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A microexpression is a facial expression that only lasts for a short moment. It is the innate result of a voluntary and an involuntary emotional response occurring simultaneously and conflicting with one another, and occurs when the amygdala responds appropriately to the stimuli that the individual experiences and the individual wishes to conceal this specific emotion. This results in the individual very briefly displaying their true emotions followed by a false emotional reaction. Human emotions are an unconscious biopsychosocial reaction that derives from the amygdala and they typically last 0.5–4.0 seconds, although a microexpression will typically last less than 1/2 of a second. Unlike regular facial expressions it is either very difficult or virtually impossible to hide microexpression reactions. Microexpressions cannot be controlled as they happen in a fraction of a second, but it is possible to capture someone's expressions with a high speed camera and replay them at much slower speeds. Microexpressions express the seven universal emotions: disgust, anger, fear, sadness, happiness, contempt, and surprise. Nevertheless, in the 1990s, Paul Ekman expanded his list of emotions, including a range of positive and negative emotions not all of which are encoded in facial muscles. These emotions are amusement, embarrassment, anxiety, guilt, pride, relief, contentment, pleasure, and shame. Microexpressions were first discovered by Haggard and Isaacs. In their 1966 study, Haggard and Isaacs outlined how they discovered these "micromomentary" expressions while "scanning motion picture films of psychotherapy for hours, searching for indications of non-verbal communication between therapist and patient" Through a series of studies, Paul Ekman found a high agreement across members of diverse Western and Eastern literate cultures on selecting emotional labels that fit facial expressions. Expressions he found to be universal included those indicating anger, disgust, fear, happiness, sadness, and surprise.
Hannes Bleuler, Solaiman Shokur
[2]Paolo Prandoni, [3]LCAV, EPFL
In [1]:
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as sp
import seaborn as sns
sns.set()
In [2]:
plt.rcParams["figure.figsize"] = (14,4)
The basic reproduction number[4]¶
The propagation mechanism of an epidemic such as the one caused by
SARS-CoV-2 virus can be roughly modeled, at least in its inital phase,
as a process in which each infected individ