Analysis physiological signals for emotion recognition

Gouizi, Khadidja; Reguig, F. Bereksi; Maaoui, Choubeila

doi:10.1109/wosspa.2011.5931436

Cited by 24 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [71], a decrease in variance while the heart rate was high was an indicator of fear. Heart rate, combined with other variables, can successfully classify emotions [72,73], although in others it was found that it had the smallest contributing factor [74].…”

Section: Physiological Datamentioning

confidence: 99%

An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy

Bălan

Moise

Moldoveanu

et al. 2020

Sensors

View full text Add to dashboard Cite

In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment. There are two modalities of expressing fear ratings: the 2-choice scale, where 0 represents relaxation and 1 stands for fear; and the 4-choice scale, with the following correspondence: 0—relaxation, 1—low fear, 2—medium fear and 3—high fear. A set of features was extracted from the sensory signals using various metrics that quantify brain (electroencephalogram—EEG) and physiological linear and non-linear dynamics (Heart Rate—HR and Galvanic Skin Response—GSR). The novelty consists in the automatic adaptation of exposure scenario according to the subject’s affective state. We acquired data from acrophobic subjects who had undergone an in vivo pre-therapy exposure session, followed by a Virtual Reality therapy and an in vivo evaluation procedure. Various machine and deep learning classifiers were implemented and tested, with and without feature selection, in both a user-dependent and user-independent fashion. The results showed a very high cross-validation accuracy on the training set and good test accuracies, ranging from 42.5% to 89.5%. The most important features of fear level classification were GSR, HR and the values of the EEG in the beta frequency range. For determining the next exposure scenario, a dominant role was played by the target fear level, a parameter computed by taking into account the patient’s estimated fear level.

show abstract

Section: Physiological Datamentioning

confidence: 99%

An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy

Bălan

Moise

Moldoveanu

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Euclidean Distance: Let Xneu represent the signal to the neutral emotion [28]. The Euclidean distance between the signal to such an emotion and the signal Xneu is calculated as follows:…”

Section: Eeg Acquisitionmentioning

confidence: 99%

“…Spectral Coherence Function: This feature represents the correspondence of two signals Xi and Xj at each frequency [28]. It is defined as:…”

Section: Eeg Acquisitionmentioning

confidence: 99%

Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

View full text Add to dashboard Cite

Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

show abstract

“…An approach has been proposed for recognizing emotions based on physiological signals [20]. The recognition rate of 41.2% has been attained for the three emotions' categories.…”

Section: Physiologica L Arousalmentioning

confidence: 99%

Usability Engineering of Games: A Comparative Analysis of Measuring Excitement Using Sensors, Direct Observations and Self-Reported Data

Alamoudi¹,

Alomar²,

Alabdulrahman³

et al. 2014

IJU

View full text Add to dashboard Cite

Usability engineering and usability testing are concepts that continue to evolve. Interesting research studies and new ideas come up every now and then. This paper tests the hypothesis of using an EDA-based physiological measurements as a usability testing tool by considering three measures; which are observers' opinions, self-reported data and EDA-based physiological sensor data. These data were analyzed comparatively and statistically. It concludes by discussing the findings that has been obtained from those subjective and objective measures, which partially supports the hypothesis.

show abstract

Analysis physiological signals for emotion recognition

Cited by 24 publications

References 2 publications

An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy

An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy

Emotion Recognition Based on Various Physiological Signals - A Review

Usability Engineering of Games: A Comparative Analysis of Measuring Excitement Using Sensors, Direct Observations and Self-Reported Data

Contact Info

Product

Resources

About