# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 4 Discussion
### Strengths and Limitations  
This systematic review identified several strengths in the current body of research on monitoring technologies. The integration of naturalistic driving data and real-time monitoring allowed for detailed insights into driver behaviors under realistic conditions. Multi-faceted approaches, combining physiological, behavioral, and environmental data, proved particularly effective in capturing complex interactions contributing to safety risks. Advances in machine learning and artificial intelligence further enhanced the adaptability and precision of monitoring systems, allowing for personalized feedback tailored to specific drivers and contexts. However, this review also identified several limitations that constrain the generalizability and applicability of the findings. First, many studies were geographically concentrated in regions with advanced infrastructure and regulatory environments, limiting the transferability of findings to contexts with different road conditions, cultural attitudes, and policy frameworks. The underrepresentation of low- and middle-income regions, where truck driver safety is often a critical concern, highlights the need for broader geographic inclusion in future research. Second, small sample sizes and short study durations were common methodological limitations. These factors reduce statistical power and limit the ability to draw reliable conclusions about the long-term effectiveness of monitoring technologies. In addition, shorter study periods often fail to capture sustained behavioral changes or account for the possibility that drivers may become desensitized to monitoring systems over time. A further concern is the limited diversity of study samples, particularly in terms of age, gender, cultural background, geographic context, and driving experience, which restricts the adaptability of monitoring technologies to different user groups. Systems developed and validated on relatively homogeneous populations may not perform consistently across varied real-world scenarios. Addressing this issue requires greater emphasis on heterogeneous sampling strategies and cross-context validation. Third, high implementation costs remain a considerable obstacle, especially for small and medium-sized fleet operators. The deployment of advanced monitoring systems typically requires substantial upfront investment in hardware, software integration, and ongoing maintenance. These financial constraints can limit the adoption of safety-enhancing technologies, particularly in resource-constrained regions or among operators with limited access to funding, subsidies, or incentive mechanisms. Addressing this barrier will require targeted efforts to develop cost-efficient solutions, such as modular system design, open-source platforms, and scalable deployment models. Fourth, many monitoring systems, particularly those relying on vision-based sensors or physiological signal detection, are sensitive to external environmental conditions. Factors such as poor lighting, glare, precipitation, and road surface variability may distort data accuracy and compromise system reliability in real-world applications. These challenges highlight the need for more robust and adaptable system designs, as well as validation studies conducted under varied environmental scenarios to ensure consistent performance. Fifth, false alarms in systems such as FCW technologies emerged as a significant issue. High rates of false alerts can lead to driver frustration, reduced trust in the technology, and eventual disengagement. Improving algorithmic precision and ensuring that system responses align closely with actual driving conditions will be essential to enhancing usability and acceptance. Sixth, privacy concerns continue to present a substantial challenge, particularly in the case of video-based monitoring systems. Many drivers express discomfort with continuous surveillance, raising both ethical and legal issues surrounding data protection and consent.