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Cognitive Work Protection—A New Approach for Occupational Safety in Human-Machine Interaction

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Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 29))

Abstract

Previous occupational safety concepts in human-machine interaction scenarios are based on the principle of spatial separation, reduction of collision force or distance monitoring between humans and robots. Collaborative robot systems and semi-automated machines are working closely with people in more and more areas, both spatially and functionally. Therefor a new approach for occupational safety in close human-machine collaboration scenarios is presented. It relies on a real-time EEG measurement of human workers with brain computer interfaces and a subsequent adjustment of the robot system based on the detected cognitive states.

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References

  1. International Federation of Robotics, World Robotics 2017, https://ifr.org/downloads/press/Executive_Summary_WR_2017_Industrial_Robots.pdf

  2. Hofmann, D.A., Burke, M.J., Zohar, D.: 100 years of occupational safety research: from basic protections and work analysis to a multilevel view of workplace safety and risk. J. Appl. Psychol. 102(3), 375–388 (2017)

    Article  Google Scholar 

  3. Zhang, D., Wei, B., Rosen, M.: Overview of an engineering teaching module on robotics safety. In: Zhang, D., Wei, B. (eds.) Mechatronics and Robotics Engineering for Advanced and Intelligent Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Cham (2017)

    Chapter  Google Scholar 

  4. Hassard, J., Teoh, K.R.H., Visockaite, G., Dewe, P., Cox, T.: The cost of work-related stress to society: A systematic review. J. Occup. Health Psychol. 23(1), 1–17 (2018)

    Article  Google Scholar 

  5. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1) (2004)

    Article  Google Scholar 

  6. Kirchner, E.A., de Gea Fernandez, J., Kampmann, P., Schröer, M., Metzen, J.H., Kirchner, F.: Intuitive Interaction with Robots - Technical Approaches and Challenges, pp. 224–248. Springer, Heidelberg (2015)

    Google Scholar 

  7. Kulic, D.: Safety for human robot interaction, https://ece.uwaterloo.ca/~dkulic/pubs/DKulicThesisFinal.pdf

  8. de Gea Fernandez, J., Mronga, D., Gnther. M., Knobloch, T., Wirkus, M., Schrer, M., Trampler, M., Stiene, S., Kirchner, E., Bargsten, V., Bnziger, T., Teiwes, J., Krger, T., Kirchner, F.: Multimodal sensor-based whole-body control for humanrobot collaboration in industrial settings. Robot. Auton. Syst. 94, 102–119 (2017). ISSN: 0921-8890, https://doi.org/10.1016/j.robot.2017.04.007, URL http://www.sciencedirect.com/science/article/pii/S0921889016305127

    Article  Google Scholar 

  9. Sunny, T.D., Aparna, T., Neethu, P., Venkateswaran, J., Vishnupriya, V., Vyas, P.S.: Robotic arm with brain – computer interfacing. Procedia Technol. 24, 1089–1095 (2016)

    Article  Google Scholar 

  10. Latif, M.Y. et al.: Brain computer interface based robotic arm control. In: 2017 International Smart Cities Conference (ISC2), Wuxi, pp. 1–5 (2017 )

    Google Scholar 

  11. Wang, J., Liu, Y, Tang, J.: Fast robot arm control based on brain-computer interface. In: 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, pp. 571–575 (2016)

    Google Scholar 

  12. Niedermeyer, E., Lopes da Silva, F.H.: Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 3rd edition, Lippincott. Williams & Wilkins, Philadelphia (1993)

    Google Scholar 

  13. Chaudhary, U., Birbaumer, N., Ramos-Murguialday, A.: Brain–computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513–525 (2016)

    Article  Google Scholar 

  14. Kirchner, E.A., Kim, S.K., Wöhrle, H., Tabie, M., Maurus, M., Kirchner, F.: An intelligent man-machine interface - multi-robot control adapted for task engagement based on single-trial detectability of P300. Front. Hum. Neurosci. 10, 291 (2016)

    Article  Google Scholar 

  15. Wöhrle, H., Kirchner, E.A.: Online classifier adaptation for the detection of p 300 target recognition processes in a complex teleoperation scenario. In: da Silva, H.P., Holzinger, A., Fairclough, S., Majoe, D. (eds.) Physiological Computing Systems, Vol. 8908 of Lecture Notes in Computer Science, pp 105–118. Berlin, Heidelberg: Springer (2014)

    Google Scholar 

  16. Gundel, A., Wilson, G.F.: Topographical changes in the ongoing EEG related to the difficulty of mental tasks. Brain Topogr. 5(1), 17–25 (1992)

