Shufang Huang's scientific contributions
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Publications (3)
The driver is one of the most important factors in the safety of the transportation system. The driver’s perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver’...
The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation sy...
Train drivers’ inattention, including fatigue and distraction, impairs their ability to drive and is the major risk factor for human-caused train accidents. Many experts have undertaken numerous studies on train driver exhaustion and distraction, but a systematic study is still missing. Through a systematic review, this work aims to outline the typ...
Citations
... The study's primary goals are to accurately detect fatigue, develop standardized measures, and evaluate systems under realistic driving conditions [34]. Study [35] proposes a wide range of solutions, such as the creation of comprehensive datasets, the improvement of data aggregation methods, the development of real-time detection capabilities, and the design of low-cost detection environments. ...
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... Kolus et al. (2016) have proposed an ANFIS-based method to classify work rates based on factors that account for inter-participant variability and are easily evaluated in real-world settings. Recently, Escobar-Linero et al. (2022) contributed to the understanding of the relationship between physical activity and fatigue and provide a valuable tool for estimating fatigue levels in 10-min intervals using a neural network-based classifier system. ...
... Firstly, in [92], researchers from China have found a deep learning technique: by extracting features related to upper body posture, including the head, neck, chest, shoulders, and arms, from images captured of train drivers to detect drivers' fatigue level. In [93], the researchers from Beijing Jiaoton University introduced a method for detecting the fatigue state of drivers by analyzing their upper body postures extracted by Open-Pose framework and a Deep Belief Network -Back Propagation Neural Network ("DBN-BPNN") model. ...
