ABSTRACT
In this paper, we investigate how an automatic task assistant which can detect and react to a user's workload level is able to support the user in a complex, dynamic task. In a user study, we design a dispatcher scenario with low and high workload conditions and compare the effect of four support strategies with different levels of intrusiveness using objective and subjective metrics. We see that a more intrusive strategy results in higher efficiency and effectiveness, but is also less accepted by the participants. We also show that the benefit of supportive behavior depends on the user's workload level, i.e. adaptation to its changes are necessary. We describe and evaluate a Brain Computer Interface that is able to provide the necessary user state detection.
- Bailey, N. R., Scerbo, M. W., Freeman, F. G., Mikulka, P. J., and Scott, L. A. Comparison of a brain-based adaptive system and a manual adaptable system for invoking automation. Human Factors: The Journal of the Human Factors and Ergonomics Society 48, 4 (2006), 693--709.Google ScholarCross Ref
- Berka, C., Levendowski, D. J., Ramsey, C. K., Davis, G., Lumicao, M. N., Stanney, K., Reeves, L., Regli, S. H., Tremoulet, P. D., and Stibler, K. Evaluation of an EEG workload model in an aegis simulation environment. In Defense and Security, vol. 5797 (2005), 90--99.Google Scholar
- Chen, D., and Vertegaal, R. Using mental load for managing interruptions in physiologically attentive user interfaces. In Extended Abstracts on Human Factors in Computing Systems (USA, 2004), 1513--1516. Google ScholarDigital Library
- Christensen, J. C., and Estepp, J. R. Coadaptive aiding and automation enhance operator performance. Human Factors: The Journal of the Human Factors and Ergonomics Society (2013), 0018720813476883.Google Scholar
- Dijksterhuis, C., Waard, D. d., and Mulder, B. L. J. M. Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns. Frontiers in Neuroprosthetics 7 (2013).Google Scholar
- Fairclough, S. H. Fundamentals of physiological computing. Interacting with Computers 21, 1-2 (2009), 133--145. Google ScholarDigital Library
- Frøkjær, E., Hertzum, M., and Hornbæk, K. Measuring usability: are effectiveness, efficiency, and satisfaction really correlated? In Proceedings of the Conference on Human Factors in Computing Systems (New York, USA, 2000). Google ScholarDigital Library
- Gajos, K. Z., Czerwinski, M., Tan, D. S., and Weld, D. S. Exploring the design space for adaptive graphical user interfaces. In Proceedings of the Working Conference on Advanced Visual Interfaces (New York, USA, 2006). Google ScholarDigital Library
- Hart, S. G., and Staveland, L. E. Development of NASA-TLX (task load index): Results of empirical and theoretical research. In Advances in Psychology, vol. 52 of Human Mental Workload. North-Holland, 1988, 139--183.Google Scholar
- Heger, D., Putze, F., and Schultz, T. An EEG adaptive information system for an empathic robot. International Journal of Social Robotics 3, 4 (2011), 415--425.Google ScholarCross Ref
- Hornbæk, K., and Law, E. L.-C. Meta-analysis of correlations among usability measures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '07, ACM (New York, NY, USA, 2007), 617--626. Google ScholarDigital Library
- Jarvis, J., Putze, F., Heger, D., and Schultz, T. Multimodal person independent recognition of workload related biosignal patterns. In Proceedings of the 13th International Conference on Multimodal Interfaces, ICMI '11, ACM (New York, NY, USA, 2011), 205--208. Google ScholarDigital Library
- Kohlmorgen, J., Dornhege, G., Braun, M., Blankertz, B., Müller, K.-R., Curio, G., Hagemann, K., Bruns, A., Schrauf, M., and Kincses, W. Improving human performance in a real operating environment through real-time mental workload detection. In Toward Brain-Computer Interfacing. 2007, 409--422.Google Scholar
- Kothe, C., and Makeig, S. Estimation of task workload from EEG data: New and current tools and perspectives. In Proceedings of the Engineering in Medicine and Biology Society (2011), 6547--6551.Google Scholar
- Lei, S., and Rötting, M. Influence of task combination on EEG spectrum modulation for driver workload estimation. Human Factors 53, 2 (2011), 168--179.Google ScholarCross Ref
- Murata, A. An attempt to evaluate mental workload using wavelet transform of EEG. Human Factors 47, 3 (2005), 498--508.Google ScholarCross Ref
- Wang, Z., Hope, R. M., Wang, Z., Ji, Q., and Gray, W. D. Cross-subject workload classification with a hierarchical bayes model. NeuroImage 59, 1 (2012), 64--69.Google ScholarCross Ref
- Wilson, G. F., Lambert, J. D., and Russell, C. A. Performance enhancement with real-time physiologically controlled adaptive aiding. Proc. of the Human Factors and Ergonomics Society Annual Meeting 44, 13 (2000), 61--64.Google ScholarCross Ref
- Wilson, G. F., and Russell, C. A. Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Human Factors 49, 6 (2007), 1005--1018.Google ScholarCross Ref
Index Terms
- Investigating Intrusiveness of Workload Adaptation
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