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 to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Variation of the time interval between successive heartbeats. Also known as RR-interval.
- 2.
In some applications, e.g. the automotive industry, related parameters like arousal or fatigue are considered.
- 3.
GT-P8110; Google Inc., Samsung Electronics.
- 4.
Mindfield Biosystems Ltd., http://www.mindfield.de.
- 5.
Brain Products GmbH, http://www.brainproducts.com.
- 6.
Polar Electro Oy, http://www.polar.com.
- 7.
Physical Enterprises Inc. (Mio Global), http://www.mioglobal.com.
- 8.
For each of the 7 subsets all combinations of window sizes (11) and overlaps (4) are evaluated.
References
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
Bishop, C.M.: Pattern recognition and machine learning (information science and statistics). In: Kernel Methods, pp. 291–323. Springer, New York (2006)
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
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
Cain, B.: A review of the mental workload literature. In: RTO-TR-HFM-121-Part-II. NATO Science and Technology Organization (2007)
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
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
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)
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
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
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
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
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
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
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
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
Malik, M.: Heart rate variability. Ann. Noninvasive Electrocardiol. 1(2), 151–181 (1996). doi:10.1111/j.1542-474X.1996.tb00275.x
MATLAB: Version 8.6.0 (R2015b). The MathWorks Inc., Natick, Massachusetts (2015)
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
Rasmussen, C.E., Nickisch, H.: Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010)
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
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
Stroop, J.R.: Studies of interference in serial verbal reactions. J. Exp. Psychol. 18(6), 643–662 (1935). doi:10.1037/h0054651
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
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
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
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
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
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2 edn. Morgan Kaufmann Publishers Inc., San Francisco (2005)
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)