{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T03:20:14Z","timestamp":1774063214060,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T00:00:00Z","timestamp":1707091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32271130"],"award-info":[{"award-number":["32271130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023A1515012843"],"award-info":[{"award-number":["2023A1515012843"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["24050000694"],"award-info":[{"award-number":["24050000694"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JCYJ20230808105219038"],"award-info":[{"award-number":["JCYJ20230808105219038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JCYJ20210324100014040"],"award-info":[{"award-number":["JCYJ20210324100014040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["32271130"],"award-info":[{"award-number":["32271130"]}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["2023A1515012843"],"award-info":[{"award-number":["2023A1515012843"]}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["24050000694"],"award-info":[{"award-number":["24050000694"]}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["JCYJ20230808105219038"],"award-info":[{"award-number":["JCYJ20230808105219038"]}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["JCYJ20210324100014040"],"award-info":[{"award-number":["JCYJ20210324100014040"]}]},{"name":"Foundation of Shenzhen Science and Technology Innovation Committee","award":["32271130"],"award-info":[{"award-number":["32271130"]}]},{"name":"Foundation of Shenzhen Science and Technology Innovation Committee","award":["2023A1515012843"],"award-info":[{"award-number":["2023A1515012843"]}]},{"name":"Foundation of Shenzhen Science and Technology Innovation Committee","award":["24050000694"],"award-info":[{"award-number":["24050000694"]}]},{"name":"Foundation of Shenzhen Science and Technology Innovation Committee","award":["JCYJ20230808105219038"],"award-info":[{"award-number":["JCYJ20230808105219038"]}]},{"name":"Foundation of Shenzhen Science and Technology Innovation Committee","award":["JCYJ20210324100014040"],"award-info":[{"award-number":["JCYJ20210324100014040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assessing drivers\u2019 mental workload is crucial for reducing road accidents. This study examined drivers\u2019 mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers\u2019 mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers\u2019 mental states.<\/jats:p>","DOI":"10.3390\/s24031041","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T03:22:31Z","timestamp":1707189751000},"page":"1041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Assessment of Drivers\u2019 Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios"],"prefix":"10.3390","volume":"24","author":[{"given":"Jiaqi","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Qiliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Physical Science and Technology College, Yichun University, Yichun 336000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2395-1864","authenticated-orcid":false,"given":"Tingru","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Tieyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Xiamen Meiya Pico Information Co., Ltd., Xiamen 361008, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2319-359X","authenticated-orcid":false,"given":"Da","family":"Tao","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). Global Status Report on Road Safety 2018, World Health Organization."},{"key":"ref_2","unstructured":"Dattani, S., Spooner, F., Ritchie, H., and Roser, M. (2023, December 11). Causes of Death. Our World in Data 2023. Available online: https:\/\/ourworldindata.org\/causes-of-death."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e390","DOI":"10.1016\/S2542-5196(19)30170-6","article-title":"The global macroeconomic burden of road injuries: Estimates and projections for 166 countries","volume":"3","author":"Chen","year":"2019","journal-title":"Lancet Planet. Health"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E.H., Alzoubi, K., Khandakar, A., Khallifa, R., Abouhasera, R., Koubaa, S., Ahmed, R., and Hasan, A. (2019). Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents. Sensors, 19.","DOI":"10.3390\/s19122780"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"275","DOI":"10.4236\/cus.2022.102017","article-title":"Analysis of Global Road Traffic Death Data Using a Clustering Approach","volume":"10","author":"Dutta","year":"2022","journal-title":"Curr. Urban Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1068\/p251081","article-title":"The Information That Drivers Use: Is it Indeed 90% Visual?","volume":"25","author":"Sivak","year":"1996","journal-title":"Perception"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Morales-Alvarez, W., Sipele, O., L\u00e9beron, R., Tadjine, H.H., and Olaverri-Monreal, C. (2020). Automated Driving: A Literature Review of the Take over Request in Conditional Automation. Electronics, 9.","DOI":"10.3390\/electronics9122087"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hussain, I., Young, S., and Park, S.-J. (2021). Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System. Sensors, 21.","DOI":"10.3390\/s21216985"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.trf.2022.05.013","article-title":"Interaction strategies with advanced driver assistance systems","volume":"88","author":"Neuhuber","year":"2022","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103958","DOI":"10.1016\/j.apergo.2022.103958","article-title":"Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction","volume":"108","author":"Zhang","year":"2023","journal-title":"Appl. Ergon."