Современные технологии, в том числе с использованием искусственного интеллекта, в мониторинге состояния здоровья профессиональных водителей и обеспечении безопасности вождения
- Авторы: Мингазова Э.Н.1, Муслимов М.И.2, Чинилов С.С.2
- Учреждения:
- Национальный научно-исследовательский институт общественного здоровья имени Н. А. Семашко, г. Москва, Российская Федерация, ФГБОУ ВО «Казанский государственный медицинский университет», Казань, Россия
- Национальная ассоциация управленцев сферы здравоохранения, Москва, Россия
- Выпуск: № 1 (2026)
- Страницы: 9-14
- Раздел: Статьи
- URL: https://remedium-journal.ru/journal/article/view/1869
- DOI: https://doi.org/10.32687/1561-5936-2026-30-1-9-14
- Цитировать
Аннотация
Об авторах
Эльмира Нурисламовна Мингазова
Национальный научно-исследовательский институт общественного здоровья имени Н. А. Семашко, г. Москва, Российская Федерация, ФГБОУ ВО «Казанский государственный медицинский университет», Казань, Россия
Email: elmira_mingazova@mail.ru
Муслим Ильясович Муслимов
Национальная ассоциация управленцев сферы здравоохранения, Москва, Россия
Email: office@auz.clinic
Сергей Сергеевич Чинилов
Национальная ассоциация управленцев сферы здравоохранения, Москва, Россия
Email: chinilovss@yandex.ru
Список литературы
- Wang L., Song F., Zhou T. H., et al. EEG and ECG-based multi-sensor fusion computing for real-time fatigue driving recognition based on feedback mechanism. Sensors (Basel). 2023;23(20):8386. doi: 10.3390/s23208386
- Sawatari H., Kumagai H., Kawaguchi K., et al. Risk factors for collisions attributed to microsleep-related behaviors while driving in professional truck drivers. Sci Rep. 2024;14(1):6378. doi: 10.1038/s41598-024-57021-1
- McMahon J., Thompson D. R., Brazil K., Ski C. F. An eHealth intervention (ManGuard) to reduce cardiovascular disease risk in male taxi drivers: protocol for a feasibility randomised controlled trial. Pilot Feasibility Stud. 2022;8(1):209. doi: 10.1186/s40814-022-01163-4
- Ayas S., Donmez B., Tang X. Drowsiness mitigation through driver state monitoring systems: a scoping review. Hum Factors. 2024;66(9):2218—2243. doi: 10.1177/00187208231208523
- Muslimov M. I., Chinilov S. S., Mingazova E. N. Transformations in the organization of pre-shift and pre-trip medical examinations in the Russian Federation based on the use of hardware and software systems. Health Care Manager. 2025;(12):131—138. doi: 10.21045/1811-0185-2025-12-131-138
- Muslimov M. I., Chinilov S. S., Mingazova E. N. On the need for medical support of professional drivers. Bulletin of the National Research Institute of Public Health named after N. A. Semashko. 2025;(4):71—74. doi: 10.69541/NRIPH.2025.04.011
- Fonseca T., Ferreira S. Monitoring technologies for truck drivers: a systematic review of safety and driving behavior. Appl Sci. 2025;15(12):6513. doi: 10.3390/app15126513
- Masello L., Sheehan B., Castignani G., et al. On the impact of advanced driver assistance systems on driving distraction and risky behaviour: an empirical analysis of Irish commercial drivers. Accid Anal Prev. 2023;183:106969. doi: 10.1016/j.aap.2023.106969
- Malik M., Sharma P., Punj G. K., et al. Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion. Sci Rep. 2025;15(1):8838. doi: 10.1038/s41598-025-89479-y
- Ryabikin A. A., Gavrilenko L. V. Significance and assessment of the psychophysiological state of a driver as a participant in a road traffic accident. Eurasian Advocacy. 2022;56(1):67. doi: 10.52068/2304—9839_2022_56_1_67
- Dementienko V. V. Devices for monitoring and maintaining driver performance: tasks and solutions. Road Traffic Safety. 2020;(20):70—74.
