Application of fractional Fourier transform in feature extraction from ELECTROCARDIOGRAM and GALVANIC SKIN RESPONSE for emotion recognition

2021 ◽  
Vol 69 ◽  
pp. 102863
Author(s):  
Farnaz Panahi ◽  
Saeid Rashidi ◽  
Ali Sheikhani
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Emotion Recognition ◽  
Galvanic Skin Response ◽  
Fractional Fourier Transform ◽  
Skin Response

A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset

2020 ◽  
pp. 42-66
Author(s):  
Dinesh Bhatia ◽  
Animesh Mishra
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Mapping Technique ◽  
Self Organizing Maps ◽  
Map Generation ◽  
Unsupervised Training ◽  
Cardio Vascular ◽  
Short Time

The role of ECG analysis in the diagnosis of cardio-vascular ailments has been significant in recent times. Although effective, the present computational algorithms lack accuracy, and no technique till date is capable of predicting the onset of a CVD condition with precision. In this chapter, the authors attempt to formulate a novel mapping technique based on feature extraction using fractional Fourier transform (FrFT) and map generation using self-organizing maps (SOM). FrFT feature extraction from the ECG data has been performed in a manner reminiscent of short time Fourier transform (STFT). Results show capability to generate maps from the isolated ECG wavetrains with better prediction capability to ascertain the onset of CVDs, which is not possible using conventional algorithms. Promising results provide the ability to visualize the data in a time evolution manner with the help of maps and histograms to predict onset of different CVD conditions and the ability to generate the required output with unsupervised training helping in greater generalization than previous reported techniques.

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).

Gear fault feature extraction based on fractional Fourier transform

2016 ◽  
pp. 931-936
Author(s):  
Hongfang Chen ◽  
Yanqiang Sun ◽  
Zhaoyao Shi ◽  
Jiachun Lin ◽  
Zaihua Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Gear Fault ◽  
Fault Feature Extraction
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