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Related Experiment Video

Updated: Jan 1, 2026

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments.

Válber César Cavalcanti Roza1,2, Octavian Adrian Postolache1

  • 1Instituto Universitário de Lisboa (ISCTE-IUL) and Instituto de Telecomunicações (IT-IUL), Av. das Forças Armadas, 1649-026 Lisbon, Portugal.

Sensors (Basel, Switzerland)
|December 19, 2019
PubMed
Summary

This study demonstrates that pilot emotions during flight simulation can be recognized using physiological data like Heart Rate (HR), Galvanic Skin Response (GSR), and Electroencephalography (EEG). Artificial Neural Networks accurately identified emotions, with surprise being the easiest and sadness the most difficult to detect.

Keywords:
deep learningemotion recognitionflight simulationmultimodal sensingphysiological sensing

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Area of Science:

  • Human-Computer Interaction
  • Affective Computing
  • Aerospace Psychology

Background:

  • Understanding pilot emotions is crucial for flight safety and performance.
  • Previous research has gaps in correlating physiological responses with specific flight phases.
  • Simulated flight environments offer a controlled setting for studying pilot emotional states.

Purpose of the Study:

  • To develop a sensing architecture for capturing pilot bio-reactions during simulated flights.
  • To recognize pilot emotions (happy, sad, angry, surprise, scared) using physiological and facial data.
  • To evaluate the effectiveness of Artificial Neural Networks and Deep Learning in emotion recognition.

Main Methods:

  • Utilized Microsoft Flight Simulator (FSX-SE) for 13 simulated flights with 8 beginner pilots.
  • Collected physiological data: Heart Rate (HR), Galvanic Skin Response (GSR), and Electroencephalography (EEG).
  • Employed Artificial Neural Networks and Deep Learning for emotion classification, measuring accuracy with Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

Main Results:

  • Emotion recognition models performed best when using all data or omitting GSR data.
  • The emotion 'surprised' was most accurately recognized (mean RMSE 0.13, MAE 0.01).
  • The emotion 'sad' was least accurately recognized (mean RMSE 0.82, MAE 0.08), with accuracies between 55%-100% for high-intensity emotions.

Conclusions:

  • It is feasible to recognize pilot emotions during flight by integrating present and past emotional states.
  • Physiological signals (HR, GSR, EEG) combined with facial analysis provide valuable insights into pilot affect.
  • The findings contribute to developing more responsive and adaptive flight training and monitoring systems.