    Article  Google Scholar 

  17. Scerbo, M.W., Freeman, F.G., Mikulka, P.J.: A brain-based system for adaptive automation. Theor. Issues Ergon. Sci. 4(1–2), 200–219 (2003)

    Article  Google Scholar 

  18. Postma, M.A., Schellekens, J.M.H., Hanson, E.K.S., Hoogeboom, P.J.: Fz theta divided by Pz alpha as an index of task load during a PC-based air traffic control simulation. In: De Waard, D., Brookhuis, K.A., van Egmond, R., Boersema, T. (eds.) Human Factors in Design, Safety, and Management, pp. 465–470 (2005)

    Google Scholar 

  19. Berka, C., Levendowski, D.J.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B244 (2007)

    Google Scholar 

  20. Holm, A., Lukander, K., Korpela, J., Sallinen, M., Müller, K.M.: Estimating brain load from the EEG. Sci. World J. 9, 639–651 (2009)

    Article  Google Scholar 

  21. Kamzanova, A.T., Kustubayeva, A.M.: Use of EEG workload indices for diagnostic monitoring of vigilance decrement. Hum. Factors 56(6), 136–1149 (2014)

    Article  Google Scholar 

  22. Dasari, D., Shou, G., Ding, L.: ICA-derived EEG correlates to mental fatigue, effort, and workload in a realistically simulated air traffic control task. Front. Neurosci. 11, 297 (2017)

    Article  Google Scholar 

  23. Boksem, M.A.S., Meijman, T.F., Lorist, M.M.: Effects of mental fatigue on attention: an ERP study. Cogn. Brain. Res. 25, 107–116 (2005)

    Article  Google Scholar 

  24. Käthner, I., Wriessnegger, S.C., Müller-Putz, G.R., Kübler, A., Haldera, S.: Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain–computer interface. Biol. Psychol. 102, 118–129 (2014)

    Article  Google Scholar 

  25. Pigeau, R., Hoffmann, R. Purcell, S., Moffitt A.: The effect of endogenous alpha on hemispheric asymmetries and the relationship of frontal theta to sustained attention. Defense Technical Information Center (1987)

    Google Scholar 

  26. Akerstedt, T., Gillberg, T.: Subjective and objective sleepiness in the active individual. Int. J. Neurosci. 52, 29–37 (1999)

    Article  Google Scholar 

  27. Lal, S.K.L., Craig, A.: Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 39, 313–321 (2002)

    Article  Google Scholar 

  28. Marcora, S.M., Staiano, W., Manning, V.: Mental fatigue impairs physical performance in humans. J. Appl. Physiol. 106, 857–864 (2009)

    Article  Google Scholar 

  29. Tanakal, M., Shigihara, Y., Ishii, A., Funakura, M., Kanai, E., Watanabe, Y.: Effect of mental fatigue on the central nervous system: an electroencephalography study. Behav. Brain Funct. 8, 48 (2012)

    Article  Google Scholar 

  30. Barwick, F., Arnett, P., Slobounov, S.: EEG correlates of fatigue during administration of a neuropsychological test battery. Clin. Neurophysiol. 123(2), 278–284 (2012)

    Article  Google Scholar 

  31. Zhaoa, C., Zhaoa, M., Liu, J., Zhengb, C.: Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accid. Anal. Prev. 45, 83–90 (2012)

    Article  Google Scholar 

  32. Kok, A.: On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology 38(3), 557–577 (2001)

    Article  Google Scholar 

  33. Kim, S.K., Kirchner, E.A.: Classifier transferability in the detection of error related potentials from observation to interaction. In: Proceedings of IEEE international conference of system, man, cybernetics, pp. 3360–3365 (2013)

    Google Scholar 

  34. Chavarriaga, R., Sobolewski, A.: Millán, J.d.R.: Errare machinale est: the use of error-related potentials in brain-machine interfaces. Front. Neurosci. 8, 208 (2014)

    Article  Google Scholar 

  35. Kim, S.K., Kirchner, E.A.: Handling few training data: classifier transfer between different types of error-related potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 24(3), 320–332 (2016)

    Article  Google Scholar 

  36. Kim, S.K., Kirchner, E.A., Stefes, A., Kirchner, F.: Intrinsic interactive reinforcement learning – using error-related potentials for real world human-robot interaction. Sci. Rep. 7, 17562 (2017)

    Article  Google Scholar 

  37. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., Yger, F: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3) (2018)

    Article  Google Scholar 

  38. Roy, R.N., Bonnet, S., Charbonnier, S., Jallon, P., Campagne, A.: A comparison of ERP spatial filtering methods for optimal mental workload estimation. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7254–7257, Milan (2015)

