首页|Researchers at University of Queensland Report Research in Machine Learning (Mac hine learning based classification of presence utilizing psychophysiological sig nals in immersive virtual environments)
Researchers at University of Queensland Report Research in Machine Learning (Mac hine learning based classification of presence utilizing psychophysiological sig nals in immersive virtual environments)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of the Uni versity of Queensland by NewsRx editors, research stated, "In Virtual Reality (V R), a higher level of presence positively influences the experience and engageme nt of a user." Funders for this research include The University of Queensland Phd Scholarship. Our news journalists obtained a quote from the research from University of Queen sland: "There are several parameters that are responsible for generating differe nt levels of presence in VR, including but not limited to, graphical fidelity, m ulti-sensory stimuli, and embodiment. However, standard methods of measuring pre sence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, inclu ding electroencephalography and electrodermal activity. An experiment has been u ndertaken whereby participants (N = 22) were each exposed to three different lev els of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodi ment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector M achine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Re gression, and Multiple Layer Perceptron."
University of QueenslandCyborgsEmerg ing TechnologiesMachine LearningPerceptronVirtual Environments
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