  Numerous studies have established the necessity for developing safety
equipment to detect drowsiness among vehicle drivers. However, for reliable
implementations, such systems must employ dependable sources of stimuli;
through Electrooculography (EOG), the tendencies of drowsiness can be directly
sensed by measuring blinks of prolonged durations. While conventional machine
learning (ML) algorithms can be utilized for the detection and classification
of these prolonged blinks (PB), executing them on microcontroller units (MCU)
may prove to be a laborious task. Hence, by keeping resource constraints and
practicality in mind, an ML algorithm is proposed in this study to identify PBs
executed by an individual with desirable accuracy and precision while being
efficient enough to be deployed on portable wearables using economic MCUs.
Furthermore, the suggested algorithm is subjected to multiple rounds of testing
in this study thereby, establishing its possibility as a feasible drowsiness
detection measure for wearable systems.
