TY - JOUR
T1 - Predicting Driver's mental workload using physiological signals
T2 - A functional data analysis approach
AU - Lee, Chaeyoung
AU - Shin, Min Ju
AU - Eniyandunmo, David
AU - Anwar, Alvee
AU - Kim, Eunsik
AU - Kim, Kyongwon
AU - Yoo, Jae Keun
AU - Lee, Chris
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/7
Y1 - 2024/7
N2 - This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers’ mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.
AB - This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers’ mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.
KW - Driver mental workload
KW - Functional data analysis
KW - Physiological signals
UR - http://www.scopus.com/inward/record.url?scp=85188552728&partnerID=8YFLogxK
U2 - 10.1016/j.apergo.2024.104274
DO - 10.1016/j.apergo.2024.104274
M3 - Article
C2 - 38521001
AN - SCOPUS:85188552728
SN - 0003-6870
VL - 118
JO - Applied Ergonomics
JF - Applied Ergonomics
M1 - 104274
ER -