Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation

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.

Expert–Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level Attention Mechanism

“…Therefore, the classification of athletes into "expert" and "novice" is a fundamental methodology [1]. In recent years, the popularization of wearable devices, such as smartwatches and motion capture devices, has facilitated the acquisition of biometric data, and various methods for expert-novice level classification using biometric data have been proposed [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For example, Kuo et al proposed a classification method for laparoscopic surgical skills based on multiple machine learning methods such as a multilayer perceptron using gaze information [19].…”