- Fadeeva S. A., Sitdikova I. D., Mingazova E. N., et al. Risk assessment as a criterion of environmental stress. Indo Am J Pharm Sci. 2018;5(9):9323—9327. doi: 10.5281/zenodo.1439332
- Stadin M. R., Asplund S., Nyman T., et al. Managers' and safety representatives' perspectives on electronic monitoring and occupational health in the transport and logistics industries in Sweden. Int J Occup Saf Ergon. 2025:32(1):250—258. doi: 10.1080/10803548.2025.2524991
- Liebherr M., Staab V., de Waard D. Classification of advanced driver assistance systems according to their impact on mental workload. Theor Issues Ergon Sci. 2024;26(3):332—348. doi: 10.1080/1463922X.2024.2443973
- Neumann T. Analysis of advanced driver-assistance systems for safe and comfortable driving of motor vehicles. Sensors (Basel). 2024;24(19):6223. doi: 10.3390/s24196223
- Bhargav M. Advanced driver assistance systems for driver health and fatigue monitoring using camera, biometric sensors, telematics and machine learning. SAE Technical Paper 2025-28-0195. 2025. doi: 10.4271/2025-28-0195
- Zhao W., Gong S., Zhao D., et al. Developing a new integrated advanced driver assistance system in a connected vehicle environment. Expert Syst Appl. 2024;238(Pt A):121733. doi: 10.1016/j.eswa.2023.121733
- Essahraui S., Lamaakal I., El Hamly I., et al. Real-time driver drowsiness detection using facial analysis and machine learning techniques. Sensors (Basel). 2025;25(3):812. doi: 10.3390/s25030812
- Wood J. M., Henry E., Kaye S.-A., et al. Exploring perceptions of advanced driver assistance systems (ADAS) in older drivers with age-related declines. Transp Res Part F Traffic Psychol Behav. 2024;100:419—430. doi: 10.1016/j.trf.2023.12.006
- Kumagai H., Tsuda H., Kawaguchi K., et al. Truck collisions attributed to falling asleep at the wheel in two commercial drivers prescribed oral appliance therapy for obstructive sleep apnea. J Clin Sleep Med. 2023;19(12):2117—2122. doi: 10.5664/jcsm.10758
- Zeng C., Zhang J., Su Y., et al. Driver fatigue detection using heart rate variability features from 2-minute electrocardiogram signals while accounting for sex differences. Sensors (Basel). 2024;24(13):4316. doi: 10.3390/s24134316
- Lu K., Sjörs Dahlman A., Karlsson J., Candefjord S. Detecting driver fatigue using heart rate variability: a systematic review. Accid Anal Prev. 2022;178:106830. doi: 10.1016/j.aap.2022.106830
- Abbas Q., Alsheddy A. Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis. Sensors (Basel). 2020;21(1):56. doi: 10.3390/s21010056
- Peivandi M., Ardabili S. Z., Sheykhivand S., Danishvar S. Deep learning for detecting multi-level driver fatigue using physiological signals: a comprehensive approach. Sensors (Basel). 2023;23(19):8171. doi: 10.3390/s23198171
- Cao S., Feng P., Kang W., et al. Optimized driver fatigue detection method using multimodal neural networks. Sci Rep. 2025;15(1):12240. doi: 10.1038/s41598-025-86709-1
- Rostamzadeh S., Abouhossein A., Vosoughi S., et al. Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads. Int J Occup Saf Ergon. 2024;30(1):252—263. doi: 10.1080/10803548.2023.2293391
- Hassan O. F., Ibrahim A. F., Gomaa A., et al. Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach. Sci Rep. 2025;15(1):17493. doi: 10.1038/s41598-025-02111-x
- Aravinth S. S., Nagamani G. M., Kumar C. K., et al. Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations. Sci Rep. 2025; 15(1):15840. doi: 10.1038/s41598-025-92701-6
- Pan H., Logan D. B., Stephens A. N., et al. Exploring the effect of driver drowsiness on takeover performance during automated driving: an updated literature review. Accid Anal Prev. 2025;216:108023. doi: 10.1016/j.aap.2025.108023
- Béquet A. J., Hidalgo-Muñoz A. R., Jallais C. Towards mindless stress regulation in advanced driver assistance systems: a systematic review. Front Psychol. 2020;11:609124. doi: 10.3389/fpsyg.2020.609124





