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Roza, V. & Postolache, O. (2019). Multimodal approach for emotion recognition based on simulated flight experiments. Sensors. 19 (24)

V. C. Roza and O. A. Postolache,  "Multimodal approach for emotion recognition based on simulated flight experiments", in Sensors, vol. 19, no. 24, 2019

@article{roza2019_1713588421541,
	author = "Roza, V. and Postolache, O.",
	title = "Multimodal approach for emotion recognition based on simulated flight experiments",
	journal = "Sensors",
	year = "2019",
	volume = "19",
	number = "24",
	doi = "10.3390/s19245516",
	url = "https://www.mdpi.com/1424-8220/19/24/5516"
}

TY  - JOUR
TI  - Multimodal approach for emotion recognition based on simulated flight experiments
T2  - Sensors
VL  - 19
IS  - 24
AU  - Roza, V.
AU  - Postolache, O.
PY  - 2019
SN  - 1424-8220
DO  - 10.3390/s19245516
UR  - https://www.mdpi.com/1424-8220/19/24/5516
AB  - The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.
ER  -