EEG Based Workload and Stress Assessment During Remote Ship Operations

¹ Norwegian University of Science and Technology, Ålesund, Norway
² Kristiania University College, Oslo, Norway
³ Norwegian University of Science and Technology, Trondheim, Norway

DOI: 10.12716/1001.16.02.13

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ABSTRACT: Autonomous and remotely controlled ships present new types of human factor challenges. An investigation of the underlying human factors in such operations is therefore necessary to mitigate safety hazards while improving operational efficiency. More tests are needed to identify operators’ levels of control, workload and stress. The aim of this study is to assess how increases in mental workload influence the stress levels of Shore Control Centre (SCC) operators during remote ship operations. Nine experiments were performed to investigate the stress levels of SCC operators during human-human and human-machine interactions. Data on the brain signals of human operators were collected directly by electroencephalography (EEG) and subjectively by the NASA task load index (TLX). The results show that the beta and gamma band powers of the EEG recordings were highly correlated with subjective levels of workload and stress during remote ship operations. They also show that there was a significant change in stress levels when workload increased, when ships were operating in harsh weather, and when the number of ships each SCC operator is responsible for was increased. Furthermore, no significant change in stress was identified when SCC operators established very high frequency (VHF) communication or when there was a risk of accident.

KEYWORDS: Safety at Sea, Shore Control Centre (SCC), Human Factor (HF), Remote Ship Operations, Remotely Controlled Ships, NASA Task Load Index (TLX), Electroencephalography (EEG), Workload and Stress

REFERENCES

Rødseth ØJ, Faivre J, Hjørungnes SR, Andersen P, Bolbot V, Pauwelyn A-S, Wennersberg LA (2020) AUTOSHIP deliverable D3.1 Autonomous ship design standards, Revision 2.0.

Rødseth, Ørnulf & Tjora, Åsmund. (2014). A system architecture for an unmanned ship.

Kim, M., Joung, T. H., Jeong, B., & Park, H. S. (2020). Autonomous shipping and its impact on regulations, technologies, and industries. Journal of International Maritime Safety, Environmental Affairs, and Shipping, 4(2), 17-25. - doi:10.1080/25725084.2020.1779427

Kari, R.; Steinert, M. Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains. J. Mar. Sci. Eng. 2021, 9, 385. - doi:10.3390/jmse9040385

MO MSC, 2021. Regulatory Scoping Exercise for the Use of Maritime Autonomous Surface Ships (MASS) (No. 99/WP.9). London.

S. N. MacKinnon, Y. Man, and M. Baldauf, “D8.8: Final Report: Shore Control Centre.” Maritime Unmanned Navigation through Intelligence in Networks, 2015.

Man, Y., Weber, R., Cimbritz, J., Lundh, M., & MacKinnon, S. N. (2018). Human factor issues during remote ship monitoring tasks: An

Rødseth, Ørnulf & Nordahl, Håvard. (2018). Definition of autonomy levels for merchant ships, Report from NFAS, Norwegian Forum for Autonomous Ships, 2017-08-04.. 10.13140/RG.2.2.21069.08163.

Zhu, T., Haugen, S., & Liu, Y. (2019, September). Human factor challenges and possible solutions for the operation of highly autonomous ships. In Proceedings of the 29th European Safety and Reliability Conference, Hannover, Germany (pp. 22-26). - doi:10.3850/978-981-11-2724-3_0554-cd

Grech, M.R., Horberry, T., & Koester, T. (2008). Human Factors in the Maritime Domain.

Wahlström, M.; Hakulinen, J.; Karvonen, H.; Lindborg, I. Human factors challenges in unmanned ship operations-insights from other domains. Procedia Manuf. 2015, 3, 1038–1045. - doi:10.1016/j.promfg.2015.07.167

Man, Y., Lundh, M., Porathe, T., & MacKinnon, S. (2015). From desk to field-Human factor issues in remote monitoring and controlling of autonomous unmanned vessels. Procedia Manufacturing, 3, 2674-2681. - doi:10.1016/j.promfg.2015.07.635

Alsuraykh, N. H., Wilson, M. L., Tennent, P., & Sharples, S. (2019, May). How stress and mental workload are connected. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 371-376) - doi:10.1145/3329189.3329235

Dussault C, Jouanin J-C, Philippe M, Guezennec C-Y. EEG and ECG changes during simulator operation reflect mental workload and vigilance. Aviat Space Environ Med 2005; 76:344–351.