    Google Scholar 

  39. Kirchner, E.A., Kim, S.K., Straube S., Seeland, A., Wöhrle, H., Krell, M. M., Tabie, M. Fahle, M.: On the applicability of brain reading for predictive human-machine interfaces in robotics. PLoS ONE 8(12), e81732, 12 (2013)

    Article  Google Scholar 

  40. Gwin, J.T., Gramann, K., Makeig, S., Ferris, D.P.: Removal of movement artifact from high-density eeg recorded during walking and running. J. Neurophysiol. 103(6), pp. 3526–3534 (2010, June)

    Article  Google Scholar 

  41. Kohli, S., Casson, A.J.: Towards out-of-the-lab EEG in uncontrolled environments: feasibility study of dry EEG recordings during exercise bike riding. Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 1025–1028 (2015)

    Google Scholar 

  42. Bailey, S.P., Hall, E.E., Folger, S.E., Miller, P.C.: Changes in EEG during graded exercise on a recumbent cycle ergometer. J. Sports Sci. Med. 7(4), 505–511 (2008)

    Google Scholar 

  43. Reis, P.M.R., Hebenstreit, F., Gabsteiger, F., von Tscharner, V., Lochmann, M.: Methodological aspects of EEG and body dynamics measurements during motion. Front. Hum. Neurosci. 8, 156 (2014)

    Google Scholar 

  44. Wöhrle, H., Teiwes, J., Krell, M.M., Seeland, A., Kirchner, E.A., Kirchner, F.: Reconfigurable dataflow hardware accelerators for machine learning and robotics. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, (ECML PKDD-2014), 15.9.2014–19.9.2014, Nancy, Springer, pp. 129–138 (2014)

    Google Scholar 

  45. Wöhrle, H., Tabie, M., Kim, S.K., Kirchner, E., Kirchner, F. (2017). A hybrid FPGA-based system for EEG- and EMG-based online movement prediction. Sensors 17 (2017)

    Article  Google Scholar 

  46. Wöhrle, H., Teiwes, J., Krell, M.M., Kirchner, E.A., Kirchner, F.: A dataflow-based mobile brain reading system on chip with supervised online calibration. In: Congress Proceedings International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX-2013), Vilamoura, Portugal, SCITEPRESS Digital Library, 18–20 September 2013

    Google Scholar 

  47. Kirchner, E.A., Drechsler, R.: A formal model for embedded brain reading. Ind. Robot Int. J. 40(6), 530–540 (2013)

    Article  Google Scholar 

  48. https://www.emotiv.com, 2018/04/30

  49. http://www.choosemuse.com/, 2018/04/30

  50. https://www.slashgear.com/portable-eeg-machine-shows-how-music-affects-the-brain-during-exercise-19519957/, 2018/04/30

  51. Jaekel, M.: Die Macht der digitalen Plattformen. Wegweiser im Zeitalter einer expandierenden Digitalshpäre und künstlicher Intelligenz. Springer, Wiesbaden (2017)

    Google Scholar 

  52. Tiwana, A.: Platform Ecosystems. Aligning Architecture, Governance, and Strategy. Morgan Kaufmann, Waltham (2014)

    Google Scholar 

  53. Shariatzadeh, N., Lundholm, T., Lindberg, L., Sivard, G.: Integration of digital factory with smart factory based on Internet Of Things. Procedia CIRP 50, 512–517 (2016)

    Article  Google Scholar 

  54. Lee, J., Bagheri, B., Jin, C.: Introduction to cyber manufacturing. Manuf. Lett. 8, 11–15 (2016)

    Article  Google Scholar 

  55. Klasnja, P., Hekler, E.B.: Wearable technology and long-term weight loss. JAMA. 317(3), 317–318 (2017)

    Article  Google Scholar 

  56. Pevnick, J.M., Birkeland, K., Zimmer, R., Elad, Y., Kedan, I.: Wearable technology for cardiology: an update and framework for the future. Trends Cardiovasc. Med. 28(2), 144–150 (2018)

    Article  Google Scholar 

  57. Awolusi, I., Marks, E., Hallowell, M.: Wearable technology for personalized construction safety monitoring and trending: review of applicable devices. Autom. Constr. 85, 96–106 (2018)

    Article  Google Scholar 

  58. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. 852 Sys. Technol. (TIST) 2(27), 1–27 (2011)

    Article  Google Scholar 

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Correspondence to Christian Neu .

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Neu, C., Kirchner, E.A., Kim, SK., Tabie, M., Linn, C., Werth, D. (2019). Cognitive Work Protection—A New Approach for Occupational Safety in Human-Machine Interaction. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-01087-4_26

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