Adaptive aiding with an individualized workload model based on psychophysiological measures
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| Publicado en: | Human-Intelligent Systems Integration vol. 2, no. 1-4 (Dec 2020), p. 1 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 2932321568 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2524-4876 | ||
| 022 | |a 2524-4884 | ||
| 024 | 7 | |a 10.1007/s42454-019-00005-8 |2 doi | |
| 035 | |a 2932321568 | ||
| 045 | 2 | |b d20201201 |b d20201231 | |
| 100 | 1 | |a Teo, Grace |u University of Central Florida, Institute for Simulation and Training, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) | |
| 245 | 1 | |a Adaptive aiding with an individualized workload model based on psychophysiological measures | |
| 260 | |b Springer Nature B.V. |c Dec 2020 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Potential benefits of technology such as automation are oftentimes negated by improper use and application. Adaptive systems provide a means to calibrate the use of technological aids to the operator’s state, such as workload state, which can change throughout the course of a task. Such systems require a workload model which detects workload and specifies the level at which aid should be rendered. Workload models that use psychophysiological measures have the advantage of detecting workload continuously and relatively unobtrusively, although the inter-individual variability in psychophysiological responses to workload is a major challenge for many models. This study describes an approach to workload modeling with multiple psychophysiological measures that was generalizable across individuals, and yet accommodated inter-individual variability. Under this approach, several novel algorithms were formulated. Each of these underwent a process of evaluation which included comparisons of the algorithm’s performance to an at-chance level, and assessment of algorithm robustness. Further evaluations involved the sensitivity of the shortlisted algorithms at various threshold values for triggering an adaptive aid. | |
| 653 | |a Psychophysiological measures | ||
| 653 | |a Machine learning | ||
| 653 | |a Behavior | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Flow velocity | ||
| 653 | |a Human performance | ||
| 653 | |a Algorithms | ||
| 653 | |a Electrocardiography | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Workload | ||
| 653 | |a Automation | ||
| 653 | |a Electroencephalography | ||
| 653 | |a Workloads | ||
| 653 | |a Physiological psychology | ||
| 653 | |a Oxygen saturation | ||
| 653 | |a Variability | ||
| 653 | |a Heart rate | ||
| 653 | |a Psychophysiology | ||
| 700 | 1 | |a Matthews, Gerald |u University of Central Florida, Institute for Simulation and Training, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) | |
| 700 | 1 | |a Reinerman-Jones, Lauren |u University of Central Florida, Institute for Simulation and Training, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) | |
| 700 | 1 | |a Barber, Daniel |u University of Central Florida, Institute for Simulation and Training, Orlando, USA (GRID:grid.170430.1) (ISNI:0000 0001 2159 2859) | |
| 773 | 0 | |t Human-Intelligent Systems Integration |g vol. 2, no. 1-4 (Dec 2020), p. 1 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2932321568/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2932321568/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2932321568/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |