<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="1.1d1" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher">REMEDIUM</journal-id><journal-title-group><journal-title>REMEDIUM</journal-title></journal-title-group><issn publication-format="print">1561-5936</issn><issn publication-format="electronic">2658-3534</issn><publisher><publisher-name>Joint-Stock Company Chicot</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">1869</article-id><article-id pub-id-type="doi">10.32687/1561-5936-2026-30-1-9-14</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>Modern technologies, including those utilizing artificial intelligence, for monitoring the health status of professional drivers and ensuring driving safety</article-title></title-group><contrib-group><contrib contrib-type="author"><name name-style="western"><surname>Mingazova</surname><given-names>Elmira N.</given-names></name><bio></bio><email>elmira_mingazova@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Muslimov</surname><given-names>Muslim I.</given-names></name><bio></bio><email>office@auz.clinic</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Chinilov</surname><given-names>Sergey S.</given-names></name><bio></bio><email>chinilovss@yandex.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff id="aff-1">N. A. Semashko National Research Institute of Public Health, Moscow, Russia, Kazan State Medical University, Kazan, Russia</aff><aff id="aff-2">National Association of Healthcare Managers, Moscow, Russia</aff><pub-date date-type="epub" iso-8601-date="2026-03-30" publication-format="electronic"><day>30</day><month>03</month><year>2026</year></pub-date><volume>30</volume><issue>1</issue><fpage>9</fpage><lpage>14</lpage><history><pub-date date-type="received" iso-8601-date="2026-03-23"><day>23</day><month>03</month><year>2026</year></pub-date></history><permissions><copyright-statement>Copyright © 2026,</copyright-statement><copyright-year>2026</copyright-year></permissions><abstract>Currently, there is a critical need for eHealth technologies capable of enhancing driving safety by accurately assessing a driver’s functional state through regular health screening and monitoring, visual observation, and analysis of vehicle behaviour. Leveraging contemporary technological capabilities, Russian scientists and developers have introduced an innovative system for remote pre-trip medical examinations of professional drivers. This system integrates hardware and software components that automatically record physiological parameters, capture visual examination data, and transmit this information to a medical centre in real time. The outcome is an electronic document that holds the legal validity of a medical certificate. A key advantage of this technology is its speed: the entire examination cycle takes no more than 2—3 minutes. Moreover, the solution demonstrates substantial economic efficiency through significant optimisation of medical personnel utilisation. A single remotely operating medical professional can perform up to 100 examinations per hour. This innovative approach entirely eliminates superficial procedures limited solely to alcohol testing, thereby ensuring a comprehensive health assessment for every driver. The system’s capacity enables the processing of up to 30,000 examinations per day, forming the foundation for a large-scale medical database. Analysis of this data facilitates early detection of occupational diseases, timely adjustment of therapeutic and preventive interventions, and a reduction in the risk of road traffic incidents.</abstract><kwd-group xml:lang="en"><kwd>monitoring, technologies, health status, occupational risks, drivers' health, safety, artificial intelligence</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>мониторинг, технологии, состояние здоровья, профессиональные риски, здоровье водителей, безопасность, искусственный интеллект</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>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</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>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</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>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</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>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</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>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</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>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</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>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</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>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</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>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</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>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</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Dementienko V. V. Devices for monitoring and maintaining driver performance: tasks and solutions. Road Traffic Safety. 2020;(20):70—74.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>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</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Stadin M. R., Asplund S., Nyman T., et al. Managers&amp;apos; and safety representatives&amp;apos; 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</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>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</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>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</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>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</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>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</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>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</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>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</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>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</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>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</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>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</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>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</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>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</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>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</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>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</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>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</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>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</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>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</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>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</mixed-citation></ref></ref-list></back></article>
