BergamascoM.HerrH. (2016). Human–Robot Augmentation. In SicilianoB.KhatibO. (Eds.), Springer Handbook of Robotics (pp. 1875–1906). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_70
3.
BommerS. C.FendleyM. (2018). A theoretical framework for evaluating mental workload resources in human systems design for manufacturing operations. International Journal of Industrial Ergonomics, 63, 7–17. https://doi.org/10.1016/j.ergon.2016.10.007
4.
BrouwerA.-M.HogervorstM. A.HolewijnM.van ErpJ. B. F. (2014). Evidence for effects of task difficulty but not learning on neurophysiological variables associated with effort. International Journal of Psychophysiology, 93(2), 242–252. https://doi.org/10.1016/j.ijpsycho.2014.05.004
5.
ChadwellA.KenneyL.ThiesS.GalpinA.HeadJ. (2016). The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control. Frontiers in Neurorobotics, 10. https://doi.org/10.3389/fnbot.2016.00007
6.
ClayV.KönigP.KönigS. U. (2019). Eye tracking in virtual reality. Journal of Eye Movement Research, 12(1), Article 1. https://doi.org/10.16910/jemr.12.1.3
7.
CognolatoM.AtzoriM.MüllerH. (2018). Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances. Journal of Rehabilitation and Assistive Technologies Engineering, 5, 2055668318773991. https://doi.org/10.1177/2055668318773991
De RivecourtM.KuperusM. N.PostW. J.MulderL. J. M. (2008). Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight. Ergonomics, 51(9), 1295–1319. https://doi.org/10.1080/00140130802120267
ForoughiC. K.SibleyC.CoyneJ. T. (2017). Pupil size as a measure of within-task learning. Psychophysiology, 54(10), 1436–1443. https://doi.org/10.1111/psyp.12896
12.
HaddadinS.CroftE. (2016). Erratum to: Physical Human–Robot Interaction. In SicilianoB.KhatibO. (Eds.), Springer Handbook of Robotics (pp. E1–E1). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_81
13.
HartS. G.StavelandL. E. (1988). Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Advances in Psychology (Vol. 52, pp. 139–183). Elsevier. https://doi.org/10.1016/S0166-4115(08)62386-9
14.
HolmqvistK.NyströmM.AnderssonR.DewhurstR.HalszkaJ.van de WeijerJ. (2011). Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press. http://lup.lub.lu.se/record/1852359
15.
JustM. A.CarpenterP. A.MiyakeA. (2003). Neuroindices of cognitive workload: Neuroimaging, pupillometric and event-related potential studies of brain work. Theoretical Issues in Ergonomics Science, 4(1–2), 56–88. https://doi.org/10.1080/14639220210159735
16.
KomogortsevO. V.KarpovA. (2013). Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades. Behavior Research Methods, 45(1), 203–
https://doi.org/10.3758/s13428-012-0234-9
SchieberF.GillandJ. (2008). Visual Entropy Metric Reveals Differences in Drivers’ Eye Gaze Complexity across Variations in Age and Subsidiary Task Load. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 52(23), 1883–1887. https://doi.org/10.1177/154193120805202311
22.
ShiferawB. A. (2018). Stationary gaze entropy predicts lane departure events in sleep-deprived drivers. SCIENTIfIC REPOrTS, 10.
23.
ShiferawB.DowneyL.CrewtherD. (2019). A review of gaze entropy as a measure of visual scanning efficiency. Neuroscience & Biobehavioral Reviews, 96, 353–366. https://doi.org/10.1016/j.neubiorev.2018.12.007
24.
SobuhM. M. D.KenneyL. P. J.GalpinA. J.ThiesS. B.McLaughlinJ.KulkarniJ.KyberdP. (2014). Visuomotor behaviours when using a myoelectric prosthesis. Journal of NeuroEngineering and Rehabilitation, 11(1), 72. https://doi.org/10.1186/1743-0003-11-72
25.
SrinivasanD.MartinB. J. (2010). Eye–hand coordination of symmetric bimanual reaching tasks: Temporal aspects. Experimental Brain Research, 203(2), 391–405. https://doi.org/10.1007/s00221-0102241-3
26.
SteinfeldA.FongT.KaberD.LewisM.ScholtzJ.SchultzA.GoodrichM. (2006). Common metrics for human-robot interaction. Proceeding of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction -HRI ‘06, 33. https://doi.org/10.1145/1121241.1121249
27.
StirlingL.Kelty-StephenD.FinemanR.JonesM. L. H.Daniel ParkB.K.ReedM. P.ParhamJ.ChoiH. J. (2020). Static, Dynamic, and Cognitive Fit of Exosystems for the Human Operator. Human Factors, 62(3), 424–440. https://doi.org/10.1177/0018720819896898
28.
Van AckerB. B.BombekeK.DurnezW.ParmentierD. D.MateusJ. C.BiondiA.SaldienJ.VlerickP. (2020). Mobile pupillometry in manual assembly: A pilot study exploring the wearability and external validity of a renowned mental workload lab measure. International Journal of Industrial Ergonomics, 75, 102891. https://doi.org/10.1016/j.ergon.2019.102891
29.
WhiteM. M.ZhangW.WinslowA. T.ZahabiM.ZhangF.HuangH.KaberD. B. (2017). Usability Comparison of Conventional Direct Control Versus Pattern Recognition Control of Transradial Prostheses. IEEE Transactions on Human-Machine Systems, 47(6), 1146–1157. https://doi.org/10.1109/THMS.2017.2759762
30.
WuC.ChaJ.SulekJ.SundaramC. P.WachsJ.ProctorR. W.YuD. (2021). Sensor-based indicators of performance changes between sessions during robotic surgery training. Applied Ergonomics, 90, 103251. https://doi.org/10.1016/j.apergo.2020.103251
31.
WuC.ChaJ.SulekJ.ZhouT.SundaramC. P.WachsJ.YuD. (2019). Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training. Human Factors, 0018720819874544. https://doi.org/10.1177/0018720819874544
32.
ZhouT.ZhuQ.DuJ. (2020). Intuitive robot teleoperation for civil engineering operations with virtual reality and deep learning scene reconstruction. Advanced Engineering Informatics, 46, 101170. https://doi.org/10.1016/j.aei.2020.101170