Real time drowsiness detection using eye blink monitoring

Rahman, Amna; Sirshar, Mehreen; Khan, Amir Azam

doi:10.1109/nsec.2015.7396336

Cited by 78 publications

(37 citation statements)

References 5 publications

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“…For these reasons, the test of the proposed system is performed on simulated events, with a combination of behavioural and physiological symptoms of the drowsiness. This approach has already been validated by previous works, and demonstrated to be an effective and reliable way to test drowsiness detection [67,66,68,69].…”

Section: Methodsmentioning

confidence: 72%

A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems

et al. 2018

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Drowsiness detection mechanisms have been extensively studied in the last years since they are one of the prevalent causes of accidents within the mining, driving and industrial activities. Many research efforts were done to quantify the drowsiness levels using behavioral analyses based on camera eye tracking systems as well as by analyzing physiological features contained in EEG signals. Detection systems typically use specific drowsiness indicators from only one of these methods, leaving a risk of missed detection since not all the population presents same symptoms of drowsiness [1]. Thus, multi-feature systems are preferable even though most of the current State-of-the-Art (SoA) solutions are based on power-hungry platforms and they have meager chance to be used in embedded wearable applications with long battery lifetime. This work presents a drowsiness detection scheme fusing behavioral information coming from user motion through an IMU sensor and physiological information coming from brain activity through a single EEG electrode. The solution is implemented and tested on a low power programmable platform based on an ARM Cortex-M4 microcontroller, resulting in a wearable device capable to detect 5 different levels of drowsiness with an average accuracy of 95.2% and a battery life of 6 hours, using a 200mAh battery. We also study the energy optimization achievable by accelerating the sensor fusion-based drowsiness detector on a parallel ultra-low power (PULP) platform. Results show that the use of PULP as efficient processing platform provides an energy improvement of 63x with respect to a solution based on a commercial microcontroller. This may extend the battery life of the complete system up to 46 hours with a 7x improvement, paving the way for a completely wearable, always-on system.

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Section: Methodsmentioning

confidence: 72%

A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems

et al. 2018

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“…The behavioral and physiological features used on this work allowed the validation of the system using simulated conditions. This approach has the advantage of not exposing the subject to dangerous conditions and has been used extensively in other researches [28] [26]. For this purpose, 7 test subjects have participated.…”

Section: A System Validationmentioning

confidence: 99%

“…Many of the presented approaches are obtained in laboratory conditions in offline mode [17][18] [29], while the proposed implementation is fully online, wearable and tested in a real environment. Moreover, by using the current technique for the blinking duration is possible to replace or complement computer vision methods [26][9] with a significant reduction in energy consumption. Concerning other wearable approaches, [27] introduced a low power device capable to operate for 5 hours with a 200mAh battery.…”

Section: A System Validationmentioning

confidence: 99%

A wearable EEG-based drowsiness detection system with blink duration and alpha waves analysis

Kartsch

Benatti

Rossi

et al. 2017

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…Detection of physiological signs of drowsiness and fatigue is carried out via measurements of the heartbeat rate, brain activity, eye blinking and closure. Eye behavior can be observed with the help of electrooculogram (EOG) [9,10], with eye tracker, such as in researches presented in [11][12][13], based on eye image video analysis [14], etc. A lot of research has been done for exploration of brain activity of sleepy and drowsy drivers, some of the latest are [15][16][17][18][19].…”

Section: Definition Of Fatigued and Drowsy Statementioning

confidence: 99%

Subjective Methods for Assessment of Driver Drowsiness

Mashko

2017

APP

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Abstract. The paper deals with the issue of fatigue and sleepiness behind the wheel, which for a long time has been of vital importance for the research in the area of driver-car interaction safety. Numerous experiments on car simulators with diverse measurements to observe human behavior have been performed at the laboratories of the faculty of the authors. The paper provides analysis and an overview and assessment of the subjective (self-rating and observer rating) methods for observation of driver behavior and the detection of critical behavior in sleep deprived drivers using the developed subjective rating scales.

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Real time drowsiness detection using eye blink monitoring

Cited by 78 publications

References 5 publications

A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems

A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems

A wearable EEG-based drowsiness detection system with blink duration and alpha waves analysis

Subjective Methods for Assessment of Driver Drowsiness

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