Skip to main content
SpringerLink
Log in
Book cover

International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2016: Biomedical Engineering Systems and Technologies pp 205–223Cite as

Detailed Estimation of Cognitive Workload with Reference to a Modern Working Environment

  • Conference paper
  • First Online:

  • 624 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 690))

Abstract

In modern industry, employees are confronted with ever more complex working tasks. As a consequence, cognitive workload of the employees rises. This makes automatic estimation of cognitive workload a key subject of research. Such an estimate would enable adaptive Human-Machine Interaction that could be used to fit the employees’ workload accordingly to their needs. In this work, a tablet interaction study is presented that is designed to induce cognitive workload. Supervised machine learning methods are used to estimate the induced cognitive workload based on features taken from heart rate, electrodermal activity and user interaction (touch input). Ground truth data is obtained from the subjects’ self-reported cognitive workload. Inter-subject accuracy of the best learner is 74.1% for the detailed 5-class problem and 96.0% for the simplified binary problem.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Variation of the time interval between successive heartbeats. Also known as RR-interval.

  2. 2.

    In some applications, e.g. the automotive industry, related parameters like arousal or fatigue are considered.

  3. 3.

    GT-P8110; Google Inc., Samsung Electronics.

  4. 4.

    Mindfield Biosystems Ltd., http://www.mindfield.de.

  5. 5.

    Brain Products GmbH, http://www.brainproducts.com.

  6. 6.

    Polar Electro Oy, http://www.polar.com.

  7. 7.

    Physical Enterprises Inc. (Mio Global), http://www.mioglobal.com.

  8. 8.

    For each of the 7 subsets all combinations of window sizes (11) and overlaps (4) are evaluated.

References

  1. Alberdi, A., Aztiria, A., Basarab, A.: Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J. Biomed. Inform. 59, 49–75 (2016). doi:10.1016/j.jbi.2015.11.007

    Article  Google Scholar 

  2. Bishop, C.M.: Pattern recognition and machine learning (information science and statistics). In: Kernel Methods, pp. 291–323. Springer, New York (2006)

    Google Scholar 

  3. Botthof, A., Hartmann, E.: Zukunft der Arbeit in Industrie 4.0 - Neue Perspektiven und offene Fragen. In: Botthof, A., Hartmann, E.A. (eds.) Zukunft der Arbeit in Industrie 4.0, pp. 161–163. Springer, Heidelberg (2015). doi:10.1007/978-3-662-45915-7_15

  4. Bowling, N.A., Kirkendall, C.: Workload: a review of causes, consequences, and potential interventions. In: Houdmont, J., Leka, S., Sinclair, R.R. (eds.) Contemporary Occupational Health Psychology, vol. 2, pp. 221–238. Wiley, Chichester (2012). doi:10.1002/9781119942849.ch13

  5. Cain, B.: A review of the mental workload literature. In: RTO-TR-HFM-121-Part-II. NATO Science and Technology Organization (2007)

    Google Scholar 

  6. Choi, J., Ahmed, B., Gutierrez-Osuna, R.: Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16(2), 279–286 (2012). doi:10.1109/TITB.2011.2169804

    Article  Google Scholar 

  7. Choi, J., Gutierrez-Osuna, R.: Using heart rate monitors to detect mental stress. In: Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 219–223, June 2009. doi:10.1109/BSN.2009.13

  8. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)

    Google Scholar 

  9. Fürnkranz, J.: Decision tree. In: Sammut, C., Webb, G. (eds.) Encyclopedia of Machine Learning, pp. 263–267. Springer New York (2010). doi:10.1007/978-0-387-30164-8_324

  10. Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Human Mental Workload, vol. 52, pp. 139–183. North-Holland (1988). doi:10.1016/S0166-4115(08)62386-9

  11. Healey, J., Picard, R.: Detecting stress during real-world driving tasks using physiological sensors. IEEE TITS 6, 156–166 (2005). doi:10.1109/TITS.2005.848368

  12. Isshiki, H., Yamamoto, Y.: Instrument for monitoring arousal level using electrodermal activity. In: Proceedings of IEEE International Conference on Instrumentation and Measurement Technology, pp. 975–978. IEEE (1994). doi:10.1109/IMTC.1994.351943

  13. Jorna, P.G.: Spectral analysis of heart rate and psychological state: a review of its validity as a workload index. Biol. Psychol. 34(2–3), 237–257 (1992). doi:10.1016/0301-0511(92)90017-O

    Article  Google Scholar 

  14. Karthikeyan, P., Murugappan, M., Yaacob, S.: Detection of human stress using short-term ECG and HRV signals. J. Mech. Med. Biol. 13(02), 1350038 (2013). doi:10.1142/S0219519413500383

    Article  Google Scholar 

  15. Keogh, E.: Nearest neighbor. In: Sammut, C., Webb, G. (eds.) Encyclopedia of Machine Learning, pp. 714–715. Springer, New York (2010). doi:10.1007/978-0-387-30164-8_204

