首页|Researcher at Kyoto University Targets Machine Learning (Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EM G)
Researcher at Kyoto University Targets Machine Learning (Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EM G)
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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 originating from Kyoto, Japan, by NewsRx correspondents, research stated, “Understanding the association between subjective emotional experiences and physiological signals is of practi cal and theoretical significance.” Financial supporters for this research include Japan Science And Technology Agen cy-mirai Program. Our news reporters obtained a quote from the research from Kyoto University: “Pr evious psychophysiological studies have shown a linear relationship between dyna mic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyz ed the data of two previous studies that measured dynamic valence ratings and fa cial EMG of the corrugator supercilii and zygomatic major muscles from 50 partic ipants who viewed emotional film clips. We employed multilinear regression analy ses and two nonlinear machine learning (ML) models: random forest and long short -term memory. In cross-validation, these ML models outperformed linear regressio n in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subject ive valence dynamics.”
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