Daytime drowsiness and fatigue lead to decreased driving reliability, lower working efficiency and fatal accidents. According to recent research, heart rate variability (HRV) can be robustly calculated from the photoplethysmogram (PPG) to indicate parasympathetic nervous activity and classify drowsiness level. In this paper, a low power wireless PPG sensor has been designed. N-back M-pitch, a working memory cognitive test has been used to correlate HRV, extracted from the new sensor, with mental fatigue, indicated by lower accuracy in the test. Signal processing algorithms have been designed, which are being implemented into real time software running on Intel Tunnel Creek Atom board, to function as the drowsiness control center.
Y. J. Xu et al., "Drowsiness control center by photoplythesmogram," 2012 38th Annual Northeast Bioengineering Conference (NEBEC), Philadelphia, PA, 2012, pp. 430-431, doi: 10.1109/NEBC.2012.6206925.
*denotes a WPI undergraduate student author