Design and Testing of a Wearable System for Monitoring Car Drivers
Source abstract
Abstract
The increasing concern over driver safety has led to the development of various monitoring systems aimed at assessing the physiological state of drivers. This paper presents the design development and testing of a wearable system that monitors key physiological indicators such as heart rate, skin conductance, and movement to evaluate whether the driver’s behavior is aggressive. The system integrates multiple sensors to provide real-time data on the driver’s state. Our objective is to enhance driver safety by detecting aggressive driving behavior through continuous monitoring. This paper covers the conceptual design, performance analysis, and testing layout of the system. Results indicate the system’s effectiveness in detecting changes in physiological states, suggesting its potential for improving road safety and its potential for practical implementation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sleep and Work-Related Fatigue · Autonomous Vehicle Technology and Safety · Ergonomics and Musculoskeletal Disorders
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
SeaTech, Engineering School, University of Toulon, Av. de l’Université, 83130 La Garde, France
Department of Industrial Engineering, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy
References (26)
Show source-provided references
1. Influences of the Physical Parameters on the Risk to Neck Injuries in Low Impact Speed Rear-end Collisions10.1016/0001-4575(96)00019-X ↗
2. Garrosa, M., Ceccarelli, M., Diaz, V., and Russo, M. (2023). Experimental Validation of a Driver Monitoring System. Machines, 11.10.3390/machines11121060 ↗
3. Preventing Minor Neck Injuries in Rear Crashes—Forty Years of Progress10.1097/JOM.0b013e3181bb777c ↗
4. A Comparison of Biomechanical Mechanisms of Whiplash Injury from Rear Impacts. Annual proceedings
5. Effects of Seatback Recline and Belt Restraint Type on PMHS Responses and Injuries in Rear-Facing Frontal Impacts
6. Problems and Requirements in Impact Analysis from Vehicle Accidents
7. Laribi, M.-A., Ceccarelli, M., Nelson Carl, A., and Zeghloul, S. (2023). Torso Motion Monitoring with an IMU Set-Up. New Advances in Mechanisms, Transmissions and Applications: Proceedings of the Sixth MeTrApp Conference 2023, Springer.10.1007/978-3-031-29815-8 ↗
8. Lundell, B., Jakobsson, L., Alfredsson, B., Jernström, C., and Isaksson-Hellman, I. (1998). Guidelines for and the Design of a Car Seat Concept for Improved Protection Against Neck Injuries in Rear End Car Impacts, Springer. SAE Technical Paper 980301.10.4271/980301 ↗
9. Davoli, L., Mattioli, V., Gambetta, S., Belli, L., Carnevali, L., Martaló, M., Sgoifo, A., Raheli, R., and Ferrari, G. (2021). Non-Invasive Psycho- Physiological Driver Monitoring through IoT-Oriented Systems. The Internet of Medical Things: Enabling technologies and emerging applications. Healthc. Technol., 19–33.10.1049/PBHE034E_ch2 ↗
10. Driver state recognition with physiological signals Based on deep feature fusion and feature selection techniques10.1016/j.bspc.2024.106204 ↗
11. Begum, S. (2013, January 6–9). Intelligent driver monitoring systems based on physiological sensor signals: A review. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands.10.1109/ITSC.2013.6728246 ↗
12. Sriranga, A.-K., Lu, Q., and Birrell, S. (2023). A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring. Sensors, 23.10.3390/s23042214 ↗
13. Kim, D., Park, H., Kim, T., Kim, W., and Paik, J. (2023). Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition. Sci. Rep., 13.10.1038/s41598-023-44955-1 ↗
14. Garrosa, M., Russo, M., Ceccarelli, M., and Cafolla, D. (2023). Lab Experiences for a Driver Monitoring System. New Trends in Medical and Service Robotics, Springer.10.1007/978-3-031-32446-8_12 ↗
15. EDT: An EEG-based attention model for feature learning and depression recognition10.1016/j.bspc.2024.106182 ↗
16. Jeong, M., Kim, D., Park, S., and Paik, J. (2022, January 6–9). Drowsy status monitoring system: Based on face feature analysis. Proceedings of the 2022 International Conference on Electronics, Information, and Communication (ICEIC) 1–4 (IEEE, 2022), Jeju, Republic of Korea.10.1109/ICEIC54506.2022.9748269 ↗
17. Algorithm for drowsiness detection based on hybrid brain network parameter optimization10.1016/j.bspc.2024.106344 ↗
18. Wearable Mobile-Based Emotional Response-Monitoring System for Drivers10.1109/THMS.2017.2658442 ↗
19. SafeDriving: An Effective Abnormal Driving Behavior Detection System Based on EMG Signals10.1109/JIOT.2021.3135512 ↗
20. Khan, T., Choi, G., and Lee, S. (2023). EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism. Sensors, 23.10.3390/s23083835 ↗
21. Bonfati, L.V., Mendes Junior, J.J.A., Siqueira, H.V., and Stevan, S.L. (2023). Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors. Sensors, 23.10.2139/ssrn.4183382 ↗
22. Akiduki, T., Nagasawa, J., Zhang, Z., Omae, Y., Arakawa, T., and Takahashi, H. (2022). Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study. Sensors, 22.10.3390/s22010352 ↗
23. (2025, January 09). InvenSence, IMU Datasheet. Available online: https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf.
24. (2025, January 09). Arduino, Arduino 33 IoT Datasheet. Available online: https://content.arduino.cc/assets/st_imu_lsm6ds3_datasheet.pdf.
25. (2025, January 09). Analog, MAX30102 Datasheet. Available online: https://www.analog.com/media/en/technical-documentation/data-sheets/MAX30102.pdf.
26. (2025, January 09). Seeed Studion, Grove GSR Sensor Datasheet. Available online: https://wiki.seeedstudio.com/Grove-GSR_Sensor/.