Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation
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https://hdl.handle.net/10037/23078Date
2021-10-19Type
Journal articleTidsskriftartikkel
Peer reviewed
Abstract
This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.
Is part of
Xue, H. (2023). Methods for enhanced learning using wearable technologies. A study of the maritime sector. (Doctoral thesis). https://hdl.handle.net/10037/31091.Publisher
MDPICitation
Xue H, Batalden B, Sharma P, Johansen JA, Prasad DK. Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation. Applied Sciences. 2021Metadata
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