Design of a Multimodal Interface based on Psychophysiological Sensing to Identify Emotion |
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| Válber César Cavalcanti Roza, Octavian Adrian Postolache |
- Abstract:
- This work proposes a design of a multimodal interface to classify or estimate emotion states. Thus, 7emotions are considered such as:anger, boredom, disgust, anxiety/fear, happiness, sadness and normal.A couple of sensing technologies such as: galvanic skin response (GSR), heart rate (HR), electrocardiography (ECG), oxygen saturation (SpO2) and electroencephalography (EEG)are used to collect psychophysiological signals in relation with emotion state estimation. The International Affective Picture System (IAPS) dataset is used to design the classifier system. Regarding the classification task, a comparison between artificial neural networks (ANN-MLP) and support vector machine (SVM) is presented. The tests were carried out for 20 healthy volunteers ( ) of both genders with age from 23-50 years old. The proposed classifier presents accuracies of 85.71% when using ANN-MLP and 77.14% when using SVM.
- Keywords:
- Multimodal interface, signal analysis, emotion classification, psychophysiological signals
- Download:
- IMEKO-TC4-2017-078.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC4
- Event name:
- TC4 Symposium 2017
- Title:
22nd IMEKO TC4 Symposium and 20th International Workshop on ADC Modelling and Testing
"Supporting world development through electric&electronic measurements"- Place:
- Iasi, ROMANIA
- Time:
- 14 September 2017 - 15 September 2017