xHits: An Automatic Team Performance Metric for VR Police Training

Jacob C. Uhl; Huu Quynh Huong Nguyen; Yannick Hill; Markus Murtinger; Manfred Tscheligi

doi:10.1109/MetroXRAINE58569.2023.10405600

xHits: An Automatic Team Performance Metric for VR Police Training

Jacob C. Uhl
, Huu Quynh Huong Nguyen
, Yannick Hill
, Markus Murtinger
, Manfred Tscheligi

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

76 Downloads (Pure)

Abstract

VR training offers scenario-based sessions with analyzable replays, but performance assessment often relies on trainers' subjective judgment. To increase objectivity, we identified key performance indicators (KPIs) through trainer interviews for VR police training and examined their feasibility for automatic VR tracking during a user study. The amount of rooms cleared before the first shot, the teams movement and the stress reaction after the first shot significantly predicted the teams success, operationalized as the number of team members shot in the scenario, which resulted in the “xHits” team performance metric. This paper details the methodology of creating such indicators, the xHits model, and its evaluation, demonstrating the potential of metrology for objective team performance measurements.

Original language	English
Title of host publication	2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
Subtitle of host publication	[Proceedings]
Publisher	IEEE
Pages	178-183
Number of pages	6
ISBN (Electronic)	9798350300802, 9798350300796
ISBN (Print)	9798350300819
DOIs	https://doi.org/10.1109/MetroXRAINE58569.2023.10405600
Publication status	Published - 2023

Funding

This work is supported by the European Commission’s Horizon 2020 Research and Innovation Programme (Grant number: 833572) and by the industrial PhD program of the austrian research promotion agency (FFG) under grant agreement No FO999887876.. This work is supported by the European Commission's Horizon 2020 Research and Innovation Programme (Grant number: 833572) and by the industrial PhD program of the austrian research promotion agency (FFG) under grant agreement No FO999887876.

Funders	Funder number
European Commission’s Horizon 2020 Research and Innovation Programme
Horizon 2020
???publication-publication-funding-organisation-not-added???	833572
Österreichische Forschungsförderungsgesellschaft	FO999887876

Access to Document

10.1109/MetroXRAINE58569.2023.10405600

xHits_An_Automatic_Team_Performance_Metric_for_VR_Police_TrainingFinal published version, 5.47 MBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/377910809_xHits_An_Automatic_Team_Performance_Metric_for_VR_Police_Training

Persistent URL (handle)

Persistent link

Cite this

Uhl, J. C., Nguyen, H. Q. H., Hill, Y., Murtinger, M., & Tscheligi, M. (2023). xHits: An Automatic Team Performance Metric for VR Police Training. In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE): [Proceedings] (pp. 178-183). IEEE. https://doi.org/10.1109/MetroXRAINE58569.2023.10405600

@inproceedings{ac7fcaef039d47e99484eccfb5eab81e,

title = "xHits: An Automatic Team Performance Metric for VR Police Training",

abstract = "VR training offers scenario-based sessions with analyzable replays, but performance assessment often relies on trainers' subjective judgment. To increase objectivity, we identified key performance indicators (KPIs) through trainer interviews for VR police training and examined their feasibility for automatic VR tracking during a user study. The amount of rooms cleared before the first shot, the teams movement and the stress reaction after the first shot significantly predicted the teams success, operationalized as the number of team members shot in the scenario, which resulted in the “xHits” team performance metric. This paper details the methodology of creating such indicators, the xHits model, and its evaluation, demonstrating the potential of metrology for objective team performance measurements.",

author = "Uhl, \{Jacob C.\} and Nguyen, \{Huu Quynh Huong\} and Yannick Hill and Markus Murtinger and Manfred Tscheligi",

year = "2023",

doi = "10.1109/MetroXRAINE58569.2023.10405600",

language = "English",

isbn = "9798350300819",

pages = "178--183",

booktitle = "2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)",

publisher = "IEEE",

}

xHits: An Automatic Team Performance Metric for VR Police Training. / Uhl, Jacob C.; Nguyen, Huu Quynh Huong; Hill, Yannick et al.
2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE): [Proceedings]. IEEE, 2023. p. 178-183.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - xHits: An Automatic Team Performance Metric for VR Police Training

AU - Uhl, Jacob C.

AU - Nguyen, Huu Quynh Huong

AU - Hill, Yannick

AU - Murtinger, Markus

AU - Tscheligi, Manfred

PY - 2023

Y1 - 2023

N2 - VR training offers scenario-based sessions with analyzable replays, but performance assessment often relies on trainers' subjective judgment. To increase objectivity, we identified key performance indicators (KPIs) through trainer interviews for VR police training and examined their feasibility for automatic VR tracking during a user study. The amount of rooms cleared before the first shot, the teams movement and the stress reaction after the first shot significantly predicted the teams success, operationalized as the number of team members shot in the scenario, which resulted in the “xHits” team performance metric. This paper details the methodology of creating such indicators, the xHits model, and its evaluation, demonstrating the potential of metrology for objective team performance measurements.

AB - VR training offers scenario-based sessions with analyzable replays, but performance assessment often relies on trainers' subjective judgment. To increase objectivity, we identified key performance indicators (KPIs) through trainer interviews for VR police training and examined their feasibility for automatic VR tracking during a user study. The amount of rooms cleared before the first shot, the teams movement and the stress reaction after the first shot significantly predicted the teams success, operationalized as the number of team members shot in the scenario, which resulted in the “xHits” team performance metric. This paper details the methodology of creating such indicators, the xHits model, and its evaluation, demonstrating the potential of metrology for objective team performance measurements.

U2 - 10.1109/MetroXRAINE58569.2023.10405600

DO - 10.1109/MetroXRAINE58569.2023.10405600

M3 - Conference contribution

SN - 9798350300819

SP - 178

EP - 183

BT - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

PB - IEEE

ER -

xHits: An Automatic Team Performance Metric for VR Police Training

Abstract

Funding

Access to Document

Persistent URL (handle)

Fingerprint

Cite this