Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Abstract

Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

A Review on Physiological Signal Based Emotion Detection

“…In these investigations different sensors augmented with machine learning were presented, which subsequently yield in emotion detection system. Authors of study [10,11,13,14,17] used benchmark datasets for the emotion classification. The authors of the study [10] used the Augsburg dataset of physiological signals for emotion detection, which has only twenty-five recordings for each emotion (joy, anger, pleasure, and sadness), yielding a dataset of only a hundred instances.…”

“…In study [13,14] Mahnob-Hci data set is used for emotion classification of nine emotions on arousal and valance scale. Physiological signals were collected using cameras that, due to the effects of environmental variables such as light, temperature, and so on, could not be used in real time.…”

“…Physiological signals were collected using cameras that, due to the effects of environmental variables such as light, temperature, and so on, could not be used in real time. Feature fusion is used to classify emotions on arousal and valance scale in [13]. The ratio of data used for training and testing is missing in the manuscript.…”

“…The ratio of data used for training and testing is missing in the manuscript. The trained model in study [13] is implemented on RPi in study [14]. In study [17] both Augsburg and DEAP datasets are used for emotion classification.…”

“…In the study [13] MAHNOB-HCI dataset of physiological signal was used for emotion classification in the arousal valence model. ECG, respiration, skin temperature, and galvanic skin response (GSR) were utilized for emotion classification.…”