# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 5 Conclusions  
This systematic review examines the role of monitoring technologies in improving truck driver safety, highlighting the diverse technologies, variables measured, and outcomes observed across the studies. The findings demonstrate that technologies such as wearable devices, in-vehicle cameras, telematics systems, and AI-driven analytics show promise in enhancing driver safety by monitoring fatigue, stress, distraction, and other critical driving behaviors. However, the effectiveness of these technologies varies, and challenges such as high implementation costs, privacy concerns, and driver acceptance remain significant barriers to widespread adoption.  
Despite these challenges, the reviewed studies underscore the potential of these technologies to reduce risky driving behaviors and improve overall safety outcomes. Theevidence presented offers valuable insights into current trends in truck driver safety and provides a foundation for future research focused on addressing the identified gaps and optimizing the deployment of monitoring technologies. Further investigation is needed to refine these technologies, ensure their practical applicability in real-world settings, and enhance their integration into comprehensive safety management systems for the trucking industry.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15126513/s1](https://www.mdpi.com/article/10.3390/app15126513/s1)]([https://www.mdpi.com/article/10.3390/app15126513/s1](https://www.mdpi.com/article/10.3390/app15126513/s1)), Document S1: PRISMA 2020 Checklist.  
Conceptualization, T.F. and S.F.; methodology, T.F.; validation, S.F.; formal analysis, T.F.; investigation, T.F.; resources, S.F.; data curation, T.F.; writing--original draft preparation, T.F.; writing--review and editing, S.F.; visualization, T.F and S.F.; supervision, S.F.; project administration, T.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to acknowledge the Doctoral Program in Occupational Safety and Health at the University of Porto for providing access to digital library resources, which enabled the retrieval of the studies included in this review. During the preparation of this study, the authors used ChatGPT (OpenAI, GPT-4, 2025) for the purposes of preliminary data extraction and text drafting. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ADASsAdvanced driver assistance systems ADHDAttention deficit hyperactivity disorder AI Artificial intelligence ANFAutonomic nerve function BSMBasic safety message C/VISsCamera/video imaging systems CDCrash data CVConnected vehicle CV-VSLConnected vehicle-based variable speed limit DMSDriver monitoring systems DSEDriving simulator experiment EDREvent data recorder EEGElectroencephalographic ELDElectronic logging device EOGElectrooculographic FCWForward collision warning GPSGlobal positioning system HMIHuman-machine interface HMWHeadway monitoring and warning HOSHours-of-service HRVHeart rate variability IVMSIn-vehicle monitoring systems