Robotics & Machine Learning Daily News2024,Issue(Oct.4) :97-97.

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)

Robotics & Machine Learning Daily News2024,Issue(Oct.4) :97-97.

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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Abstract

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."

Key words

University of Queensland/Cyborgs/Emerg ing Technologies/Machine Learning/Perceptron/Virtual Environments

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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