Ma, Qing & Shang, Qian & Fu, Hui & Chen, Fu. (2012). Mental Workload Analysis during the Production Process: EEG and GSR Activity. Applied Mechanics and Materials. 220-223. 193-197. 10.4028/www.scientific.net/AMM.220-223.193. - doi:10.4028/www.scientific.net/AMM.220-223.193

So, W. K., Wong, S. W., Mak, J. N., & Chan, R. H. (2017). An evaluation of mental workload with frontal EEG. PloS one, 12(4), e0174949 - doi:10.1371/journal.pone.0174949

Mohanavelu, K., Poonguzhali, S., Adalarasu, K., Ravi, D., Chinnadurai, V., Vinutha, S., ... & Jayaraman, S. (2020). Dynamic cognitive workload assessment for fighter pilots in simulated fighter aircraft environment using EEG. Biomedical Signal Processing and Control, 61, 102018. - doi:10.1016/j.bspc.2020.102018

Saeed, S. M. U., Anwar, S. M., Khalid, H., Majid, M., & Bagci, U. (2020). EEG based classification of long-term stress using psychological labeling. Sensors, 20(7), 1886. - doi:10.3390/s20071886

Gjoreski, M., Luštrek, M., Gams, M., & Gjoreski, H. (2017). Monitoring stress with a wrist device using context. Journal of biomedical informatics, 73, 159-170. - doi:10.1016/j.jbi.2017.08.006

Clifford, R.M.S., Engelbrecht, H., Jung, S. et al. Aerial firefighter radio communication performance in a virtual training system: radio communication disruptions simulated in VR for Air Attack Supervision. Vis Comput 37, 63–76 (2021). https://doi-org.ezproxy.uio.no/10.1007/s00371-020-01816-6 - doi:10.1007/s00371-020-01816-6

Ø. J. Rødseth, B. Kvamstad, T. Porathe and H. -C. Burmeister, "Communication architecture for an unmanned merchant ship," 2013 MTS/IEEE OCEANS - Bergen, 2013, pp. 1-9, doi: 10.1109/OCEANS-Bergen.2013.6608075. - doi:10.1109/OCEANS-Bergen.2013.6608075

Van Buskirk L.J., Alman P.R., McTigue J.J. (2019) Further Perspectives on Operator Guidance and Training for Heavy Weather Ship Handling. In: Belenky V., Spyrou K., van Walree F., Almeida Santos Neves M., Umeda N. (eds) Contemporary Ideas on Ship Stability. Fluid Mechanics and Its Applications, vol 119. Springer, Cham. https://doi-org.ezproxy.uio.no/10.1007/978-3-030-00516-0_49 - doi:10.1007/978-3-030-00516-0_49

Yoshida, M.; Shimizu, E.; Sugomori, M.; Umeda, A. (2021) Identification of the Relationship between Maritime Autonomous Surface Ships and the Operator’s Mental Workload. Appl. Sci. 2021, 11, 2331. - doi:10.3390/app11052331

Kimberly Tam, Rory Hopcraft, Tom Crichton & Kevin Jones (2021) The potential mental health effects of remote control in an autonomous maritime world, Journal of International Maritime Safety, Environmental Affairs, and Shipping, 5:2, 40-55, DOI: 10.1080/25725084.2021.1922148 - doi:10.1080/25725084.2021.1922148

Liu, J., Aydin, M., Akyuz, E. et al. Prediction of human–machine interface (HMI) operational errors for maritime autonomous surface ships (MASS). J Mar Sci Technol (2021). https://doi-org.ezproxy.uio.no/10.1007/s00773-021-00834-w - doi:10.1007/s00773-021-00834-w

G. Borghini et al., "Stress Assessment by Combining Neurophysiological Signals and Radio Communications of Air Traffic Controllers," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, pp. 851-854, doi: 10.1109/EMBC44109.2020.9175958. - doi:10.1109/EMBC44109.2020.9175958

