# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 4 Discussion
### Future Research  
Future research should address the gaps and limitations identified in this review to strengthen the evidence base for monitoring technologies. Longitudinal studies with larger and more diverse populations are needed to evaluate the long-term impacts of these systems on driver behavior and safety outcomes. Additionally, innovative, non-invasive monitoring methods, such as non-visual fatigue detection systems and wearable devices equipped with physiological sensors (e.g., heart rate variability, skin conductance, or eye-tracking capabilities), should be further explored to offer privacy-preserving and user-friendly solutions.  
Developing standardized metrics and methodologies for evaluating monitoring technologies is essential to enable robust comparisons and identify best practices. Comparative studies assessing cost-effectiveness, scalability, and long-term feasibility will provide valuable insights for stakeholders. In particular, future investigations should explore affordable and scalable solutions, including the use of commercially available devices (e.g., smartphones or smartwatches), open-source platforms, and cloud-based data processing, all of which can reduce hardware demands and total cost of ownership.  
Moreover, research into policy frameworks and financial mechanisms, such as public-private partnerships, government subsidies, tax incentives, or insurance discounts linked to safety technology adoption, could inform strategies to overcome financial barriers, especially for small and medium-sized enterprises. Collaborative models, including shared service platforms and subscription-based implementations, may also offer practical alternatives in cost-sensitive settings.  
In addition, future work should explore the integration of multiple and diverse data sources, such as facial fatigue indicators, cocktail audio, physiological signals, and vehicle telemetry, to support more comprehensive and context-aware assessments of driving behavior. Combining these complementary inputs can provide a richer understanding of both driver state and environmental context, which is essential for accurately identifying risk patterns. Adaptive algorithms, particularly those based on deep learning techniques, offer strong potential for processing such complex data and dynamically detecting risky behaviors. These approaches can enhance system responsiveness, improve behavioral sensitivity, and support the development of individualized, data-driven safety interventions.  
Beyond technical and economic considerations, future studies should also examine the broader human and organizational implications of monitoring technologies. This includes evaluating their impact on driver well-being, stress levels, and job satisfaction, which are essential for long-term adoption and behavioral change. Addressing these research priorities will contribute to optimizing the design and implementation of monitoring systems and promoting their effective integration into the trucking industry, ultimately supporting safer roads and improved outcomes for all stakeholders.