The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features

2019 ◽  
Vol 43 (1) ◽  
pp. 119-134 ◽  
Author(s):  
Atefeh Goshvarpour ◽  
Ateke Goshvarpour
Keyword(s):  
Emotion Recognition ◽  
Galvanic Skin Response ◽  
Skin Response ◽  
Nonlinear Features

scholarly journals Platform Biometrics

2019 ◽  
Vol 17 (1/2) ◽  
pp. 54-62 ◽  
Author(s):  
Jeremy W Crampton
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Galvanic Skin Response ◽  
Human Experience ◽  
Basic Emotions ◽  
Biometric Systems ◽  
Skin Response ◽  
The Face ◽  
The Subject ◽  
Iris Patterns

This article identifies and analyses the emergence of platform biometrics. Biometrics are measurements of behavioral and physical characteristics, such as facial expressions, gait, galvanic skin response, and palm or iris patterns. Platform biometrics not only promise to connect geographically distant actors but also to curate new forms of value. In this piece I describe Microsoft Face, one of the major facial biometric systems currently on the market; this software promises to analyze which of seven “universal” emotions a subject is experiencing. I then offer a critique of the assumptions behind the system. First, theories of emotion are divided on whether emotions can be reliably and measurably expressed by the face. Second, emotions may not be universal, nor are there likely only seven basic emotions. Third, I draw on the work of Rouvroy and Berns (2013) to identify emotion-recognition technologies as a classic example of algorithmic governance. To outcome algorithmic governance is to enable the subject to creation and govern surveillance.  Platform biometrics will therefore provide a key component of surveillance capitalism’s appropriation of human experience (neuro-liberalism).

Emotion recognition from physiological signals and video games to detect personality traits

2020 ◽  
pp. 1-9
Author(s):  
M. Callejas-Cuervo ◽  
L.A. Martínez-Tejada ◽  
A.C. Alarcón-Aldana
Keyword(s):  
Emotion Recognition ◽  
Galvanic Skin Response ◽  
Skin Conductance Response ◽  
Statistical Data ◽  
Inverse Correlation ◽  
Physiological Signals ◽  
Physiological Signal ◽  
First Approximation ◽  
Skin Response ◽  
Electrocardiogram Ecg

This paper presents a system that allows for the identification of two values: arousal and valence, which represent the degree of stimulation in a subject, using Russell’s model of affect as a reference. To identify emotions, a step-by-step structure is used, which, based on statistical data from physiological signal metrics, generates the representative arousal value (direct correlation); from the PANAS questionnaire, the system generates the valence value (inverse correlation), as a first approximation to the techniques of emotion recognition without the use of artificial intelligence. The system gathers information concerning arousal activity from a subject using the following metrics: beats per minute (BPM), heart rate variability (HRV), the number of galvanic skin response (GSR) peaks in the skin conductance response (SCR) and forearm contraction time, using three physiological signals (Electrocardiogram - ECG, Galvanic Skin Response - GSR, Electromyography - EMG).

User Stress Modeling through Galvanic Skin Response

2021 ◽  
Author(s):  
Fahad Ahmed Satti ◽  
Musarrat Hussain ◽  
Jamil Hussain ◽  
Tae-Seong Kim ◽  
Sungyoung Lee ◽  
...  
Keyword(s):  
Galvanic Skin Response ◽  
Stress Modeling ◽  
Skin Response
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