Manufacture of A Device That Warns Drivers of A Heart Attack or Drowsiness
Abstract
Injuries resulting from accidental crashes are the 8th leading cause of global mortality.Sleepiness greatly undermines neurological function, creating a significant risk for road traffic accidents. This systematic review seeks to assess the relationship between driving fatigue and road incidents. Official data from the U.S. government indicates that drowsy driving is responsible for only 1%–2% of motor vehicle collisions; however, numerous studies suggest a more extensive issue. Our research investigates driver drowsiness prior to crashes, examining data from a thorough naturalistic driving study that monitored over 3,500 individuals using in-vehicle cameras and tracking equipment. By employing validated metrics based on the duration of eye closure, drowsiness was identified in 8.8%–9.5% of all crashes analyzed and in 10.6%–10.8% of those that resulted in significant damage or injury.It is essential to maintain driver focus and alertness.For longer journeys, it is advisable to have alternate drivers or to take regular breaks to reduce fatigue-related risks.Despite taking precautions, drivers frequently fall victim to drowsiness, resulting in serious consequences.Therefore, the implementation of smart features and devices to proactively alert drivers is necessary.With around 1.3 million traffic fatalities each year attributed to various factors, including driver fatigue and health problems, our device aims to reduce accidents by preventing operation by individuals who are sleep-deprived or medically unfit, thereby enhancing pedestrian safety and public trust in road travel.
References
M. A. Grandner, “Epidemiology of insufficient sleep and poor sleep quality,” in Sleep and Health, Elsevier, 2019, pp. 11–20. doi: 10.1016/b978-0-12-815373-4.00002-2.
P. Philip, “Excessive daytime sleepiness versus sleepiness at the wheel, the need to differentiate global from situational sleepiness to better predict sleep-related accidents,” Sleep, vol. 46, no. 11, Sep. 2023, doi: 10.1093/sleep/zsad231.
R. Sijabat, “The Association between Foreign Investment and Gross Domestic Product in Ten ASEAN Countries,” Economies, vol. 11, no. 7, p. 188, Jul. 2023, doi: 10.3390/economies11070188.
A. O. Adeoti, O. O. Desalu, J. O. Fadare, and T. Elebiyo, “Obstructive Sleep Apnea, Excessive Daytime Sleepiness, and Its Association With Road Traffic Accidents Among Nigerian Truck Drivers,” in B55. EXPLORING LUNG HEALTH ACROSS POPULATIONS, American Thoracic Society, Apr. 2024, pp. A3873–A3873. doi: 10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a3873.
K. Sadeghniiat-Haghighi, M. M. Nia, O. Aminian, and A. Esmaeeli, “Sleepiness, fatigue and road traffic accidents,” Sleep Med, vol. 14, p. e253, Dec. 2013, doi: 10.1016/j.sleep.2013.11.613.
F. H. Caryn and L. Rahadianti, “Driver Drowsiness Detection Based on Drivers’ Physical Behaviours: A Systematic Literature Review,” Computer Engineering and Applications Journal, vol. 10, no. 3, pp. 161–175, Oct. 2021, doi: 10.18495/comengapp.v10i3.381.
G. Bouchouras and K. Kotis, “Integrating Artificial Intelligence, Internet of Things, and Sensor-Based Technologies: A Systematic Review of Methodologies in Autism Spectrum Disorder Detection,” Algorithms, vol. 18, no. 1, p. 34, Jan. 2025, doi: 10.3390/a18010034.
N. Guttman and T. Lotan, “Driver-Monitoring Technologies Measure,” 2011, American Psychological Association (APA). doi: 10.1037/t34317-000.
E. Dolezalek, M. Farnan, and C.-H. Min, “Physiological Signal Monitoring System to Analyze Driver Attentiveness,” in 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), IEEE, Aug. 2021, pp. 635–638. doi: 10.1109/mwscas47672.2021.9531871.
L. Kalake, Y. Dong, W. Wan, and L. Hou, “Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras,” Sensors, vol. 22, no. 6, p. 2123, Mar. 2022, doi: 10.3390/s22062123.
M. Ong, “Emergency Medical Services in Singapore,” in Emergency Medical Service Systems: A Global Perspective, Jaypee Brothers Medical Publishers (P) Ltd., 2014, p. 7. doi: 10.5005/jp/books/12298_2.
S. Wang, “Factors related to user perceptions of artificial intelligence (AI)-based content moderation on social media,” Comput Human Behav, vol. 149, p. 107971, Dec. 2023, doi: 10.1016/j.chb.2023.107971.
C. Balasubrahmanyan, A. Akbar Badusha, and S. Viswanatham, “Quantification of Alertness and Evaluation Method for Vision Based Driver Drowsiness and Alertness Warning System,” in SAE Technical Paper Series, in SIAT. SAE International, Jan. 2024. doi: 10.4271/2024-26-0021.
I. R. Nair, “A Survey on Driver Fatigue-Drowsiness Detection System,” International Journal Of Engineering And Computer Science, Nov. 2016, doi: 10.18535/ijecs/v5i11.92.
S. R. Tunis and C. Turkelson, “Using Health Technology Assessment to Identify Gaps in Evidence and Inform Study Design for Comparative Effectiveness Research,” Journal of Clinical Oncology, vol. 30, no. 34, pp. 4256–4261, Dec. 2012, doi: 10.1200/jco.2012.42.6338.
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