首页|Investigators from University of Madeira Release New Data on Machine Learning (M ultimodal Emotion Classification Using Machine Learning In Immersive and Non-imm ersive Virtual Reality)
Investigators from University of Madeira Release New Data on Machine Learning (M ultimodal Emotion Classification Using Machine Learning In Immersive and Non-imm ersive Virtual Reality)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Funchal, Portugal, by NewsRx editors, research stated, “Affective computing has been widely used to de tect and recognize emotional states. The main goal of this study was to detect e motional states using machine learning algorithms automatically.” Financial support for this research came from Fundacao para a Ciencia e a Tecnol ogia (FCT). Our news journalists obtained a quote from the research from the University of M adeira, “The experimental procedure involved eliciting emotional states using fi lm clips in an immersive and non-immersive virtual reality setup. The participan ts’ physiological signals were recorded and analyzed to train machine learning m odels to recognize users’ emotional states. Furthermore, two subjective ratings emotional scales were provided to rate each emotional film clip. Results showed no significant differences between presenting the stimuli in the two degrees of immersion. Regarding emotion classification, it emerged that for both physiologi cal signals and subjective ratings, user-dependent models have a better performa nce when compared to user-independent models. We obtained an average accuracy of 69.29 +/- 11.41% and 71.00 +/- 7.95% for the subjec tive ratings and physiological signals, respectively. On the other hand, using u ser-independent models, the accuracy we obtained was 54.0 +/- 17.2% and 24.9 +/- 4.0%, respectively. We interpreted these data as the r esult of high inter-subject variability among participants, suggesting the need for user-dependent classification models. In future works, we intend to develop new classification algorithms and transfer them to real-time implementation.”
FunchalPortugalEuropeCyborgsEmer ging TechnologiesMachine LearningUniversity of Madeira
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