# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Monitoring Technologies
#### 3.3.1 Wearable Monitoring Devices  
Wearable monitoring devices have shown substantial potential in monitoring physiological and behavioral parameters to enhance safety for professional truck drivers. [PERSON] et al. [16] explored a multimodal approach combining electroencephalographic (EEG), electrocoulographic (EOG), photoplethysmography (PPG), and electrodermal activity to monitor fatigue in professional drivers during a simulated driving task. Their findings highlighted brain activity as the most sensitive and immediate indicator of fatigue onset, followed by ocular parameters, which exhibited delayed effects. This study demonstrates the effectiveness of wearable devices in capturing critical physiological changes that sig nal mental fatigue, thus providing essential inputs for real-time interventions among truck drivers.  
Similarly, [PERSON] et al. [17] utilized wearable devices to analyze heart rate variability (HRV) for predicting collision risks associated with driver fatigue. Their research employed autonomic nerve function (ANF) indices derived from HRV data to develop a novel model that predicts collision risks within a 30 min window. With an accuracy of 74.9%, the system demonstrated the feasibility of leveraging physiological data from wearable devices to anticipate fatigue-induced risks, allowing for timely and targeted safety interventions. The study also highlighted the importance of integrating physiological data with external variables, such as speed and road conditions, to improve prediction accuracy.  
[PERSON] et al. [18] further extended this line of research by examining the relationship between physiological conditions, such as stress-induced fatigue, and rear-end collision risk. Their study used autonomic nerve function indices, monitored continuously during real-world truck driving conditions, to reveal that acute stress increased rear-end collision risks by exacerbating sympathetic nerve activity and inhibiting parasympathetic responses. The findings underscore the critical role of wearable devices in capturing these physiological states, enabling the development of systems that warn drivers or automatically adjust vehicle behavior to mitigate risks.  
Despite these promising findings, wearable monitoring devices face several limitations that could hinder their effectiveness. Environmental factors, such as extreme temperatures, vibrations, or poor lighting conditions, can affect the accuracy and reliability of physiological data collection. For example, photoplethysmography sensors used for HRV monitoring are sensitive to motion artifacts or improper placement, which may introduce noise into the data. Privacy concerns are another significant barrier, as continuous monitoring of physiological data may be perceived as intrusive by drivers, potentially leading to resistance or non-compliance. Additionally, drivers may find wearable devices uncomfortable or burdensome during long-haul trips, reducing consistent usage and undermining their overall effectiveness. Finally, while wearable devices can detect physiological changes indicative of fatigue or stress, their practical utility relies heavily on integration with external systems and effective calibration to provide actionable insights tailored to individual driver conditions.