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Detecting pilots’ expertise using transition matrix measures: a machine learning approach

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ISAE-SUPAERO Conference paper

The 1st International Conference on Cognitive Aircraft

Systems – ICCAS

March 18-19, 2020

https://events.isae-supaero.fr/event/2

Scientific Committee

Mickaël Causse, ISAE-SUPAERO

Caroline Chanel, ISAE-SUPAERO

Jean-Charles Chaudemar, ISAE-SUPAERO

Stéphane Durand, Dassault Aviation

Bruno Patin, Dassault Aviation

Nicolas Devaux, Dassault Aviation

Jean-Louis Gueneau, Dassault Aviation

Claudine Mélan, Université Toulouse Jean-Jaurès

Jean-Paul Imbert, ENAC

Permanent link :

https://doi.org/10.34849/cfsb-t270

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ICCAS 2020 Detecting pilots’ expertise using tr …

Detecting pilots’ expertise using transition matrix

measures: a machine learning approach

Content

Visual perceptual skills are considered to be a crucial ability accounting for the advantage of highly trained experts in many domains (Li et al., 2012). Indeed, expertise exerts a top-down modulation on gaze behavior and strategies. In this sense, experts with extensive training, domain knowledge, and experience can perceive important relationships among multiple information, enabling them to orient their attention toward relevant information and identify abnormalities with a high effi-ciency (Hoffman and Fiore 2007; Palmeri et al. 2004). Multiple studies investigated differences in scan paths and scan patterns between novices and experts from different domains (Law et al, 2004; Ooms et al., 2014). In aviation, the literature also emphasizes different visual scanning strategies in novices vs expert’s pilots (Kasarskis et al., 2001; Yang et al., 2013). In particular, early work investigated the effects of expertise on the visual scanning of flight instrument (Fitts et al., 1949). Later, Tole et al. (1983), and more recently, machine learning approach was applied to this type of topic. For example, Hayashi (2005) proposed a Hidden Markov Model (HMM) approach based on gaze behavior in a space shuttle crew: with Hidden Markov state corresponding to different flight tasks.

We assume that there are differences between novice and expert pilots in the way they explore flight instruments. Moreover, transition matrices give probabilities about possible transitions from an instrument to another. The purpose of this article was to compute dwell patterns and generate transition matrices to perform machine learning in order to classify participants’ expertise (Boc-cignone et al., 2014; Castner et al., 2018).

In this work, gaze movements were recorded in a manual landing task involving sixteen novices and sixteen expert pilots. The novice group had no flight experience and only some aeronautical knowledge. Expert group was professional airliners and had a minimum of 1600 flight hours. Eye movements were acquired through a remote eye-tracking system (SmartEyePro 6.1 with 5 cam-eras) at a sampling frequency of 60 Hz. Cockpit was divided into eleven Areas Of Interest (AOI) corresponding to the flight instruments. Dwells on AOIs inferior to 200 ms were discarded accord-ing to Goldberg et al. (2000). Transition matrices were computed and concatenated into a saccord-ingle feature space for each participant. Machine learning models were trained on the concatenated transition matrices to investigate the possibility to automatically classify the two groups of partic-ipants (novice vs expert). Five-fold cross-validation was used, which is a good tradeoff between bias and variance estimation (Friedman et al., 2001). According to Combrisson and Jerbi (2015) theoretical chance level for classification for p<0.05 with two classes is around 58%.

The results showed that approach based on Cubic Support Vector Machine (C-SVM) reached clas-sification accuracy up to 90%. The outcome of this research could be used to evaluate visuomotor performance during cockpit monitoring activity, enabling instructors to check that trainee’s visual behavior tend to resemble that of an expert. This approach also paves the way for the definition of eye-tracking assistants (Lounis et al., 2018, Lounis et al., 2019) that provide an on-line evaluation of the cockpit monitoring performance.

Mr LOUNIS, CHRISTOPHE (ISAE-SUPAERO), Dr PEYSAKHOVICH, VSEVOLOD (ISAE-SUPAERO)

;

Prof. CAUSSE, MICKAEL (ISAE-SUPAERO)

Keywords : Transparent AI, Natural human-machine interaction, Innovative warning

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