERCLOS) are the indicators of drowsiness, but the quantification of these parameters, inter-individual differences, and scientific or the physiological validation of the results have not been addressed. This study attempts to resolve these problems by the systems dynamic approach by modelling the oculomotor system. Autoregressive model of the EOG blink signatures during active and drowsy states are used to approximate and model the system. The impulse response of the active blink signal shows under damped response with the damping ratio of 0.61-0.75, (p<0.0005), and Drowsy blink signal shows a critically damped behavior with the damping ratio of 1, (p<0.0005). It is Clinically correlated that the continuous bombarding of the neuronal impulses from the brain acts as the stimulus for the blink, Hence during the drowsy phase, the response of the Oculomotor system is sluggish (Damping Ratio is high) thus causing increased Blink duration.
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