Citizen emotion analysis in Smart City

Roza, Valber Cesar Cavalcanti; Postolache, Octavian

doi:10.1109/iisa.2016.7785335

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…These volunteers are not real pilots, and before the experiment, they signed a consent form. Furthermore, this work is a continuation of the work presented by [5][6][7], with the possibility to adapt the present work methodology (changing the experiments and results) to be applied on other contexts, e.g., smart city [8], biophilia, automobilism [9], music [10] or even administrative works (secretaries or customer services, for instance). Regarding the aviation context, this research brings a contribution, showing that it is indeed necessary to improve the training of the pilot, decreasing the cognition effect over their actions and thus attenuating the level of brain activity (also called as brain energy) during critical phases of a real flight.…”

Section: Introductionmentioning

confidence: 98%

β-Band Analysis from Simulated Flight Experiments

Roza

Postolache

2021

Aerospace

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Several safety-related improvements are applied every year to try to minimize the total number of civil aviation accidents. Fortunately, these improvements work well, reducing the number of accident occurrences. However, while the number of accidents due to mechanical failures has decreased, the number of accidents due to human errors seems to grow. On that basis, this work presents a contribution regarding the brain’s β-band activities for different levels of volunteers’ expertise on flight simulator, i.e., experienced, mid-level and beginner, in which they acted as pilots in command during several simulated flights. Spectrogram analysis and statistical measurements of each volunteer’s brain’s β-band were carried out. These were based on seven flight tasks: takeoff, climb, cruise flight, descent, approach, final approach and landing. The results of the proposed experiment showed that the takeoff, approach and landing corresponded to the highest brain activities, i.e., close to 37.06–67.33% more than the brain activity of the other flight tasks: when some accidents were about to occur, the intensities of the brain activity were similar to those of the final approach task. When the volunteers’ expertise and confidence on flight simulation were considered, it was shown that the highest brain magnitudes and oscillations observed of more experienced and confident volunteers were on average close to 68.44% less, compared to less experienced and less confident volunteers. Moreover, more experienced and confident volunteers in general presented different patterns of brain activities compared to volunteers with less expertise or less familiarity with fight simulations and/or electronic games.

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Section: Introductionmentioning

confidence: 98%

β-Band Analysis from Simulated Flight Experiments

Roza

Postolache

2021

Aerospace

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“…These "pilots" in command, were represented by the beginner users of a flight simulator (not real pilots), following a sequence of steps during the flight experiments. The result of this work can also be applied to several workplaces and contexts e.g., administrative sectors [21], in aviation companies/schools [11] and in urban areas [16], among others.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it is also possible to present some research that is more related to emotion analysis e.g., the use of the Friedman test to verify whether the work on exposure and emotional identification influences helps to decrease the levels of anxiety and depression [15]; emotion recognition system based on cross-correlation and the Flowsense database [16]; derived features based on bi-spectral analysis for quantification of emotions using a Valence-Arousal emotion model, to get a way of gaining phase information by detecting phase relationships between frequency components and characterization of the non-Gaussian information from EEG data [17]; a novel real-time subject-dependent algorithm using Stability Intra-class Correlation Coefficient (ICC) with the most stable features that gives a better accuracy than other available algorithms when it is crucial to have only one training session [18]; analysis of emotion recognition techniques used in existing systems to enhance ongoing research on the improvement of tutoring adaptation [19]; and the ensemble deep learning framework by integrating multiple stacked auto-encoder with parsimonious structure to reduce the model complexity and improve the recognition accuracy using physiological feature abstractions [20].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Roza

Postolache

2019

Sensors

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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 lights = 13) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator (N pilots = 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%.

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