An intelligent framework for detection of fatigue induced by sleep-deprivation

Abstract

 Most of the people who do not take required sleep are prone to sleep-deprived mental fatigue. This mental fatigue due to sleep deprivation is very harmful to persons involved in critical jobs like Pilots, Surgeons, Air traffic controllers and others. The present research paper proposes an intelligent method based on re-enforced learning, followed by classification supported by the adaptive threshold. Moreover, the method proposed by us is non-intrusive, in which the subject is unaware of being monitored during the test; it helps prevent biased results. The novelty lies in the use of the Inter-frame interval of an open and close eye for feature extraction that leads to the detection of “Alertness” or “Fatigue” based on the adaptive threshold. The proposed self-learning framework is real-time in nature and has a detection accuracy of 97.5 %. Since the method is self-learning, as the size of the data set increases, its accuracy and sensitivity are likely to increase further.