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BEGIN:VEVENT
DTSTAMP:20260315T044403Z
DTSTART;VALUE=DATE-TIME:20160225T140000
DTEND;VALUE=DATE-TIME:20160225T150000
SUMMARY:Seminar: Hossein Moghimi (Birmingham)
TZID:Europe/London
UID:20160225-094d434552f8ff1e0152f96e93412b1a@warwick.ac.uk
CREATED:20160219T120839Z
DESCRIPTION:Hossein Moghimi (Birmingham) Adaptive Virtual Environments - 
 A Psychophysiological Feedback HCI System Concept This project aims to d
 esign an adaptive dynamic virtual environment\, capable of responding to
  human emotions. Based on the development of a Valence-Arousal-Dominance
  “Circumplex” (model of emotions)\, a controllable affective virtual med
 ium (a computer game capable of evoking multiple emotions on the users) 
 has been constructed. The project included five phases: 1) Designing a g
 eneric game scenario which can incorporate a large set of variables\, wi
 th potential variable impact on the users’ emotional experience\; 2) Usi
 ng an online survey with 35 participants to assess the potential emotion
 al impact of each variable\; 3) Designing games with combined variables 
 to maximise their emotional effect. The results were validated using add
 itional 68 participants\, who played and emotionally rated their experie
 nces. 4) A physiologically-based experiment has been executed\, in which
  the EEG\, GSR and Heart Rate of 30 male and female gamers have been rec
 orded during exposure to the most powerful affective environments\, iden
 tified in the earlier study. A physiological database\, with correspondi
 ng processed game events and self-reported emotional experiences\, has b
 een constructed to be used in the design and evaluation of an affective 
 computing system. 5) More than 700 physiological features have been extr
 acted from the training database\, while only minority of them have been
  selected\, to be used in the classification process\, using a variable 
 selection algorithm. The selected features have been used to train an af
 fective computing system\, using 3 different classification techniques\;
  K-Nearest Neighbour (KNN)\, Linear Discriminant Analysis (LDA) and Supp
 ort Vector Machines (SVM). The performance of the different classificati
 on techniques (each with various settings) have been compared\, using a 
 10-Fold Cross validation. The best classifier has been able to identify 
 subjects’ emotions\, with 92% accuracy\, using KNN technique while emplo
 ying only 4 features.
LOCATION:C1.06
CATEGORIES:Seminars
LAST-MODIFIED:20160219T121117Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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