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.ergon.2019.06.001","article-title":"Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling","volume":"72","author":"Lee","year":"2019","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1518\/001872008X288394","article-title":"Multiple Resources and Mental Workload","volume":"50","author":"Wickens","year":"2008","journal-title":"Hum. Factors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting Stress During Real-World Driving Tasks Using Physiological Sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.bspc.2013.06.014","article-title":"A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals","volume":"8","author":"Singh","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1080\/00140139.2020.1759699","article-title":"Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning","volume":"63","author":"Ding","year":"2020","journal-title":"Ergonomics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"883321","DOI":"10.3389\/fpsyg.2022.883321","article-title":"Human Mental Workload: A Survey and a Novel Inclusive Definition","volume":"13","author":"Longo","year":"2022","journal-title":"Front. Psychol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tao, D., Tan, H., Wang, H., Zhang, X., Qu, X., and Zhang, T. (2019). A Systematic Review of Physiological Measures of Mental Workload. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16152716"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.3389\/fpsyg.2014.01344","article-title":"Mental workload and driving","volume":"5","author":"Paxion","year":"2014","journal-title":"Front. Psychol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.apergo.2018.08.028","article-title":"Measuring mental workload using physiological measures: A systematic review","volume":"74","author":"Charles","year":"2019","journal-title":"Appl. Ergon."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1177\/154193120605000909","article-title":"Nasa-Task Load Index (NASA-TLX); 20 Years Later","volume":"50","author":"Hart","year":"2006","journal-title":"Proc. Hum. Factors Ergon. Soc. Annu. Meet."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0166-4115(08)62387-0","article-title":"The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload","volume":"Volume 52","author":"Reid","year":"1988","journal-title":"Advances in Psychology"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Almogbel, M.A., Dang, A.H., and Kameyama, W. (2018, January 11\u201314). EEG-signals based cognitive workload detection of vehicle driver using deep learning. Proceedings of the 2018 20th International Conference on Advanced Communications Technology (ICACT), 2\/2018, Chuncheon, Republic of Korea.","DOI":"10.23919\/ICACT.2018.8323715"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1080\/00140130802120267","article-title":"Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight","volume":"51","author":"Kuperus","year":"2008","journal-title":"Ergonomics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"197","DOI":"10.5194\/ars-13-197-2015","article-title":"Statistical sensor fusion of ECG data using automotive-grade sensors","volume":"13","author":"Koenig","year":"2015","journal-title":"Adv. Radio Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zontone, P., Affanni, A., Bernardini, R., Piras, A., and Rinaldo, R. (2019, January 2\u20136). Stress Detection Through Electrodermal Activity (EDA) and Electrocardiogram (ECG) Analysis in Car Drivers. Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), 9\/2019, A Coru\u00f1a, Spain.","DOI":"10.23919\/EUSIPCO.2019.8902631"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1007\/978-3-319-96074-6_31","article-title":"Mental Workload and Performance Measurements in Driving Task: A Review Literature","volume":"823","author":"Bagnara","year":"2019","journal-title":"Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1080\/00140139.2011.604431","article-title":"The impact of cognitive workload on physiological arousal in young adult drivers: A field study and simulation validation","volume":"54","author":"Reimer","year":"2011","journal-title":"Ergonomics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1177\/0018720812442086","article-title":"Sensitivity of Physiological Measures for Detecting Systematic Variations in Cognitive Demand From a Working Memory Task: An On-Road Study Across Three Age Groups","volume":"54","author":"Mehler","year":"2012","journal-title":"Hum. Factors"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Borghini, G., Vecchiato, G., Toppi, J., Astolfi, L., Maglione, A., Isabella, R., Caltagirone, C., Kong, W., Wei, D., and Zhou, Z. (September, January 28). Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. Proceedings of the 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 8\/2012, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347469"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2012.10.003","article-title":"Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness","volume":"44","author":"Borghini","year":"2014","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.apergo.2018.06.006","article-title":"Mental workload is reflected in driver behaviour, physiology, eye movements and prefrontal cortex activation","volume":"73","author":"Foy","year":"2018","journal-title":"Appl. Ergon."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1177\/0018720821990484","article-title":"The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection","volume":"63","author":"Yang","year":"2021","journal-title":"Hum. Factors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3141\/2518-06","article-title":"Impact of Repeated Exposure to a Multilevel Working Memory Task on Physiological Arousal and Driving Performance","volume":"2518","author":"Belyusar","year":"2015","journal-title":"Transp. Res. Rec."