Physiological Data Models to Understand the Effectiveness of Drone Operation Training in Immersive Virtual Reality

Sakib, Nazmus; Chaspari, Theodora; Behzadan, Amir H.

doi:10.1061/(asce)cp.1943-5487.0000941

Cited by 32 publications

(40 citation statements)

References 52 publications

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“…Unfortunately, these kinds of tests are difficult in a real-world training environment because of the risks to both the pilot and the drone (Sakib et al, 2021). Additionally, there are challenges associated with FAA regulations regarding flying UAVs in public, further limiting where and how physical drone training can occur (Sakib et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the majority of training and evaluation programs focus on total time to complete a flight objective (Jacoff, 2019;Sakib et al, 2021). However, total time alone does not provide a very accurate picture of the pilot's control of the aircraft, and there are many other characteristics of flight that can help to provide a more holistic view, especially as a user learns new skills.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Early Predictors of Learning in VR-based Drone Training

Hayes

Dwivedi

Karthikeyan

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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The use of small unmanned aerial systems, or drones, has grown rapidly in recent years, but the FAA has no formal requirement for hands-on training. Physical drone training has major limitations, and virtual reality (VR) offers a promising alternative. This study sought to identify early performance markers of learning in VR-based drone training using a physical controller. 14 participants completed a customized VR-based drone training curriculum, while performance metrics, perceptions of workload, and physiological data were collected. Participants were clustered into high and low performers based on a final evaluation task, and separate analyses of variance were conducted to test performance differences between the two groups over time in each training level. We found significant differences in the performance metrics and subjective workload of low and high performers throughout training, suggesting that performance can be predicted early in training and opening the door to future adaptive training systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Early Predictors of Learning in VR-based Drone Training

Hayes

Dwivedi

Karthikeyan

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

show abstract

“…Researchers have been exploring general drone flight training (O’Keeffe et al , 2017; Qi et al , 2018; Smolyanskiy and Gonzalez-Franco, 2017; Ware, 2017; Wlodyka and Dulat, 2015); however, challenges such as high costs, liability concerns and safety risks hinder the adaptation of such training in CEM education programs (Bu et al , 2015; De la Torre et al , 2016; Weldon and Kozak, 2017). These factors paved the way to introduce virtual reality (VR)-based simulators as safe, advantageous and efficient training alternatives (Balakirsky and Kootbally, 2012; Bu et al , 2015; De la Torre et al , 2016; Meyer et al , 2012; Sakib et al , 2020, 2021; Wang et al , 2017; Weldon and Kozak, 2017). Although drone building inspection flights and general flights seem to require similar piloting skills, there are distinctive flight operation characteristics pertaining to the construction domain.…”

Section: Introductionmentioning

confidence: 99%

“…Based on physiological data collected by wearable devices, results showed that VR-mediated drone training is an efficient real-world training alternative. Using the same VR-based and real-world drone training environments, a more recent analysis demonstrated the reliability of physiological metrics and self-assessments in accurately evaluating pilots’ performance, mental workload and stress while operating drones (Sakib et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

DroneSim: a VR-based flight training simulator for drone-mediated building inspections

Albeaino

Eiris

Gheisari

et al. 2021

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Purpose This study aims to explore DroneSim, a virtual reality (VR)-based flight training simulator, as an alternative for real-world drone-mediated building inspection training. Design/methodology/approach Construction, engineering and management students were asked to pilot drones in the VR-based DroneSim space and perform common flight operations and inspection tasks within the spatiotemporal context of a building construction project. Another student group was also recruited and asked to perform a similar building inspection task in real world. The National Aeronautics and Space Administration (NASA)–Task Load Index (TLX) survey was used to assess students’ inflight workload demand under both Real and DroneSim conditions. Post-assessment questionnaires were also used to analyze students’ feedback regarding the usability and presence of DroneSim for drone building inspection training. Findings None of the NASA–TLX task load levels under real and DroneSim conditions were highly rated by students, and both groups experienced comparable drone-building inspection training. Students perceived DroneSim positively and found the VR experience stimulating. Originality/value This study’s contribution is twofold: to better understand the development stages involved in the design of a VR-based drone flight training simulator, specifically for building inspection tasks; and to improve construction students’ drone operational and flight training skills by offering them the opportunity to enhance their drone navigation skills in a risk-free, repeatable yet realistic environment. Such contributions ultimately pave the way for better integration of drone-mediated building inspection training in construction education while meeting industry needs.

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“…The developed AR inspection solution can facilitate efficient, comprehensive, and unbiased UAV-based building inspections. In another study, Sakib et al (2021) used immersive VR and physiological data models to understand the effectiveness of drone operation training. More specifically, they focused on the reliability of using the drone operator's physiological indexes and self-assessments to predict performance, mental workload (MWL), and stress in immersive virtual reality training and outdoor deployment.…”

mentioning

confidence: 99%