Physiology-driven adaptive virtual reality stimulation for prevention and treatment of stress related disorders.
ABSTRACT The significant proportion of severe psychological problems related to intensive stress in recent large peacekeeping operations underscores the importance of effective methods for strengthening the prevention and treatment of stress-related disorders. Adaptive control of virtual reality (VR) stimulation presented in this work, based on estimation of the person's emotional state from physiological signals, may enhance existing stress inoculation training (SIT). Physiology-driven adaptive VR stimulation can tailor the progress of stressful stimuli delivery to the physiological characteristics of each individual, which is indicated for improvement in stress resistance. Following an overview of physiology-driven adaptive VR stimulation, its major functional subsystems are described in more detail. A specific algorithm of stimuli delivery applicable to SIT is outlined.
- SourceAvailable from: Alan JovicEuroCon 2013; 07/2013
Conference Proceeding: Emotional speech corpus of Croatian language[show abstract] [hide abstract]
ABSTRACT: As a first step in developing an emotion recognition system from human voice, it is necessary to collect relevant set of emotionally rich utterances that will be used for system training. Thus, a first emotional speech corpus of Croatian language (KEG) was built and annotated. The collection and annotation process together with some interesting statistical properties of the designed corpus are described in this paper. Utterances were collected from both male and female speakers, from child age to adults, verbally expressing their emotions. Materials were taken from Internet and other public media sources, with the total duration of approximately 40 minutes. Emotion classification used for annotation has been based on 5 discrete emotional states: happiness, sadness, fear, anger and neutral state. For each of the non-neutral emotional states, the perceived intensity was also annotated in 10 steps. Preliminary KEG evaluation was performed by building and testing an emotion recognition system based on this specific corpus. Initial results are presented in this paper.Image and Signal Processing and Analysis (ISPA), 2011 7th International Symposium on; 10/2011