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"35890","DOI":"10.1109\/ACCESS.2018.2851309","article-title":"Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios","volume":"6","author":"Liao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cardone, D., Perpetuini, D., Filippini, C., Mancini, L., Nocco, S., Tritto, M., Rinella, S., Giacobbe, A., Fallica, G., and Ricci, F. (2022). Classification of Drivers\u2019 Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals. Sensors, 22.","DOI":"10.3390\/s22197300"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.apergo.2016.12.015","article-title":"Electrocardiographic features for the measurement of drivers\u2019 mental workload","volume":"61","author":"Heine","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1002\/hbm.20284","article-title":"Compensatory activations in patients with multiple sclerosis during preserved performance on the auditory N-back task","volume":"28","author":"Forn","year":"2007","journal-title":"Hum. Brain Mapp."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.trf.2020.01.019","article-title":"Driving mental workload and performance of ageing drivers","volume":"69","author":"Yusoff","year":"2020","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.3758\/BRM.41.4.1149","article-title":"Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses","volume":"41","author":"Faul","year":"2009","journal-title":"Behav. Res. Methods"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ploch, C.J., Bae, J.H., Ploch, C.C., Ju, W., and Cutkosky, M.R. (2017, January 6\u20139). Comparing haptic and audio navigation cues on the road for distracted drivers with a skin stretch steering wheel. Proceedings of the 2017 IEEE World Haptics Conference (WHC), 6\/2017, Munich, Germany.","DOI":"10.1109\/WHC.2017.7989943"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103201","DOI":"10.1016\/j.apergo.2020.103201","article-title":"Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data","volume":"89","author":"Chihara","year":"2020","journal-title":"Appl. Ergon."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10578-019-00915-3","article-title":"Increased Working Memory Load in a Dual-Task Design Impairs Nonverbal Social Encoding in Children with High and Low Attention-Deficit\/Hyperactivity Disorder Symptoms","volume":"51","author":"Hilton","year":"2020","journal-title":"Child Psychiatry Hum. Dev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pang, L., Fan, Y., Deng, Y., Wang, X., and Wang, T. (2020, January 17\u201319). Mental Workload Classification By Eye Movements in Visual Search Tasks. Proceedings of the 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Chengdu, China.","DOI":"10.1109\/CISP-BMEI51763.2020.9263668"},{"key":"ref_45","first-page":"B231","article-title":"EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks","volume":"78","author":"Berka","year":"2007","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3233\/THC-209008","article-title":"Assessment of mental workload based on multi-physiological signals","volume":"28","author":"Fan","year":"2020","journal-title":"Technol. Health Care"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"94","DOI":"10.2466\/22.PMS.121c12x5","article-title":"Effective Indices for Monitoring Mental Workload While Performing Multiple Tasks","volume":"121","author":"Hsu","year":"2015","journal-title":"Percept. Mot. Skills"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.trf.2009.02.004","article-title":"Risk behaviour and mental workload: Multimodal assessment techniques applied to motorbike riding simulation","volume":"12","author":"Maldonado","year":"2009","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_49","unstructured":"De Waard, D., and Brookhuis, K.A. (2023, December 16). The Measurement of Drivers\u2019 Mental Workload. Available online: https:\/\/www.researchgate.net\/profile\/Dick-Waard\/publication\/30481401_The_measurement_of_drivers%27_mental_workload\/links\/00b7d53aab0ca05ce3000000\/The-measurement-of-drivers-mental-workload.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pfleging, B., Fekety, D.K., Schmidt, A., and Kun, A.L. (2016, January 7\u201312). A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions. Proceedings of the CHI\u201916: CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA.","DOI":"10.1145\/2858036.2858117"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.trf.2016.04.007","article-title":"The effects of driving environment complexity and dual tasking on drivers\u2019 mental workload and eye blink behavior","volume":"40","author":"Faure","year":"2016","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.apergo.2015.07.009","article-title":"Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study","volume":"52","author":"Fallahi","year":"2016","journal-title":"Appl. Ergon."},{"key":"ref_53","first-page":"3","article-title":"An Analysis of Mental Workload in Pilots During Flight Using Multiple Psychophysiological Measures","volume":"12","author":"Wilson","year":"2002","journal-title":"Int. J. Aerosp. Psychol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Huang, J., Liu, Y., and Peng, X. (2022). Recognition of driver\u2019s mental workload based on physiological signals, a comparative study. Biomed. Signal Process. Control, 71.","DOI":"10.1016\/j.bspc.2021.103094"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3389\/fnhum.2019.00057","article-title":"A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving","volume":"13","author":"Lohani","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.trf.2023.02.004","article-title":"Driver\u2019s mental workload classification using physiological, traffic flow and environmental factors","volume":"94","author":"Wei","year":"2023","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/1041\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:55:22Z","timestamp":1760104522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/1041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,5]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24031041"],"URL":"https:\/\/doi.org\/10.3390\/s24031041","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,5]]}}}