STOPIA: A Wearable Sensor and IoT based Truck Driver Stress Monitoring System to Reduce Traffic Accidents

Abstract

Truck drivers are known to be a demanding profession as their workload affects their body condition. On the basis of these problems, it is necessary to take preventive measures to reduce material losses and casualties. Through the use of the literature review method, the authors reveal a visual representation of the prevailing trends in the existing body of research on how stress impacts truckers and stress control to minimize accidents. The representation of ongoing research and shortcomings in potential future investigations was then used as a reference in prototyping, the stages of which are presented in the form of flow charts. The results show that stress primarily causes driver fatigue, alongside various indicators that serve as benchmarks for identifying fatigue-related factors. It was also found that there is an undeniable link between stress and fatigue in truck drivers during work performance. As part of the research results, the authors propose a solution in the form of sensors and IoT-based wearable devices that can help measure stress indicators, namely STOPIA. With the implementation of STOPIA, it is hoped that accurate recognition of driver fatigue conditions can be achieved, reducing the risk of traffic accidents, and providing comfort and safety for truck drivers and the transportation industry. Thus, this technology has the potential to decrease accident risks, enhance driver management, and reduce accident-related costs in the transportation industry.