Article
Biosignal-Based Driving Skill Classification Using Machine
Learning: A Case Study of Maritime Navigation
Hui Xue
1,
* , Bjørn-Morten Batalden
1
, Puneet Sharma
2
, Jarle André Johansen
2
and Dilip K. Prasad
3
Citation: Xue, H.; Batalden, B.-M.;
Sharma, P.; Johansen, J.A.; Prasad,
D.K. Biosignal-Based Driving Skill
Classification Using Machine
Learning: A Case Study of Maritime
Navigation. Appl. Sci. 2021, 11, 9765.
https://doi.org/
10.3390/app11209765
Academic Editors: YangQuan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, M. Jamal Deen, Junseop
Lee and Simone Morais
Received: 26 September 2021
Accepted: 15 October 2021
Published: 19 October 2021
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4.0/).
1
Department of Technology and Safety, UiT The Arctic University of Norway, 9019 Tromsø, Norway;
bjorn.batalden@uit.no
2
Department of Automation and Processing Technology, UiT The Arctic University of Norway,
9019 Tromsø, Norway; puneet.sharma@uit.no (P.S.); jarle.johansen@uit.no (J.A.J.)
3
Department of Computer Science, UiT The Arctic University of Norway, 9019 Tromsø, Norway;
dilip.prasad@uit.no
* Correspondence: hui.xue@uit.no
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.
Keywords:
biosignal; maritime navigation; classification; situation awareness (SA); neural network;
maritime training
1. Introduction
The classic methodology for maritime training generally involves multiple sensors
in addition to simulator for improving situation awareness (SA) in maritime navigation
and seafaring skills [
1
]. The purpose of this study is to determine if a wearable sensor
can be used to detect stress changes with skills during a maritime navigation task. We
define stress as the task requirement for both experienced seafarers (experts) and novices
(students). We collected the biosignal data of subjects for indicating the stress differences
under the SA tasks during maritime navigation. Biosignal data including electrodermal
activity (EDA), body temperature, blood volume pulse (BVP) and heart rate (HR) are some
of the indicators to present the stress level, since stress is the body’s reaction to pressure
and a physical response to situations in which people feel threatened.
Safe maritime navigation in the Arctic region is challenging because there is less
infrastructure, long distances between harbours and harsh weather conditions [
2
]. How-
ever, the safety of the Arctic route is of great significance to the economic development
of Scandinavia, and at the same time has an impact on environmental protection and the
safe growth of marine life. The sailing route in the Vessel Traffic Services area on the west
coast of Norway, north of Bergen, is a typical route for training the seafarers because of
its complexity and busy traffic, especially for training SA in the safety of the maritime
navigation.
In maritime, the study of situation awareness (SA) has always been an important
topic of discussion. Studies show that many ship collisions and groundings occur due
to navigators’ erroneous SA. Grech et al. [
3
] found that 71% of navigators’ errors can be
Appl. Sci. 2021, 11, 9765. https://doi.org/10.3390/app11209765 https://www.mdpi.com/journal/applsci
