@article{Meteier:11319,
      recid = {11319},
      author = {Meteier, Quentin and de Salis, Emmanuel and Capallera,  Marine and Widmer, Marino and Angelini, Leonardo and  Khaled, Omar Abou and Sonderegger, Andreas and Mugellini,  Elena},
      title = {Relevant physiological indicators for assessing workload  in conditionally automated driving, through three-class  classification and regression},
      journal = {Frontiers in Computer Science},
      address = {2022-01},
      number = {ARTICLE},
      pages = {23 p.},
      abstract = {In future conditionally automated driving, drivers may be  asked to take over control of the car while it is driving  autonomously. Performing a non-driving-related task could  degrade their takeover performance, which could be detected  by continuous assessment of drivers' mental load. In this  regard, three physiological signals from 80 subjects were  collected during 1 h of conditionally automated driving in  a simulator. Participants were asked to perform a  non-driving cognitive task (N-back) for 90 s, 15 times  during driving. The modality and difficulty of the task  were experimentally manipulated. The experiment yielded a  dataset of drivers' physiological indicators during the  task sequences, which was used to predict drivers'  workload. This was done by classifying task difficulty  (three classes) and regressing participants' reported level  of subjective workload after each task (on a 0–20 scale).  Classification of task modality was also studied. For each  task, the effect of sensor fusion and task performance were  studied. The implemented pipeline consisted of a repeated  cross validation approach with grid search applied to three  machine learning algorithms. The results showed that three  different levels of mental load could be classified with a  f1-score of 0.713 using the skin conductance and  respiration signals as inputs of a random forest  classifier. The best regression model predicted the  subjective level of workload with a mean absolute error of  3.195 using the three signals. The accuracy of the model  increased with participants' task performance. However,  classification of task modality (visual or auditory) was  not successful. Some physiological indicators such as  estimates of respiratory sinus arrhythmia, respiratory  amplitude, and temporal indices of heart rate variability  were found to be relevant measures of mental workload.  Their use should be preferred for ongoing assessment of  driver workload in automated driving.},
      url = {http://arodes.hes-so.ch/record/11319},
      doi = {https://doi.org/10.3389/fcomp.2021.775282},
}