영문내용
(English Abstract) |
Affective computing is becoming a pioneer research domain to understand human’s emotion through scientific methods. From genome sequence to face recognition and from neuroimaging to social-post mining each of this domain tries to use their scientific methodology to recognize, realize and predict human’s affective state. The pen and paper-based affective state determination methods are not so accurate and impressive therefore due to the advancement of intelligent technology researchers are trying to apply some intelligent learning methods to realize individuals affective state. This research uses biosensors data to realize humans’ affective state. Humans’ psychophysiological data is collected through Electroencephalogram (ECG), Electro-Dermal Activity (EDA), Electromyography (EMG) and Photoplethysmogram (PPG) and analyzed those data using the deep recurrent neural network to determine affective mood. Here, based on Russell’s circumplex four primary affective mood i.e. Joy, Sad, Surprise, and Disgust is considered for realization. The benchmark DEAP dataset is used to analyze the performance of the proposed method. The higher accuracy in classification of the primary affective mood justifies the performance of the proposed method.
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