TY - GEN
T1 - Towards Safer Flights
T2 - 4th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2022
AU - Li, Yuhan
AU - Li, Ke
AU - Wang, Shaofan
AU - Li, Yuangan
AU - Chen, Jia'Ao
AU - Wen, Dongsheng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The cognitive overload experienced by pilots in arduous circumstances is related to the psychological state and sympathetic response of the human subject. The physiological signals of subjects are predictive and reliable in detecting their mental states, thus preventing cognitive overload. This study proposes a Multi-modality Fusion Technology (MFT) based model for recognizing pilot cognitive load through pilots' physiological signals, including electrocardiosignal (ECG), photoplethysmography (PPG), electrodermal response (EDA), electromyography signal (EMG), respiration signal (RESP) and skin temperature signal (SKT). From these signals features are extracted and then fused at the feature layer into a united vector. This vector is then sent into the model for learning. In the decision level, individual decisions from several models are combined and a decision is finalized. Various subjects were involved in experimental data collection on a flight simulator, and the collected data were used to train and test the model. The model is evaluated through both 10-fold cross-validation and Leave-One-person-Out (LOO) cross-validation.
AB - The cognitive overload experienced by pilots in arduous circumstances is related to the psychological state and sympathetic response of the human subject. The physiological signals of subjects are predictive and reliable in detecting their mental states, thus preventing cognitive overload. This study proposes a Multi-modality Fusion Technology (MFT) based model for recognizing pilot cognitive load through pilots' physiological signals, including electrocardiosignal (ECG), photoplethysmography (PPG), electrodermal response (EDA), electromyography signal (EMG), respiration signal (RESP) and skin temperature signal (SKT). From these signals features are extracted and then fused at the feature layer into a united vector. This vector is then sent into the model for learning. In the decision level, individual decisions from several models are combined and a decision is finalized. Various subjects were involved in experimental data collection on a flight simulator, and the collected data were used to train and test the model. The model is evaluated through both 10-fold cross-validation and Leave-One-person-Out (LOO) cross-validation.
KW - cognitive load
KW - ensemble learning
KW - machine learning
KW - multi-modality
KW - physiological signals
KW - pilot states
KW - workload recognition
UR - https://www.scopus.com/pages/publications/85146424182
U2 - 10.1109/ICCASIT55263.2022.9986937
DO - 10.1109/ICCASIT55263.2022.9986937
M3 - 会议稿件
AN - SCOPUS:85146424182
T3 - Proceedings of 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2022
SP - 525
EP - 530
BT - Proceedings of 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2022
A2 - Sun, Huabo
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 October 2022 through 14 October 2022
ER -