Affect detection in normal groups with the help of biological markers

• Published in: 2015 Annual IEEE India Conference (INDICON) pp. 1 - 6
• Main Authors: Basu, Saikat, Bag, Arnab, Mahadevappa, M., Mukherjee, Jayanta, Guha, Rajlakshmi
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2015
• Subjects: Affect; Algorithms; Arousal; Biomarkers; Classification; Classifiers; Data acquisition; Electrocardiography; Electromyography; Electronics; Emotion; Emotion recognition; Emotions; Face; Feature Selection; IAPS; Machine Intelligence; Recognition; Software; Support vector machines; Valence
• ISSN: 2325-9418
• DOI: 10.1109/INDICON.2015.7443733

Emotion Recognition always has been one of the key areas in human machine interaction, machine learning or affective computing. Two dimensional valence arousal model has been used here. In this paper, we present how simple emotion recognition can be done by measuring nine basic non-invasive Biological markers or physiological signals including BR [BReathing], ECG [ElectroCardioGram], EMG [ElectroMyoGram], GSR [Galvanic Skin Response], HR [Heart Rate], PR [Pulse Rate], RR [Respiration Rate], and ST [Skin Temperature] on thirty healthy subjects. Pictorial emotional stimuli categorized as High Valence High Arousal [HVHA], High Valence Low Arousal [HVLA], Low Valence High Arousal [LVHA] and Low Valence Low Arousal [LVLA] were shown using International Affective Picture System (IAPS) for approximately thirty minutes. Six features from each signal were extracted for analysis. Different types of classification algorithms like QDC [Quadratic Discriminant Classifier], kNN [k Nearest Neighbour], Naive Bayes and LDA [Linear Discriminant Analysis] were used in classification of data. Maximum accuracy around 75% for each classifier was obtained. Further improvements are required to make this more robust and to deploy commercially.