TY - GEN
T1 - Recognition of Pilot's Cognitive States based on Combination of Physiological Signals
AU - Han, Soo Yeon
AU - Kim, Jeong Woo
AU - Lee, Seong Whan
N1 - Funding Information:
The fatigue state commonly occurs after a prolonged period of engaging in a cognitive task, especially a boring or repetitive task [5]. According to the British airline pilots’ Association (BALPA), 56 percent of 500 commercial pilots admitted to being asleep while on the flight deck and, of those, nearly one in three said that they had woken up to find their co-pilot also asleep. Also, distraction occurs when pilots divert their attention away from the flight task. Civil aviation authority (CAA) of New Zealand said that interruptions and distractions during critical phases of flight are the major causes of errors leading to accidents and incidents. In case of the mental workload, it is defined as the kind of required mental cost to accomplish the given task [6]. From the pilot’s point of view, task difficulty and mental workload could be influenced by This work was supported by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD) of Korea (06-201-305-001, A Study on Human-Computer Interaction Technology for the Pilot Status Recognition).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/2
Y1 - 2019/2
N2 - Pilot's cognitive states induced by mental fatigue, distraction, and workload could be a cause of catastrophic accidents. Therefore, many methods for the detection of pilot cognitive states have been proposed in previous studies. Especially, neuro-and peripheral physiological measures (PPMs) such as electroencephalogram (EEG), electrocardiogram (ECG), respiration, and electrodermal activity (EDA) were employed to develop the novel flight assistant technologies for assurance of pilot's safety. However, each study investigated only one kind of state. Also, they did not consider the feature optimization for each subject. In this paper, we propose a method for the recognition of pilot's diversified mental states during simulated flight. The method selects the most fitted features for each subject based on the statistical analysis. The results show that the proposed method is superior to previous methods. Consequently, it shows that the pilot assistant system based on human-computer interaction (HCI) technologies could be facilitated in real-world.
AB - Pilot's cognitive states induced by mental fatigue, distraction, and workload could be a cause of catastrophic accidents. Therefore, many methods for the detection of pilot cognitive states have been proposed in previous studies. Especially, neuro-and peripheral physiological measures (PPMs) such as electroencephalogram (EEG), electrocardiogram (ECG), respiration, and electrodermal activity (EDA) were employed to develop the novel flight assistant technologies for assurance of pilot's safety. However, each study investigated only one kind of state. Also, they did not consider the feature optimization for each subject. In this paper, we propose a method for the recognition of pilot's diversified mental states during simulated flight. The method selects the most fitted features for each subject based on the statistical analysis. The results show that the proposed method is superior to previous methods. Consequently, it shows that the pilot assistant system based on human-computer interaction (HCI) technologies could be facilitated in real-world.
KW - electroencephalography
KW - passive brain computer interface
KW - peripheral physiological meausrescomponent
KW - pilot inattention
KW - pilot safety
KW - pilot's mental states
UR - http://www.scopus.com/inward/record.url?scp=85068344963&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2019.8737317
DO - 10.1109/IWW-BCI.2019.8737317
M3 - Conference contribution
AN - SCOPUS:85068344963
T3 - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
BT - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
Y2 - 18 February 2019 through 20 February 2019
ER -