  16. Li, X., Chen, Z., Liang, Q., Yang, Y.: Analysis of mental stress recognition and rating based on Hidden Markov Model. J. Comput. Inf. Syst. 10(18), 7911–7919 (2014). doi:10.12733/jcis11559

  17. Malik, M.: Heart rate variability. Ann. Noninvasive Electrocardiol. 1(2), 151–181 (1996). doi:10.1111/j.1542-474X.1996.tb00275.x

    Article  Google Scholar 

  18. MATLAB: Version 8.6.0 (R2015b). The MathWorks Inc., Natick, Massachusetts (2015)

    Google Scholar 

  19. Quadrianto, N., Kersting, K., Xu, Z.: Gaussian process. In: Sammut, C., Webb, G. (eds.) Encyclopedia of Machine Learning, pp. 428–439. Springer, New York (2010). doi:10.1007/978-0-387-30164-8_324

  20. Rasmussen, C.E., Nickisch, H.: Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010)

    MathSciNet  MATH  Google Scholar 

  21. Rouse, W., Edwards, S., Hammer, J.M.: Modeling the dynamics of mental workload and human performance in complex systems. IEEE Trans. Syst. Man Cybern. 23, 1662–1671 (1993). doi:10.1109/21.257761

  22. Singh, D., Vinod, K., Saxena, S.: Sampling frequency of the RR interval time series for spectral analysis of heart rate variability. J. Med. Eng. Technol. 28(6), 263–272 (2004). doi:10.1080/03091900410001662350

    Article  Google Scholar 

  23. Stroop, J.R.: Studies of interference in serial verbal reactions. J. Exp. Psychol. 18(6), 643–662 (1935). doi:10.1037/h0054651

    Article  Google Scholar 

  24. Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., Griss, M.: Activity-aware mental stress detection using physiological sensors. In: Gris, M., Yang, G. (eds.) MobiCASE 2010. LNICSSITE, vol. 76, pp. 211–230. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29336-8_12

    Chapter  Google Scholar 

  25. Tarvainen, M., Ranta-aho, P., Karjalainen, P.: An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49(2), 172–175 (2002). doi:10.1109/10.979357

    Article  Google Scholar 

  26. Wallhoff, F., Ablassmeier, M., Bannat, A., Buchta, S., Rauschert, A., Rigoll, G., Wiesbeck, M.: Adaptive human-machine interfaces in cognitive production environments. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 2246–2249 (2007). doi:10.1109/ICME.2007.4285133

  27. Webb, G.: Naïve bayes. In: Sammut, C., Webb, G. (eds.) Encyclopedia of Machine Learning, pp. 713–714. Springer US (2010). doi:10.1007/978-0-387-30164-8_576

  28. Wijsman, J., Grundlehner, B., Liu, H., Hermens, H., Penders, J.: Towards mental stress detection using wearable physiological sensors. In: Proceedings of IEEE Engineering in Medicine and Biology Society, pp. 1798–1801 (2011). doi:10.1109/IEMBS.2011.6090512

  29. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2 edn. Morgan Kaufmann Publishers Inc., San Francisco (2005)

    Google Scholar 

  30. Young, M.S., Stanton, N.A.: Attention and automation: new perspectives on mental underload and performance. Theor. Issues Ergon. Sci. 3(2), 178–194 (2002). doi:10.1080/14639220210123789

Download references

Acknowledgments

This research was supported by the DFG CoE 277: Cognitive Interaction Technology (CITEC), the German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster “Intelligent Technical Systems OstWestfalenLippe” (it’s OWL), managed by the Project Management Agency Karlsruhe (PTKA), the BMBF project ALUBAR, and the PhD program “Design of Flexible Work Environments - Human-Centric Use of Cyber-Physical Systems in Industry 4.0” supported by the North Rhine-Westphalian funding scheme “Fortschrittskolleg”. The authors are responsible for the contents of this publication.

The authors would like to thank Mindfield for providing the API for their eSense Skin Response system.

Author information

Authors and Affiliations

  1. Cognitronics and Sensor Systems Group, CITEC, Bielefeld University, Universitätsstr. 21-23, 33615, Bielefeld, Germany

    Timm Hörmann, Marc Hesse, Peter Christ, Michael Adams, Christian Menßen & Ulrich Rückert

Authors
  1. Timm Hörmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marc Hesse
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Christ
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christian Menßen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ulrich Rückert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timm Hörmann .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. New University of Lisbon, Lisbon, Portugal

    Hugo Gamboa

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hörmann, T., Hesse, M., Christ, P., Adams, M., Menßen, C., Rückert, U. (2017). Detailed Estimation of Cognitive Workload with Reference to a Modern Working Environment. In: Fred, A., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2016. Communications in Computer and Information Science, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-54717-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54717-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54716-9

  • Online ISBN: 978-3-319-54717-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation