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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Recognition of driver’s mental workload based on physiological signals, a comparative study
Jing Huang, Yü Liu, Xiaoyan Peng
Biomedical Signal Processing and Control (2021) Vol. 71, pp. 103094-103094
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open challenges (2014–2021)
Hong Vin Koay, Joon Huang Chuah, Chee‐Onn Chow, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 115, pp. 105309-105309
Closed Access | Times Cited: 38
Real-Time Driver Cognitive Workload Recognition: Attention-Enabled Learning With Multimodal Information Fusion
Haohan Yang, Jingda Wu, Zhongxu Hu, et al.
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 5, pp. 4999-5009
Closed Access | Times Cited: 29
A multilayer stacking method base on RFE-SHAP feature selection strategy for recognition of driver’s mental load and emotional state
Jing Huang, Peng Yang, Lin Hu
Expert Systems with Applications (2023) Vol. 238, pp. 121729-121729
Closed Access | Times Cited: 21
LGNet: Learning local–global EEG representations for cognitive workload classification in simulated flights
Yuwen Wang, Mingxiu Han, Yudan Peng, et al.
Biomedical Signal Processing and Control (2024) Vol. 92, pp. 106046-106046
Closed Access | Times Cited: 6
Driver state recognition with physiological signals: Based on deep feature fusion and feature selection techniques
Jing Huang, Xinyu Huang, Peng Yang, et al.
Biomedical Signal Processing and Control (2024) Vol. 93, pp. 106204-106204
Closed Access | Times Cited: 6
A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring
Ashwini Kanakapura Sriranga, Qian Lu, Stewart Birrell
Sensors (2023) Vol. 23, Iss. 4, pp. 2214-2214
Open Access | Times Cited: 15
A driver stress detection model via data augmentation based on deep convolutional recurrent neural network
Qianxi Zhao, Liu Yang, Nengchao Lyu
Expert Systems with Applications (2023) Vol. 238, pp. 122056-122056
Closed Access | Times Cited: 14
Assessment of Drivers’ Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios
Jia‐Qi Huang, Qi‐Liang Zhang, Tingru Zhang, et al.
Sensors (2024) Vol. 24, Iss. 3, pp. 1041-1041
Open Access | Times Cited: 4
A robust operators’ cognitive workload recognition method based on denoising masked autoencoder
Xiaoqing Yu, Chun‐Hsien Chen
Knowledge-Based Systems (2024) Vol. 301, pp. 112370-112370
Closed Access | Times Cited: 4
Predicting mental workload of using exoskeletons for construction work: a deep learning approach
Adedeji Afolabi, Anthony Yusuf, Abiola Akanmu
Journal of Information Technology in Construction (2025) Vol. 30, pp. 1-21
Open Access
Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles
Xiao Lu, Haiqiu Tan, Haodong Zhang, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Comprehensive assessment of mental workload of armored vehicle operators based on SVM and DS evidence theory
Qingyang Huang, Houjie Sun, Yuning Wei, et al.
Cognition Technology & Work (2025)
Closed Access
Real‐Time Non‐Driving Behavior Recognition Using Deep Learning‐Assisted Triboelectric Sensors in Conditionally Automated Driving
Haodong Zhang, Haiqiu Tan, Wuhong Wang, et al.
Advanced Functional Materials (2022) Vol. 33, Iss. 6
Closed Access | Times Cited: 20
Topological EEG-Based Functional Connectivity Analysis for Mental Workload State Recognition
Yan Yan, Liang Ma, Yu-Shi Liu, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-14
Open Access | Times Cited: 9
Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition
Abdul Razak, Sharifah Noor Masidayu Sayed Ismail, Sumendra Yogarayan, et al.
Civil Engineering Journal (2023) Vol. 9, Iss. 9, pp. 2272-2285
Open Access | Times Cited: 9
A neuroergonomics approach to investigate the mental workload of drivers in real driving settings
Hilal ATICI ULUSU, Özlem Taşkapılıoğlu, Tülin GÜNDÜZ
Transportation Research Part F Traffic Psychology and Behaviour (2024) Vol. 103, pp. 177-189
Closed Access | Times Cited: 3
Investigating Methods for Cognitive Workload Estimation for Assistive Robots
Ayca Aygun, Thuan Nguyen, Zachary Haga, et al.
Sensors (2022) Vol. 22, Iss. 18, pp. 6834-6834
Open Access | Times Cited: 13
A systematic review on the influence factors, measurement, and effect of driver workload
Jun Ma, Yiping Wu, Jian Rong, et al.
Accident Analysis & Prevention (2023) Vol. 192, pp. 107289-107289
Closed Access | Times Cited: 7
Predicting Driver's mental workload using physiological signals: A functional data analysis approach
Chaeyoung Lee, MinJu Shin, David Eniyandunmo, et al.
Applied Ergonomics (2024) Vol. 118, pp. 104274-104274
Open Access | Times Cited: 2
Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach
Lichen Sun, Hongze Yang, Bo Li
Electronics (2024) Vol. 13, Iss. 19, pp. 3904-3904
Open Access | Times Cited: 2
Cognitive workload quantification for air traffic controllers: An ensemble semi-supervised learning approach
Xiaoqing Yu, Chun‐Hsien Chen, Haohan Yang
Advanced Engineering Informatics (2024) Vol. 64, pp. 103065-103065
Closed Access | Times Cited: 2
Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals
Matthias Scheutz, Shuchin Aeron, Ayca Aygun, et al.
Topics in Cognitive Science (2023) Vol. 16, Iss. 3, pp. 485-526
Closed Access | Times Cited: 5
Inter-subject cognitive workload estimation based on a cascade ensemble of multilayer autoencoders
Zhanpeng Zheng, Zhong Yin, Yongxiong Wang, et al.
Expert Systems with Applications (2022) Vol. 211, pp. 118694-118694
Closed Access | Times Cited: 8
Drivers’ Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks
Taraneh Aminosharieh Najafi, Antonio Affanni, R. Rinaldo, et al.
Sensors (2023) Vol. 23, Iss. 17, pp. 7346-7346
Open Access | Times Cited: 4
The Visual Scanning Behavior and Mental Workload of Drivers at Prairie Highway Intersections With Different Characteristics
Zhen Lyu, QI Chun-hua, Shoulin Zhu, et al.
IEEE Access (2022) Vol. 10, pp. 123043-123056
Open Access | Times Cited: 5
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