Attention Makers

CATEGORY: HEALTHCARE

A low-cost wearable eye tracking device for detecting the warning signs of fatigued and drowsy driving with mobile app for fatigue scale to take some preventive actions

MAKERS: Abdelkader Nasreddine Belkacem

Sorry Google; Sorry Tobii.. No one wants to wear your glasses anymore. Our new eye tracking device can be useful in several fields and suitable for daily life applications. One of its important applications is for detecting the warning signs of fatigued and drowsy driving with mobile phone application for fatigue scale to take some preventive actions. We tried to solve fatigue issue by predicting the drowsiness status a few hours or minutes before falling asleep with small device around the ears (using some psychological questions and EEG/EOG signal analysis) and giving the drivers some tips (such as display their degree of drowsiness, take a coffee break, wash your face, take a rest for 10 min... Etc.) via mobile phone and make them in alert situation (by adding some light indicators and speaker on our device) during their journey until they arrive safe. PS: The car driver doesn't need to look every time on his/her smart phone. because the smart phone informs the user by providing auditory/visual/haptic warning messages. The proposed device can help not only drivers but also workers and students by checking their decision-making and concentration levels. It can be used with other software for controlling wheelchair, moving a cursor, and playing a video games as well. For proof of concept, please check the video demonstration by clicking on the following link: https://www.youtube.com/watch?v=eyDQf0_-AR8 References: 1. A. N. Belkacem et al. Real-time Control of a Video Game using Eye Movements and Two Temporal EEG Sensors., Computational Intelligence and Neuroscience, (2015). 2. A. N. Belkacem et al. Online Classification Algorithm for Eye-movement-based Communication Systems Using Two Temporal EEG Sensors., Biomedical Signal Processing and Control, (2015), 16: 40-47. 3. A. N. Belkacem et al. Classification of Four Eye directions from EEG Signals for Eye-movement-based Communication Systems., Journal of Medical and Biological Engineering, (2014), 34(6): 581-588.

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