Remarks on emotion recognition from multi-modal bio-potential signals
Koji Takahashi
Abstract
Koji Takahashi
Abstract
This paper proposes an emotion recognition system from multi-modal bio-potential signals. For emotion recognition, support vector machines (SVM) are applied to design the emotion classifier and its characteristics are investigated. Using gathered data under psychological emotion stimulation experiments, the classifier is trained and tested. In experiments of recognizing five emotion: joy, anger, sadness, fear, and relax, recognition rate of 41.7% is achieved. The experimental result shows that using multi-modal bio-potential signals is feasible and that SVM is well suited for emotion recognition tasks.
OpenAlex reports 85 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This paper proposes an emotion recognition system from multi-modal bio-potential signals. For emotion recognition, support vector machines (SVM) are applied to design the emotion classifier and its characteristics are investigated. Using gathered data under psychological emotion stimulation experiments, the classifier is trained and tested. In experiments of recognizing five emotion: joy, anger, sadness, fear, and relax, recognition rate of 41.7% is achieved. The experimental result shows that using multi-modal bio-potential signals is feasible and that SVM is well suited for emotion recognition tasks.
Key concepts: Sadness, Emotion recognition, Anger, Support vector machine, Modal, Emotion classification, Computer science, Classifier (UML)