Using physiological signals for emotion recognition

Szwoch, Wioleta

doi:10.1109/hsi.2013.6577880

Cited by 71 publications

(26 citation statements)

References 20 publications

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“…Also, the human speech [10] [11] and motions or gestures are very used in emotion assessing problem. However, these channels cannot usually identify the real emotional states because it is easy to secret a facial expression or fake a tone of voice [12]. Moreover, they are not effective for people who cannot reveal their feeling verbally like autistic people [13].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

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Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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“…visual [5], depth [6], audio [7], textual [8], physiological [9], standard input devices [10]- [15] or multi-modal input [16]. Not all of them seem practical in any situation, e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards detecting programmers’ stress on the basis of keystroke dynamics

Kołakowska¹

2016

Annals of Computer Science and Information Systems

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Abstract-The article describes the idea of detecting stress among programmers on the basis of keystroke dynamics. An experiment with a group of students of artificial intelligence classes was performed. Two samples of keystroke data were recorded for each case, the first while programming without stress, the second under time pressure. A number of timing and frequency parameters were calculated for each sample. Then statistical analysis was performed to evaluate the significance of keystroke parameters changes. It turned out that some of the defined features might be indicators of being stressed.

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“…In the developed path, the combined use of the data was obtained through the criteria inherent in the circumplex model (Jonathan Posner, James A. Russell, 2008), an interpretative model updated over the years (Jonathan Posner, James A. Russell, 2008;Wioleta, 2013) which allows tracing back to emotions using only the cognitive states of valence and arousal (Yik, Russell and Steiger, 2011). With the valence value as first polar coordinate and the arousal value as second polar coordinate, it is possible to obtain a point within the circumplex model that represents the emotion of the observer.…”

Section: Means and Methodsmentioning

confidence: 99%

Survey and Co-Design the Urban Landscape. Innovative Digital Path for Perception Analysis and Data-Driven Project

Bianconi

Filippucci

Seccaroni

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The research presents the participatory processes implemented using Eye-track and EEG to analyse and "survey" the landscape, the immaterial relation that determines the quality of places. If it is always feasible to measure quantities, it is more complex to bring out data and interpretations on the values of places. This path based on the centrality of vision, it focuses on the possibility of detecting through biosensors what attracts the eye most, but also on the effect of vision of this on man. By combining these instruments and their data, it is possible to associate the observer's staring with the sensations he feels, thus explicitly explaining the concept of imageability expressed by Kevin Lynch, it is possible to individuate precisely which elements of the urban space attract the observer and what kind of status arouse in him.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, SpainThis contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W15-165-2019 | © Authors 2019. CC BY 4.0 License.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, Spain This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W15-165-2019 | © Authors 2019. CC BY 4.0 License. Ancona, L., 1970. Dinamica della percezione. Mondadori, Milano.Anderson, D.M., 2002. Build-to-order and mass customization : the ultimate supply chain management and lean manufacturing strategy for low-cost on-demand production without forecasts or inventory.

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Using physiological signals for emotion recognition

Cited by 71 publications

References 20 publications

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Towards detecting programmers’ stress on the basis of keystroke dynamics

Survey and Co-Design the Urban Landscape. Innovative Digital Path for Perception Analysis and Data-Driven Project

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