# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 4 Discussion
### Key Findings  
This review synthesizes findings from 40 peer-reviewed studies investigating the use of monitoring technologies to improve truck driver safety. Across the literature, a wide range of technologies was employed to monitor physiological, behavioral, and environmental indicators associated with risky driving behaviors and safety outcomes.  
Wearable devices, such as wristbands and physiological sensors, were frequently used to detect fatigue and stress through measures including heart rate variability, electrodermal activity, and blink rate (Section 3.3.1). These systems enabled early detection of driver impairment and offered non-intrusive alternatives to visual monitoring, particularly valuable in low-light or high-distraction environments.  
In-vehicle monitoring systems and telematics platforms were widely applied to track behavioral indicators such as speeding, abrupt braking, and unsafe lane changes (Section 3.3.2). When combined with real-time feedback mechanisms, these systems were associated with measurable reductions in risky behaviors. The integration of GPS and telemetric data also enabled the classification of driving styles and the identification of high-risk operational patterns.  
Vision-based systems were used to monitor distraction and fatigue through facial analysis, eye movement, and head position (Section 3.3.3). While these systems offered detailed driver-state insights, their performance was often affected by environmental factors such as poor lighting, glare, or sensor occlusion.  
Advanced driver assistance systems, including forward collision warning and lane departure warning functions, were also examined (Section 3.3.4). Some studies applied machine learning to personalize alert thresholds, which helped reduce false alarms and improve alignment with individual driver behavior. These results highlight the benefit of adapting system outputs to context-specific risk factors.  
Data logging and event recording systems, such as ELDs and EDRs, supported driver safety by enabling compliance monitoring and post-trip behavior evaluation (Section 3.3.5).  
Studies reported reductions in hours-of-service violations, fatigue, and speeding events, particularly when these technologies were combined with structured feedback and training. However, some unintended effects, such as the displacement of risky behaviors to unmonitored driving contexts, were also observed.  
Connectivity and communication systems, including V2V and V2I technologies, facilitated real-time safety communication (Section 3.3.6). These systems supported applications such as hazard warnings, cooperative overtaking, and variable speed limit advisories. While drivers generally responded positively to connected vehicle alerts, limitations in system usability, interface design, and reliability under mixed-traffic conditions were noted.  
The technologies reviewed monitored a broad array of variables (Section 3.4). Physiological measures included heart rate, skin conductance, and eye behavior to assess fatigue and stress. Behavioral variables, such as lane position, speed variation, braking intensity, and off-road glances, were key indicators of driver risk. Environmental factors such as road type, traffic conditions, and weather were also incorporated to improve the contextual relevance and accuracy of safety assessments. Together, these dimensions enabled a more holistic understanding of driver state and behavior.  
Despite these promising outcomes, several challenges persist. As illustrated in Figure 3, most studies were conducted in high-income countries, limiting the global applicability of results. In addition, high implementation costs, data privacy concerns, limited system adaptability, and insufficient sample diversity were consistently identified across studies. These factors affect both the scalability and acceptability of monitoring technologies in real-world operations.