Knowledge, Skills, Abilities, and Other Characteristics for Remotely Piloted Aircraft Pilots and Operators

Howse, William R.

doi:10.21236/ada552499

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…Earlier work by Dror et al (1993) on a battery of visual cognitive tests deemed relevant to Air Force pilot skill showed that experienced pilots were faster but not more accurate than control populations. Howse (2011) reviewed over 200 publications relating to knowledge, skills, abilities, and other characteristics (KSAOs) related to remotely piloted aircraft (RPA) crew positions, including mental rotation, spatial orientation, and visualization as key skills, finding only three studies that could be considered rigorous and systematic in their approach (Barnes et al, 2000;Bruskiewicz et al, 2007;Warner & Knapp, 2000), of which Bruskiewicz et al (2007) was deemed suitable for efficient validation. Carretta et al (2016) evaluated Air Force pilots using the Multidimensional Aptitude Battery-2nd Edition (MAB-II) and NEO Personality Inventory-Revised (NEO PI-R) for training, finding that pilot trainee cognitive scores were elevated above the general adult population, supporting the need for specialized norms for pilot trainees.…”

Section: Reviewmentioning

confidence: 99%

Mental imagery testing for personnel selection, training, and HCI applications

Colombe

Gallo

Panic

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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This theoretical paper advocates for research to assess whether tests of mental imagery may be used as reliable and valid predictors to inform the design of human-computer interactions suited to individual differences, and industrial-organizational psychology applications including personnel selection and customized training regimens. Mental imagery includes multisensory, non-sensory spatial, and motor rehearsal forms, and has been correlated with emotional reactivity and performance on laboratory tasks such as memory recognition and recall. We focus on air traffic control and aircraft pilot roles, as cognitive testing has been pioneered in these high-consequence professions, with resulting cultural and institutional support for the use of testing to guide personnel development decisions. Mental imagery testing may also offer benefits in the lifelong career pipeline for STEM, creative, and problem-solving work roles more broadly.

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Section: Reviewmentioning

confidence: 99%

Mental imagery testing for personnel selection, training, and HCI applications

Colombe

Gallo

Panic

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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“…maneuvering between obstacles, turning in various directions). These actions were designed considering basic cognitive skills and abilities required for drone flight and navigation, as well as take-off and landing (Howse, 2011; Sakib et al , 2021).…”

Section: Study Design and Data Descriptionmentioning

confidence: 99%

A feedforward neural network for drone accident prediction from physiological signals

Sakib

Chaspari

Behzadan

2021

SASBE

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PurposeAs drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.Design/methodology/approachThe authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.FindingsAnalysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.Originality/valueThe safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

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“…Personality traits related to risk taking, stress tolerance, comfort working in a confined space with others, and positive social exchanges are alluded to by Pavlas et al [16] as important to performance among RPA pilots in general. The reviews of selection recommendations for USAF and Navy RPA pilots also discussed the importance of hardiness (i.e., resilience to stress and adaptability) and positive social interpersonal style (i.e., group warmth) [17][18][19][20][21][22]. A more lengthy study that included the input of over 80 USAF RPA subject matter experts (training cadre, pilots, and commanders) also identified several personality traits (e.g., stress tolerance, assertiveness, selfconfidence, impulse control, etc.)…”

Section: Introductionmentioning

confidence: 99%