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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We have achieved 81.81% and 89.29% accuracy rate for arousal and valence respectively.
📌 3 Key Takeaways
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Objective & Hypothesis:Emotions play a significant and powerful role in everyday life of human beings.
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Key Findings & Data:Developing algorithms for computers to recognize emotional expression is widely studied area.
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Conclusion & Impact:We have achieved 81.81% and 89.29% accuracy rate for arousal and valence respectively.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Biomedical Signal Processing · Emotion Recognition · Pattern Recognition
Abstract
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize emotional expression is widely studied area. In this study, emotion recognition from Galvanic Skin Response signals was performed using time domain, wavelet and empirical mode decomposition based features. Valence and arousal have been categorized and relationship between physiological signals and arousal and valence has been studied using k-Nearest Neighbors, Decision Tree, Random Forest and Support Vector Machine algorithms. We have achieved 81.81% and 89.29% accuracy rate for arousal and valence respectively.
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Cite This Article
Ayata, D., Yaslan, Y., Kamaşak, M. (2017). Emotion Recognition via Galvanic Skin Response: Comparison of Machine Learning Algorithms and Feature Extraction Methods. Electrica, Cilt: 17 Sayı: 1, 3147–3156. https://ajindex.com/articles/1017406