How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological Signals Article Swipe
Jong Hoon Park
,
Lawrence R. Chen
,
Ian Higgins
,
Zhaobo Zheng
,
Shashank Mehrotra
,
Kevin Salubre
,
Mohammadreza Mousaei
,
Steven Willits
,
Blain Levedahl
,
Timothy J. Buker
,
Eliot Xing
,
Teruhisa Misu
,
Sebastian Scherer
,
Jean Oh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.06448
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.06448
Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popular in development, research, and operation. When compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique challenges as they combine many maneuvers from both types of aircraft. Pilot workload is a critical factor for safe and efficient operation of VTOLs. In this work, we conduct a user study to collect multimodal data from 28 pilots while they perform a variety of VTOL flight tasks. We analyze and interpolate behavioral patterns related to their performance and perceived workload. Finally, we build machine learning models to estimate their workload from the collected data. Our results are promising, suggesting that quantitative and accurate VTOL pilot workload monitoring is viable. Such assistive tools would help the research field understand VTOL operations and serve as a stepping stone for the industry to ensure VTOL safe operations and further remote operations.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.06448
- https://arxiv.org/pdf/2406.06448
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399554396
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399554396Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2406.06448Digital Object Identifier
- Title
-
How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological SignalsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-10Full publication date if available
- Authors
-
Jong Hoon Park, Lawrence R. Chen, Ian Higgins, Zhaobo Zheng, Shashank Mehrotra, Kevin Salubre, Mohammadreza Mousaei, Steven Willits, Blain Levedahl, Timothy J. Buker, Eliot Xing, Teruhisa Misu, Sebastian Scherer, Jean OhList of authors in order
- Landing page
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https://arxiv.org/abs/2406.06448Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2406.06448Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.06448Direct OA link when available
- Concepts
-
Workload, Estimation, Computer science, Artificial intelligence, Machine learning, Human–computer interaction, Engineering, Systems engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.research, | 30 |
| abstract_inverted_index.workload. | 104 |
| abstract_inverted_index.behavioral | 96 |
| abstract_inverted_index.challenges | 44 |
| abstract_inverted_index.fixed-wing | 37 |
| abstract_inverted_index.monitoring | 131 |
| abstract_inverted_index.multimodal | 78 |
| abstract_inverted_index.operation. | 32 |
| abstract_inverted_index.operations | 144, 158 |
| abstract_inverted_index.promising, | 122 |
| abstract_inverted_index.suggesting | 123 |
| abstract_inverted_index.understand | 142 |
| abstract_inverted_index.flexibility | 23 |
| abstract_inverted_index.interpolate | 95 |
| abstract_inverted_index.operations. | 162 |
| abstract_inverted_index.performance | 101 |
| abstract_inverted_index.rotorcraft, | 40 |
| abstract_inverted_index.traditional | 36 |
| abstract_inverted_index.development, | 29 |
| abstract_inverted_index.quantitative | 125 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile |