The Usability of Cardiovascular and Electrodermal Measures for Adaptive Automation
In case of adaptive automation, a system automatically increases the operator’s workload if there are signs of hypovigilance, reflected in psychophysiological arousal measures such as spontaneous electrodermal activity, and takes over more responsibility in case of workload becoming too high. Adaptive automation is currently discussed for long-term operations such as intercontinental flights according to instrumental flight rules. We constructed a closed-loop adaptive system for varying the strength of turbulence in a professional simulator. In the experimental condition, nine subjects flew thirty 60-s flight sections, keeping altitude and course while facing different turbulences. The number of nonspecific skin conductance responses was calculated every 60 s and was used for triggering the turbulence strength for the next 60s, dependent on the set point of the individual subject. The other nine subjects belonged to the yoked control condition, flying the same sequence of turbulences as the corresponding experimental subject, however without adaptive automation. Our results indicate that the skin conductance responses of experimental subjects oscillated very close to the individual set point, indicating that the subjects maintained an optimal vigilance/workload level as a result of adaptive control in contrast to yoked control subjects.
KeywordsSkin Conductance Response Baseline Recording Vigilance Decrement Flight Mission Turbulence Strength
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- Boucsein, W. and Backs, R.W. Engineering psychophysiology as a discipline: Historical and theoretical aspects. In: Backs, R.W. and Boucsein, W. (Eds.) Engineering Psychophysiology. Issues and applications. Lawrence Erlbaum Associates, Mahwah, NJ, (2000), pp 3–29.Google Scholar
- Endsley, M.R. Automation and situation awareness. In: Parasuraman R and Mouloua M (Eds.) Automation and human performance: Theory and applications. Lawrence Erlbaum Associates, Hillsdale, NJ, England, (1996), pp 163–181.Google Scholar
- Luczak, H. and Göbel, M. Signal processing and analysis in application. In: Backs RW and Boucsein W (Eds.) Engineering psychophysiology. Issues and applications. Lawrence Erlbaum Associates, Mahwah, NJ, (2000), pp 79–110.Google Scholar
- Morrison, J.G. and Gluckman, J.P. Definitions and prospective guidelines for the application of adaptive automation. In: Mouloua, M. and Parasuraman, R. (Eds.) Human performance in automated systems: Current research and trends. Lawrence Erlbaum Associates, Hillsdale, NJ, (1994), pp 256–263.Google Scholar
- Prinzel, III L.J., Parasuraman, R., Freeman, F.G., Scerbo, M.W., Mikulka, P.J. and Pope, A.T. Three experiments examining the use of electroencephalogram, event-related potentials, and heart-rate variability for real-time human-centered adaptive automation design. NASA Langley Research Center, Hampton, Virginia, (2003), pp 1–62 (available via http://techreports.larc.nasa.gov/ltrs/PDF/2003/tp/NASA-2003-tp212442.pdf).
- Prinzel, III L.J., Pope, A.T. and Freeman, F.G. Application of physiological self-regulation and adaptive task allocation techniques for controlling operator hazardous states of awareness. NASA Langley Research Center, Hampton, Virginia, (2001), pp 1–17 (available via http://techreports.larc.nasa.gov/ltrs/PDF/2001/tm/NASA-2001-tm211015.pdf).