Kim, D. H. (2020). Human factors influencing the ship operator's perceived risk in the last moment of collision encounter. Reliability Engineering & System Safety, 203, 107078. work. International Journal of Industrial Ergonomics, 86, 103233. - doi:10.1016/j.ress.2020.107078

Kari, R., Steinert, M., & Gaspar, H. M. (2019). Eeg application for human-centered experiments in remote ship operations. In CENTRIC 2019, The Twelfth International Conference on Advances in Human oriented and Personalized Mechanisms, Technologies, and Services. International Academy, Research and Industry Association (IARIA)

BitBrain, The Wet EEG Cap & Differences Between Water-Based, Saline and Gel EEG caps. Available online: https://www.bitbrain.com/blog/wet-eeg-cap (accessed on 16 January 2022)

Emotive, EEG EPOC FLEX. Available online: https://emotive.com (accessed on 16 January 2022)

NASA TLX: Task Load Index. Available online: https://humansystems.arc.nasa.gov/groups/TLX/ (accessed on 16 January 2022)

Hart, S. G. (2006, October). NASA-task load index (NASA-TLX); 20 years later. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 50, No. 9, pp. 904-908). Sage CA: Los Angeles, CA: Sage publications. - doi:10.1177/154193120605000909

Braarud, P. Ø. (2021). Investigating the validity of subjective workload rating (NASA TLX) and subjective situation awareness rating (SART) for cognitively complex human–machine - doi:10.1016/j.ergon.2021.103233

Vindøy, V. (2008). A functionally oriented vessel data model used as basis for classification. 7th International Conference on Computer and IT Applications in the Maritime Industries, COMPIT, 8.

Rødseth, Ørnulf Jan, Drezet, F., Pedersen, E. S., Jensen, N. A., Ehrke, K.-C., Oma, P. N., & Giere, R. (2008). TCI and status indicator specification, Flagship deliverable D-D1 - Google Search. Retrieved May 29, 2019, from https://www.google.com/search?client=safari&rls=en&q=TCI +and+status+indicator+specification,+Flagship+deliverable+ D-D1&ie=UTF-8&oe=UTF-8

Elastic. Elastic Stack, Elastic. Available online: https://www.elastic.co/elastic-stack (accessed on 19 January 202

NASA TASK LOAD INDEX (TLX) v. 1.0. Paper and Pencil Package. Human Performance Research Group. NASA Ames Research Center. Moffett Field, California.

Seo, S.-H., & Lee, J.-T. (2010). Stress and EEG. In Convergence and hybrid information technologies. IntechOpen. - doi:10.5772/9651

Harmony, T., Fernández, T., Silva, J., Bernal, J., Díaz- Comas, L., Reyes, A., ... Rodríguez, M. (1996). EEG delta activity: An indicator of attention to internal processing during performance of mental tasks. International Journal of Psychophysiology, 24(1–2), 161–171 - doi:10.1016/S0167-8760(96)00053-0

Rajendran, V. G., Jayalalitha, S., & Adalarasu, K. (2021). EEG Based Evaluation of Examination Stress and Test Anxiety Among College Students. IRBM. - doi:10.1016/j.irbm.2021.06.011

Luijcks, R., Vossen, C. J., Hermens, H. J., van Os, J., & Lousberg, R. (2015). The Influence of Perceived Stress on Cortical Reactivity: A Proof-Of-Principle Study. PloS one, 10(6), e0129220. - doi:10.1371/journal.pone.0129220

Miskovic V, Ashbaugh AR, Santesso DL, McCabe RE, Antony MM, Schmidt LA. Frontal brain oscillations and social anxiety: a cross-frequency spectral analysis during baseline and speech anticipation. Biol Psychol. 2010 Feb;83(2):125-32. doi: 10.1016/j.biopsycho.2009.11.010. Epub 2009 Nov 27. PMID: 19945500 - doi:10.1016/j.biopsycho.2009.11.010

Citation note:

Kari R., Gausdal A.H., Steinert M.: EEG Based Workload and Stress Assessment During Remote Ship Operations. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 16, No. 2, doi:10.12716/1001.16.02.13, pp. 295-305, 2022

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