Every year, in Europe alone, hundreds of workers die by falling from high height. This number could be greatly reduced by means of better training and quick detection of individuals with issues toward work at height. Workers proving to be less suited for the job can be subject to more intensive training or recruited for different positions. Unfortunately, the early detection of workers unsuited for working at height involves specialized personnel and expensive equipment to recreate a stressful environment. In this paper we propose a methodology to predict fear of heights by means of a virtual reality environment. We demonstrate that a 3D virtual environment is feasible for the prediction and give guidelines about meaningful physiological parameters useful for detection.

Predicting Real Fear of Heights Using Virtual Reality / G. Boccignone, D. Gadia, D. Maggiorini, L.A. Ripamonti, V. Tosto - In: GoodIT '21: Proceedings[s.l] : ACM, 2021. - ISBN 9781450384780. - pp. 103-108 (( convegno Conference on Information Technology for Social Good (GoodIT '21) tenutosi a Roma nel 2021 [10.1145/3462203.3475882].

Predicting Real Fear of Heights Using Virtual Reality

G. Boccignone;D. Gadia;D. Maggiorini
;
L.A. Ripamonti;
2021

Abstract

Every year, in Europe alone, hundreds of workers die by falling from high height. This number could be greatly reduced by means of better training and quick detection of individuals with issues toward work at height. Workers proving to be less suited for the job can be subject to more intensive training or recruited for different positions. Unfortunately, the early detection of workers unsuited for working at height involves specialized personnel and expensive equipment to recreate a stressful environment. In this paper we propose a methodology to predict fear of heights by means of a virtual reality environment. We demonstrate that a 3D virtual environment is feasible for the prediction and give guidelines about meaningful physiological parameters useful for detection.
virtual reality; skills assessment; affective computing; unsupervised machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/868544
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